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Ma KC, Mena E, Lindenberg L, Lay NS, Eclarinal P, Citrin DE, Pinto PA, Wood BJ, Dahut WL, Gulley JL, Madan RA, Choyke PL, Turkbey IB, Harmon SA. Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN. Oncotarget 2024; 15:288-300. [PMID: 38712741 DOI: 10.18632/oncotarget.28583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024] Open
Abstract
PURPOSE Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. METHODS A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. RESULTS Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). CONCLUSION The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.
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Affiliation(s)
- Kevin C Ma
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Esther Mena
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Liza Lindenberg
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Nathan S Lay
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Phillip Eclarinal
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - James L Gulley
- Center for Immuno-Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter L Choyke
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ismail Baris Turkbey
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephanie A Harmon
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Lin Y, Yilmaz EC, Belue MJ, Harmon SA, Tetreault J, Phelps TE, Merriman KM, Hazen L, Garcia C, Yang D, Xu Z, Lay NS, Toubaji A, Merino MJ, Xu D, Law YM, Gurram S, Wood BJ, Choyke PL, Pinto PA, Turkbey B. Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI. Radiology 2024; 311:e230750. [PMID: 38713024 DOI: 10.1148/radiol.230750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Yue Lin
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Enis C Yilmaz
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Mason J Belue
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Stephanie A Harmon
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Jesse Tetreault
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Tim E Phelps
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Katie M Merriman
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Lindsey Hazen
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Charisse Garcia
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Dong Yang
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Ziyue Xu
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Nathan S Lay
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Antoun Toubaji
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Maria J Merino
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Daguang Xu
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Yan Mee Law
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Sandeep Gurram
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Bradford J Wood
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Peter L Choyke
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Peter A Pinto
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Baris Turkbey
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H., T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
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Simon BD, Merriman KM, Harmon SA, Tetreault J, Yilmaz EC, Blake Z, Merino MJ, An JY, Marko J, Law YM, Gurram S, Wood BJ, Choyke PL, Pinto PA, Turkbey B. Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification. Acad Radiol 2024:S1076-6332(24)00220-4. [PMID: 38670874 DOI: 10.1016/j.acra.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/03/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024]
Abstract
RATIONALE AND OBJECTIVES Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI. MATERIAL AND METHODS An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology. RESULTS A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth. CONCLUSION Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.
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Affiliation(s)
- Benjamin D Simon
- Molecular Imaging Branch, NCI, NIH, Bethesda, Maryland, USA (B.D.S., K.M.M., S.A.H., E.C.Y., P.L.C., B.T.); Institute of Biomedical Engineering, Department Engineering Science, University of Oxford, UK (B.D.S.)
| | - Katie M Merriman
- Molecular Imaging Branch, NCI, NIH, Bethesda, Maryland, USA (B.D.S., K.M.M., S.A.H., E.C.Y., P.L.C., B.T.)
| | - Stephanie A Harmon
- Molecular Imaging Branch, NCI, NIH, Bethesda, Maryland, USA (B.D.S., K.M.M., S.A.H., E.C.Y., P.L.C., B.T.)
| | | | - Enis C Yilmaz
- Molecular Imaging Branch, NCI, NIH, Bethesda, Maryland, USA (B.D.S., K.M.M., S.A.H., E.C.Y., P.L.C., B.T.)
| | - Zoë Blake
- Urology Oncology Branch, NCI, NIH, Bethesda, Maryland, USA (Z.B., S.G., P.A.P.)
| | - Maria J Merino
- Laboratory of Pathology, NCI, NIH, Bethesda, Maryland, USA (M.J.M.)
| | - Julie Y An
- Department of Radiology, University of California, San Diego, California, USA (J.Y.A.)
| | - Jamie Marko
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA (J.M.)
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Sandeep Gurram
- Urology Oncology Branch, NCI, NIH, Bethesda, Maryland, USA (Z.B., S.G., P.A.P.)
| | - Bradford J Wood
- Center for Interventional Oncology, NCI, NIH, Bethesda, Maryland, USA (B.J.W.); Department of Radiology, Clinical Center, NIH, Bethesda, Maryland, USA (B.J.W.)
| | - Peter L Choyke
- Molecular Imaging Branch, NCI, NIH, Bethesda, Maryland, USA (B.D.S., K.M.M., S.A.H., E.C.Y., P.L.C., B.T.)
| | - Peter A Pinto
- Urology Oncology Branch, NCI, NIH, Bethesda, Maryland, USA (Z.B., S.G., P.A.P.)
| | - Baris Turkbey
- Molecular Imaging Branch, NCI, NIH, Bethesda, Maryland, USA (B.D.S., K.M.M., S.A.H., E.C.Y., P.L.C., B.T.).
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Gelikman DG, Mena E, Lindenberg L, Azar WS, Rathi N, Yilmaz EC, Harmon SA, Schuppe KC, Hsueh JY, Huth H, Wood BJ, Gurram S, Choyke PL, Pinto PA, Turkbey B. Reducing False-Positives Due to Urinary Stagnation in the Prostatic Urethra on 18F-DCFPyL PSMA PET/CT With MRI. Clin Nucl Med 2024:00003072-990000000-01083. [PMID: 38651785 DOI: 10.1097/rlu.0000000000005220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
PURPOSE Prostate-specific membrane antigen (PSMA)-targeting PET radiotracers reveal physiologic uptake in the urinary system, potentially misrepresenting activity in the prostatic urethra as an intraprostatic lesion. This study examined the correlation between midline 18F-DCFPyL activity in the prostate and hyperintensity on T2-weighted (T2W) MRI as an indication of retained urine in the prostatic urethra. PATIENTS AND METHODS Eighty-five patients who underwent both 18F-DCFPyL PSMA PET/CT and prostate MRI between July 2017 and September 2023 were retrospectively analyzed for midline radiotracer activity and retained urine on postvoid T2W MRIs. Fisher's exact tests and unpaired t tests were used to compare residual urine presence and prostatic urethra measurements between patients with and without midline radiotracer activity. The influence of anatomical factors including prostate volume and urethral curvature on urinary stagnation was also explored. RESULTS Midline activity on PSMA PET imaging was seen in 14 patients included in the case group, whereas the remaining 71 with no midline activity constituted the control group. A total of 71.4% (10/14) and 29.6% (21/71) of patients in the case and control groups had urethral hyperintensity on T2W MRI, respectively (P < 0.01). Patients in the case group had significantly larger mean urethral dimensions, larger prostate volumes, and higher incidence of severe urethral curvature compared with the controls. CONCLUSIONS Stagnated urine within the prostatic urethra is a potential confounding factor on PSMA PET scans. Integrating PET imaging with T2W MRI can mitigate false-positive calls, especially as PSMA PET/CT continues to gain traction in diagnosing localized prostate cancer.
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Belue MJ, Law YM, Marko J, Turkbey E, Malayeri A, Yilmaz EC, Lin Y, Johnson L, Merriman KM, Lay NS, Wood BJ, Pinto PA, Choyke PL, Harmon SA, Turkbey B. Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation. Acad Radiol 2024; 31:1429-1437. [PMID: 37858505 PMCID: PMC11015987 DOI: 10.1016/j.acra.2023.09.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/11/2023] [Accepted: 09/21/2023] [Indexed: 10/21/2023]
Abstract
RATIONALE AND OBJECTIVES Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias. MATERIALS AND METHODS This retrospective study included 1046 patients from three cohorts (ProstateX [n = 347], All-comer in-house [n = 602], enriched bad-quality MRI in-house [n = 97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa. RESULTS The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ = 0.588, κ = 0.649, κ = 0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ = 0.599, R1-R3 κ = 0.612, R2-R3 κ = 0.685), fair to moderate agreement with AI (R1-R3 κ = 0.445, κ = 0.410, κ = 0.292, respectively). AI showed substantial agreement with ground truth (κ = 0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18). CONCLUSION The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability.
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Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Jamie Marko
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA (J.M.)
| | - Evrim Turkbey
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., A.M., B.J.W.)
| | - Ashkan Malayeri
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., A.M., B.J.W.)
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Latrice Johnson
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Bradford J Wood
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., A.M., B.J.W.); Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (B.J.W.)
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (P.A.P.)
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.)
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (M.J.B., E.C.Y., Y.L., L.J., K.M.M, N.S.L., P.L.C., S.A.H., B.T.).
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6
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Gelikman DG, Kenigsberg AP, Mee Law Y, Yilmaz EC, Harmon SA, Parikh SH, Hyman JA, Huth H, Koller CR, Nethala D, Hesswani C, Merino MJ, Gurram S, Choyke PL, Wood BJ, Pinto PA, Turkbey B. Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System. EUR UROL SUPPL 2024; 62:74-80. [PMID: 38468864 PMCID: PMC10925932 DOI: 10.1016/j.euros.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
Background and objective Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI). Methods A retrospective cohort study was conducted involving patients who underwent primary FT for localized csPCa between 2013 and 2023, followed by post-FT mpMRI and a prostate biopsy. Two expert genitourinary radiologists retrospectively evaluated post-FT mpMRI using PI-FAB. The key measures included inter-reader agreement of PI-FAB scores, assessed by quadratic weighted Cohen's kappa (κ), and the system's efficacy in predicting in-field recurrence of csPCa, with a PI-FAB score cutoff of 3. Additional diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were also evaluated. Key findings and limitations Scans from 38 patients were analyzed, revealing a moderate level of agreement in PI-FAB scoring (κ = 0.56). Both radiologists achieved sensitivity of 93% in detecting csPCa, although specificity, PPVs, NPVs, and accuracy varied. Conclusions and clinical implications The PI-FAB scoring system exhibited high sensitivity with moderate inter-reader agreement in detecting in-field recurrence of csPCa. Despite promising results, its low specificity and PPV necessitate further refinement. These findings underscore the need for larger studies to validate the clinical utility of PI-FAB, potentially aiding in standardizing post-treatment surveillance. Patient summary Focal therapy has emerged as a promising approach for managing localized prostate cancer, but limitations in current imaging techniques present significant challenges for post-treatment surveillance. The Prostate Imaging after Focal Ablation (PI-FAB) scoring system showed high sensitivity for detecting in-field recurrence of clinically significant prostate cancer. However, its low specificity and positive predictive value necessitate further refinement. Larger, more comprehensive studies are needed to fully validate its clinical utility.
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Affiliation(s)
- David G. Gelikman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alexander P. Kenigsberg
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | - Enis C. Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A. Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sahil H. Parikh
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jason A. Hyman
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hannah Huth
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Christopher R. Koller
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Daniel Nethala
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Charles Hesswani
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria J. Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L. Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J. Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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7
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Johnson LA, Harmon SA, Yilmaz EC, Lin Y, Belue MJ, Merriman KM, Lay NS, Sanford TH, Sarma KV, Arnold CW, Xu Z, Roth HR, Yang D, Tetreault J, Xu D, Patel KR, Gurram S, Wood BJ, Citrin DE, Pinto PA, Choyke PL, Turkbey B. Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods. Abdom Radiol (NY) 2024:10.1007/s00261-024-04242-7. [PMID: 38512516 DOI: 10.1007/s00261-024-04242-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVE Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. RESULTS 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). CONCLUSION Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.
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Affiliation(s)
- Latrice A Johnson
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Karthik V Sarma
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Corey W Arnold
- Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ziyue Xu
- NVIDIA Corporation, Santa Clara, CA, USA
| | | | - Dong Yang
- NVIDIA Corporation, Santa Clara, CA, USA
| | | | - Daguang Xu
- NVIDIA Corporation, Santa Clara, CA, USA
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
- Molecular Imaging Branch (B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892, USA.
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8
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Wilkinson S, Ku AT, Lis RT, King IM, Low D, Trostel SY, Bright JR, Terrigino NT, Baj A, Fenimore JM, Li C, Vo B, Jansen CS, Ye H, Whitlock NC, Harmon SA, Carrabba NV, Atway R, Lake R, Kissick HT, Pinto PA, Choyke PL, Turkbey B, Dahut WL, Karzai F, Sowalsky AG. Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition. medRxiv 2024:2024.02.09.24302395. [PMID: 38370835 PMCID: PMC10871443 DOI: 10.1101/2024.02.09.24302395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Patients diagnosed with localized high-risk prostate cancer have higher rates of recurrence, and the introduction of neoadjuvant intensive hormonal therapies seeks to treat occult micrometastatic disease by their addition to definitive treatment. Sufficient profiling of baseline disease has remained a challenge in enabling the in-depth assessment of phenotypes associated with exceptional vs. poor pathologic responses after treatment. In this study, we report comprehensive and integrative gene expression profiling of 37 locally advanced prostate tumors prior to six months of androgen deprivation therapy (ADT) plus the androgen receptor (AR) inhibitor enzalutamide prior to radical prostatectomy. A robust transcriptional program associated with HER2 activity was positively associated with poor outcome and opposed AR activity, even after adjusting for common genomic alterations in prostate cancer including PTEN loss and expression of the TMPRSS2:ERG fusion. Patients experiencing exceptional pathologic responses demonstrated lower levels of HER2 and phospho-HER2 by immunohistochemistry of biopsy tissues. The inverse correlation of AR and HER2 activity was found to be a universal feature of all aggressive prostate tumors, validated by transcriptional profiling an external cohort of 121 patients and immunostaining of tumors from 84 additional patients. Importantly, the AR activity-low, HER2 activity-high cells that resist ADT are a pre-existing subset of cells that can be targeted by HER2 inhibition alone or in combination with enzalutamide. In summary, we show that prostate tumors adopt an AR activity-low prior to antiandrogen exposure that can be exploited by treatment with HER2 inhibitors.
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Affiliation(s)
- Scott Wilkinson
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Anson T Ku
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Rosina T Lis
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Isaiah M King
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Daniel Low
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Shana Y Trostel
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - John R Bright
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | | | - Anna Baj
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - John M Fenimore
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Chennan Li
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - BaoHan Vo
- Department of Urology, Emory University School of Medicine, Atlanta, GA, USA
| | - Caroline S Jansen
- Department of Urology, Emory University School of Medicine, Atlanta, GA, USA
| | - Huihui Ye
- Department of Pathology and Department of Urology, University of California Los Angeles, Los Angeles, CA, USA
| | - Nichelle C Whitlock
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | | | - Nicole V Carrabba
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Rayann Atway
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Ross Lake
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Haydn T Kissick
- Department of Urology, Emory University School of Medicine, Atlanta, GA, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Fatima Karzai
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Adam G Sowalsky
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
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9
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Belue MJ, Harmon SA, Yang D, An JY, Gaur S, Law YM, Turkbey E, Xu Z, Tetreault J, Lay NS, Yilmaz EC, Phelps TE, Simon B, Lindenberg L, Mena E, Pinto PA, Bagci U, Wood BJ, Citrin DE, Dahut WL, Madan RA, Gulley JL, Xu D, Choyke PL, Turkbey B. Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study. Acad Radiol 2024:S1076-6332(24)00008-4. [PMID: 38262813 DOI: 10.1016/j.acra.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 01/25/2024]
Abstract
RATIONALE AND OBJECTIVES Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). RESULTS In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). CONCLUSION Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.
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Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Dong Yang
- NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.)
| | - Julie Y An
- Department of Radiology, University of California, San Diego, California, USA (J.Y.A.)
| | - Sonia Gaur
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA (S.G.)
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Evrim Turkbey
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., B.J.W.)
| | - Ziyue Xu
- NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.)
| | - Jesse Tetreault
- NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.)
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Benjamin Simon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Liza Lindenberg
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Esther Mena
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (P.A.P.)
| | - Ulas Bagci
- Radiology and Biomedical Engineering Department, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA (U.B.)
| | - Bradford J Wood
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., B.J.W.); Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (B.J.W.)
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (D.E.C.)
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (W.L.D., R.A.M.)
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (W.L.D., R.A.M.)
| | - James L Gulley
- Center for Immuno-Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (J.L.G.)
| | - Daguang Xu
- NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.)
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.)
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).
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10
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Kaczanowska S, Murty T, Alimadadi A, Contreras CF, Duault C, Subrahmanyam PB, Reynolds W, Gutierrez NA, Baskar R, Wu CJ, Michor F, Altreuter J, Liu Y, Jhaveri A, Duong V, Anbunathan H, Ong C, Zhang H, Moravec R, Yu J, Biswas R, Van Nostrand S, Lindsay J, Pichavant M, Sotillo E, Bernstein D, Carbonell A, Derdak J, Klicka-Skeels J, Segal JE, Dombi E, Harmon SA, Turkbey B, Sahaf B, Bendall S, Maecker H, Highfill SL, Stroncek D, Glod J, Merchant M, Hedrick CC, Mackall CL, Ramakrishna S, Kaplan RN. Immune determinants of CAR-T cell expansion in solid tumor patients receiving GD2 CAR-T cell therapy. Cancer Cell 2024; 42:35-51.e8. [PMID: 38134936 PMCID: PMC10947809 DOI: 10.1016/j.ccell.2023.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 09/18/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023]
Abstract
Chimeric antigen receptor T cells (CAR-Ts) have remarkable efficacy in liquid tumors, but limited responses in solid tumors. We conducted a Phase I trial (NCT02107963) of GD2 CAR-Ts (GD2-CAR.OX40.28.z.iC9), demonstrating feasibility and safety of administration in children and young adults with osteosarcoma and neuroblastoma. Since CAR-T efficacy requires adequate CAR-T expansion, patients were grouped into good or poor expanders across dose levels. Patient samples were evaluated by multi-dimensional proteomic, transcriptomic, and epigenetic analyses. T cell assessments identified naive T cells in pre-treatment apheresis associated with good expansion, and exhausted T cells in CAR-T products with poor expansion. Myeloid cell assessment identified CXCR3+ monocytes in pre-treatment apheresis associated with good expansion. Longitudinal analysis of post-treatment samples identified increased CXCR3- classical monocytes in all groups as CAR-T numbers waned. Together, our data uncover mediators of CAR-T biology and correlates of expansion that could be utilized to advance immunotherapies for solid tumor patients.
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Affiliation(s)
- Sabina Kaczanowska
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tara Murty
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Ahmad Alimadadi
- La Jolla Institute for Immunology, La Jolla, CA, USA; Immunology Center of Georgia, Augusta University, Augusta, GA, USA; Georgia Cancer Center, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Cristina F Contreras
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Oncology, University of Oxford, Oxford, UK
| | - Caroline Duault
- Stanford Human Immune Monitoring Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Priyanka B Subrahmanyam
- Stanford Human Immune Monitoring Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Warren Reynolds
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Reema Baskar
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Catherine J Wu
- Broad Institute, Cambridge, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Yang Liu
- Broad Institute, Cambridge, MA, USA
| | | | - Vandon Duong
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Hima Anbunathan
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Claire Ong
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hua Zhang
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Radim Moravec
- Cancer Therapy Evaluation Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joyce Yu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Mina Pichavant
- Immunology Center of Georgia, Augusta University, Augusta, GA, USA
| | - Elena Sotillo
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Donna Bernstein
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amanda Carbonell
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanne Derdak
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jacquelyn Klicka-Skeels
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Julia E Segal
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eva Dombi
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bita Sahaf
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean Bendall
- Georgia Cancer Center, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Holden Maecker
- Immunology Center of Georgia, Augusta University, Augusta, GA, USA
| | - Steven L Highfill
- Center for Cellular Engineering, Department of Transfusion Medicine, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - David Stroncek
- Center for Cellular Engineering, Department of Transfusion Medicine, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - John Glod
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Melinda Merchant
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Catherine C Hedrick
- La Jolla Institute for Immunology, La Jolla, CA, USA; Immunology Center of Georgia, Augusta University, Augusta, GA, USA; Georgia Cancer Center, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Crystal L Mackall
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Sneha Ramakrishna
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Rosandra N Kaplan
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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11
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Yilmaz EC, Lin Y, Belue MJ, Harmon SA, Phelps TE, Merriman KM, Hazen LA, Garcia C, Johnson L, Lay NS, Toubaji A, Merino MJ, Patel KR, Parnes HL, Law YM, Wood BJ, Gurram S, Choyke PL, Pinto PA, Turkbey B. PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy. AJR Am J Roentgenol 2024; 222:e2329964. [PMID: 37729551 DOI: 10.2214/ajr.23.29964] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.
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Affiliation(s)
- Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Lindsey A Hazen
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD
- Department of Radiology, Clinical Center, NIH, Bethesda, MD
| | - Charisse Garcia
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD
- Department of Radiology, Clinical Center, NIH, Bethesda, MD
| | - Latrice Johnson
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Antoun Toubaji
- Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, MD
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, MD
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, NIH, Bethesda, MD
| | - Howard L Parnes
- Division of Cancer Prevention, National Cancer Institute, NIH, Bethesda, MD
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD
- Department of Radiology, Clinical Center, NIH, Bethesda, MD
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
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12
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Belue MJ, Harmon SA, Masoudi S, Barrett T, Law YM, Purysko AS, Panebianco V, Yilmaz EC, Lin Y, Jadda PK, Raavi S, Wood BJ, Pinto PA, Choyke PL, Turkbey B. Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study. Eur J Radiol 2024; 170:111259. [PMID: 38128256 PMCID: PMC10842312 DOI: 10.1016/j.ejrad.2023.111259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/23/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023]
Abstract
PURPOSE To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality. METHOD A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes. RESULTS The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis. CONCLUSION Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.
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Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Tristan Barrett
- Department of Radiology, University of Cambridge, Cambridge, England
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | - Andrei S Purysko
- Section of Abdominal Imaging, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Pavan Kumar Jadda
- Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Sitarama Raavi
- Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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13
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Ajkunic A, Sayar E, Roudier MP, Patel RA, Coleman IM, De Sarkar N, Hanratty B, Adil M, Zhao J, Zaidi S, True LD, Sperger JM, Cheng HH, Yu EY, Montgomery RB, Hawley JE, Ha G, Lee JK, Harmon SA, Corey E, Lang JM, Sawyers CL, Morrissey C, Schweizer MT, Gulati R, Nelson PS, Haffner MC. ASSESSMENT OF CELL SURFACE TARGETS IN METASTATIC PROSTATE CANCER: EXPRESSION LANDSCAPE AND MOLECULAR CORRELATES. Res Sq 2023:rs.3.rs-3745991. [PMID: 38196594 PMCID: PMC10775381 DOI: 10.21203/rs.3.rs-3745991/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Therapeutic approaches targeting proteins on the surface of cancer cells have emerged as an important strategy for precision oncology. To fully capitalize on the potential impact of drugs targeting surface proteins, detailed knowledge about the expression patterns of the target proteins in tumor tissues is required. In castration-resistant prostate cancer (CRPC), agents targeting prostate-specific membrane antigen (PSMA) have demonstrated clinical activity. However, PSMA expression is lost in a significant number of CRPC tumors, and the identification of additional cell surface targets is necessary in order to develop new therapeutic approaches. Here, we performed a comprehensive analysis of the expression and co-expression patterns of trophoblast cell-surface antigen 2 (TROP2), delta-like ligand 3 (DLL3), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) in CRPC samples from a rapid autopsy cohort. We show that DLL3 and CEACAM5 exhibit the highest expression in neuroendocrine prostate cancer (NEPC), while TROP2 is expressed across different CRPC molecular subtypes, except for NEPC. We observed variable intra-tumoral and inter-tumoral heterogeneity and no dominant metastatic site predilections for TROP2, DLL3, and CEACAM5. We further show that AR amplifications were associated with higher expression of PSMA and TROP2 but lower DLL3 and CEACAM5 levels. Conversely, PSMA and TROP2 expression was lower in RB1-altered tumors. In addition to genomic alterations, we demonstrate a tight correlation between epigenetic states, particularly histone H3 lysine 27 methylation (H3K27me3) at the transcriptional start site and gene body of TACSTD2 (encoding TROP2), DLL3, and CEACAM5, and their respective protein expression in CRPC patient-derived xenografts. Collectively, these findings provide novel insights into the patterns and determinants of expression of TROP2, DLL3, and CEACAM5 with important implications for the clinical development of cell surface targeting agents in CRPC.
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Affiliation(s)
- Azra Ajkunic
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Erolcan Sayar
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | - Radhika A Patel
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ilsa M Coleman
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Navonil De Sarkar
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Medical College of Wisconsin Cancer Center, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, WI, USA
| | - Brian Hanratty
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Mohamed Adil
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Jimmy Zhao
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samir Zaidi
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lawrence D True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - Heather H Cheng
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Evan Y Yu
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Robert B Montgomery
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jessica E Hawley
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Gavin Ha
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - John K Lee
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Stephanie A Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | - Eva Corey
- Department of Urology, University of Washington, Seattle, WA, USA
| | | | - Charles L Sawyers
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Colm Morrissey
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Michael T Schweizer
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Roman Gulati
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Peter S Nelson
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Urology, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Michael C Haffner
- Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Division of Clinical Research, Fred Hutchinson Cancer Center, Seattle, WA, USA
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Merriman KM, Harmon SA, Belue MJ, Yilmaz EC, Blake Z, Lay NS, Phelps TE, Merino MJ, Parnes HL, Law YM, Gurram S, Wood BJ, Choyke PL, Pinto PA, Turkbey B. Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy. AJR Am J Roentgenol 2023; 221:773-787. [PMID: 37404084 DOI: 10.2214/ajr.23.29609] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.
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Affiliation(s)
- Katie M Merriman
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Stephanie A Harmon
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Mason J Belue
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Enis C Yilmaz
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Zoë Blake
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD
| | - Nathan S Lay
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Tim E Phelps
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | | | | | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | | | - Bradford J Wood
- Center for Interventional Oncology, NCI, NIH, Bethesda, MD
- Department of Radiology, Clinical Center, NIH, Bethesda, MD
| | - Peter L Choyke
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | | | - Baris Turkbey
- Molecular Imaging Branch, NCI, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
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15
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Belue MJ, Blake Z, Yilmaz EC, Lin Y, Harmon SA, Nemirovsky DR, Enders JJ, Kenigsberg AP, Mendhiratta N, Rothberg M, Toubaji A, Merino MJ, Gurram S, Wood BJ, Choyke PL, Turkbey B, Pinto PA. Is prostatic adenocarcinoma with cribriform architecture more difficult to detect on prostate MRI? Prostate 2023; 83:1519-1528. [PMID: 37622756 PMCID: PMC10840859 DOI: 10.1002/pros.24610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/24/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden. METHODS Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy. Biopsy cores and lesions were categorized as follows: C0 = benign, C1 = PCa with no CBFM pattern, C2 = PCa with CBFM pattern. Correlation between cancer core length (CCL) and measured MRI lesion dimension were assessed using a modified Pearson correlation test for clustered data. Differences between the biopsy core groups were assessed with the Wilcoxon-signed rank test with clustering. RESULTS Between 2015 and 2022, a total of 131 consecutive patients with CBFM pattern on prostate biopsy and pre-biopsy mpMRI were included. Clinical feature analysis included 1572 systematic biopsy cores (1149 C0, 272 C1, 151 C2) and 736 MRI-targeted biopsy cores (253 C0, 272 C1, 211 C2). Of the 131 patients with confirmed CBFM pathology, targeted biopsy (TBx) alone identified CBFM in 76.3% (100/131) of patients and detected PCa in 97.7% (128/131) patients. SBx biopsy alone detected CBFM in 61.1% (80/131) of patients and PCa in 90.8% (119/131) patients. TBx and SBx had equivalent detection in patients with smaller prostates (p = 0.045). For both PCa lesion groups there was a positive and significant correlation between maximum MRI lesion dimension and CCL (C1 lesions: p < 0.01, C2 lesions: p < 0.001). There was a significant difference in CCL between C1 and C2 lesions for T2 scores of 3 and 5 (p ≤ 0.01, p ≤ 0.01, respectively) and PI-RADS 5 lesions (p ≤ 0.01), with C2 lesions having larger CCL, despite no significant difference in MRI lesion dimension. CONCLUSIONS The extent of disease for CBFM-containing tumors is difficult to capture on mpMRI. When comparing MRI lesions of similar dimensions and PIRADS scores, CBFM-containing tumors appear to have larger cancer yield on biopsy. Proper staging and planning of therapeutic interventions is reliant on accurate mpMRI estimation. Special considerations should be taken for patients with CBFM pattern on prostate biopsy.
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Affiliation(s)
- Mason J. Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Zoë Blake
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Enis C. Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stephanie A. Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Daniel R. Nemirovsky
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jacob J. Enders
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Alexander P. Kenigsberg
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Neil Mendhiratta
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Michael Rothberg
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Antoun Toubaji
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Maria J. Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sandeep Gurram
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Bradford J. Wood
- Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter L. Choyke
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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16
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Yilmaz EC, Harmon SA, Belue MJ, Merriman KM, Phelps TE, Lin Y, Garcia C, Hazen L, Patel KR, Merino MJ, Wood BJ, Choyke PL, Pinto PA, Citrin DE, Turkbey B. Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI. Eur J Radiol 2023; 168:111095. [PMID: 37717420 PMCID: PMC10615746 DOI: 10.1016/j.ejrad.2023.111095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 09/04/2023] [Accepted: 09/12/2023] [Indexed: 09/19/2023]
Abstract
OBJECTIVE To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history. MATERIALS AND METHODS This study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles. RESULTS Of the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively. CONCLUSION The reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.
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Affiliation(s)
- Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Charisse Garcia
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Lindsey Hazen
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, United States.
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17
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Lin Y, Belue MJ, Yilmaz EC, Harmon SA, An J, Law YM, Hazen L, Garcia C, Merriman KM, Phelps TE, Lay NS, Toubaji A, Merino MJ, Wood BJ, Gurram S, Choyke PL, Pinto PA, Turkbey B. Deep Learning-Based T2-weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates. J Magn Reson Imaging 2023:10.1002/jmri.29031. [PMID: 37811666 PMCID: PMC11001787 DOI: 10.1002/jmri.29031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 09/15/2023] [Accepted: 09/15/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis. PURPOSE To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm. STUDY TYPE Retrospective. SUBJECTS 615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy. FIELD STRENGTH/SEQUENCE 3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced. ASSESSMENTS Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category. STATISTICAL TESTS Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant. RESULTS 385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09). DATA CONCLUSION Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Mason J. Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Enis C. Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Stephanie A. Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Julie An
- Department of Radiology, University of California San Diego, San Diego, CA
| | - Yan Mee Law
- Department of Radiology Singapore General Hospital, Singapore
| | - Lindsey Hazen
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Charisse Garcia
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Katie M. Merriman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Tim E. Phelps
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Nathan S. Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Antoun Toubaji
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Maria J. Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Bradford J. Wood
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, MD
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Peter L. Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
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18
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Ku AT, Shankavaram U, Trostel SY, Zhang H, Sater HA, Harmon SA, Carrabba NV, Liu Y, Wood BJ, Pinto PA, Choyke PL, Stoyanova R, Davicioni E, Pollack A, Turkbey B, Sowalsky AG, Citrin DE. Radiogenomic profiling of prostate tumors prior to external beam radiotherapy converges on a transcriptomic signature of TGF-β activity driving tumor recurrence. medRxiv 2023:2023.05.01.23288883. [PMID: 37205576 PMCID: PMC10187349 DOI: 10.1101/2023.05.01.23288883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Background Patients with localized prostate cancer have historically been assigned to clinical risk groups based on local disease extent, serum prostate specific antigen (PSA), and tumor grade. Clinical risk grouping is used to determine the intensity of treatment with external beam radiotherapy (EBRT) and androgen deprivation therapy (ADT), yet a substantial proportion of patients with intermediate and high risk localized prostate cancer will develop biochemical recurrence (BCR) and require salvage therapy. Prospective identification of patients destined to experience BCR would allow treatment intensification or selection of alternative therapeutic strategies. Methods Twenty-nine individuals with intermediate or high risk prostate cancer were prospectively recruited to a clinical trial designed to profile the molecular and imaging features of prostate cancer in patients undergoing EBRT and ADT. Whole transcriptome cDNA microarray and whole exome sequencing were performed on pretreatment targeted biopsy of prostate tumors (n=60). All patients underwent pretreatment and 6-month post EBRT multiparametric MRI (mpMRI), and were followed with serial PSA to assess presence or absence of BCR. Genes differentially expressed in the tumor of patients with and without BCR were investigated using pathways analysis tools and were similarly explored in alternative datasets. Differential gene expression and predicted pathway activation were evaluated in relation to tumor response on mpMRI and tumor genomic profile. A novel TGF-β gene signature was developed in the discovery dataset and applied to a validation dataset. Findings Baseline MRI lesion volume and PTEN/TP53 status in prostate tumor biopsies correlated with the activation state of TGF-β signaling measured using pathway analysis. All three measures correlated with the risk of BCR after definitive RT. A prostate cancer-specific TGF-β signature discriminated between patients that experienced BCR vs. those that did not. The signature retained prognostic utility in an independent cohort. Interpretation TGF-β activity is a dominant feature of intermediate-to-unfavorable risk prostate tumors prone to biochemical failure after EBRT with ADT. TGF-β activity may serve as a prognostic biomarker independent of existing risk factors and clinical decision-making criteria. Funding This research was supported by the Prostate Cancer Foundation, the Department of Defense Congressionally Directed Medical Research Program, National Cancer Institute, and the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.
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Affiliation(s)
- Anson T. Ku
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Uma Shankavaram
- Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, USA
| | - Shana Y. Trostel
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Hong Zhang
- Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, USA
| | - Houssein A. Sater
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | | | - Nicole V. Carrabba
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Yang Liu
- Veracyte, Inc., South San Francisco, CA, USA
| | - Bradford J. Wood
- Center for Interventional Oncology, NIH Clinical Center, Bethesda, MD, USA
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD, USA
| | - Peter L. Choyke
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami, Miami, FL, USA
| | | | - Alan Pollack
- Department of Radiation Oncology, University of Miami, Miami, FL, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA
| | - Adam G. Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Deborah E. Citrin
- Radiation Oncology Branch, National Cancer Institute, Bethesda, MD, USA
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19
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Yilmaz EC, Shih JH, Belue MJ, Harmon SA, Phelps TE, Garcia C, Hazen LA, Toubaji A, Merino MJ, Gurram S, Choyke PL, Wood BJ, Pinto PA, Turkbey B. Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers. Radiology 2023; 307:e221309. [PMID: 37129493 PMCID: PMC10323290 DOI: 10.1148/radiol.221309] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 01/21/2023] [Accepted: 02/10/2023] [Indexed: 05/03/2023]
Abstract
Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.
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Affiliation(s)
- Enis C. Yilmaz
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Joanna H. Shih
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Mason J. Belue
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Stephanie A. Harmon
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Tim E. Phelps
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Charisse Garcia
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Lindsey A. Hazen
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Antoun Toubaji
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Maria J. Merino
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Sandeep Gurram
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Peter L. Choyke
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Bradford J. Wood
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Peter A. Pinto
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Baris Turkbey
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
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20
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Phelps TE, Harmon SA, Mena E, Lindenberg L, Shih JH, Citrin DE, Pinto PA, Wood BJ, Dahut WL, Gulley JL, Madan RA, Choyke PL, Turkbey B. Predicting Outcomes of Indeterminate Bone Lesions on 18F-DCFPyL PSMA PET/CT Scans in the Setting of High-Risk Primary or Recurrent Prostate Cancer. J Nucl Med 2023; 64:395-401. [PMID: 36265908 DOI: 10.2967/jnumed.122.264334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
Indeterminate bone lesions (IBLs) on prostate-specific membrane antigen (PSMA) PET/CT are common. This study aimed to define variables that predict whether such lesions are likely malignant or benign using features on PSMA PET/CT. Methods: 18F-DCFPyL PET/CT imaging was performed on 243 consecutive patients with high-risk primary or biochemically recurrent prostate cancer. IBLs identified on PSMA PET/CT could not definitively be interpreted as benign or malignant. Medical records of patients with IBLs were reviewed to determine the ultimate status of each lesion. IBLs were deemed malignant or benign on the basis of evidence of progression or stability at follow-up, respectively, or by biopsy results; IBLs were deemed equivocal when insufficient or unclear evidence existed. Post hoc patient, lesion, and scan variables accounting for clustered data were evaluated using Wilcoxon rank-sum and χ2 tests to determine features that favored benign or malignant interpretation. Results: Overall, 98 IBLs within 267 bone lesions (36.7%) were identified in 48 of 243 patients (19.8%). Thirty-seven of 98 IBLs were deemed benign, and 42 were deemed malignant, of which 8 had histologic verification; 19 remained equivocal. Location and SUVmax categorical variables were predictive of IBL interpretation (P = 0.0201 and P = 0.0230, respectively). For IBLs with new interpretations, 34 of 37 (91.9%) considered benign showed an SUVmax of less than 5 or exhibited focal uptake without coexisting bone metastases; 37 of 42 (88.1%) deemed malignant demonstrated an SUVmax of at least 5 or were present with coexisting bone metastases. Logistic regression predicted IBLs with a high SUVmax (univariable: odds ratio [OR], 9.29 [P = 0.0016]; multivariable: OR, 13.87 [P = 0.0089]) or present with other bone metastases (univariable: OR, 9.87 [P = 0.0112]; multivariable: OR, 11.35 [P = 0.003]) to be malignant. Conclusion: IBLs on PSMA PET/CT are concerning; however, characterizing their location, SUV, and additional scan findings can aid interpretation. IBLs displaying an SUVmax of at least 5 or present with other bone metastases favor malignancy. IBLs without accompanying bone metastases that exhibit an SUVmax of less than 5 and are observed only in atypical locations favor benign processes. These guidelines may assist in the interpretation of IBLs on PSMA PET/CT.
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Affiliation(s)
- Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland;
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Esther Mena
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Liza Lindenberg
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Joanna H Shih
- Biometric Research Program, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Bradford J Wood
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland.,Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland; and
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - James L Gulley
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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21
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Phelps TE, Yilmaz EC, Harmon SA, Belue MJ, Shih JH, Garcia C, Hazen LA, Toubaji A, Merino MJ, Gurram S, Choyke PL, Wood BJ, Pinto PA, Turkbey B. Ipsilateral hemigland prostate biopsy may underestimate cancer burden in patients with unilateral mpMRI-visible lesions. Abdom Radiol (NY) 2023; 48:1079-1089. [PMID: 36526922 PMCID: PMC10765956 DOI: 10.1007/s00261-022-03775-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
PURPOSE To evaluate the cancer detection rates of reduced-core biopsy schemes in patients with unilateral mpMRI-visible intraprostatic lesions and to analyze the contribution of systematic biopsy cores in clinically significant prostate cancer (csPCa) detection. METHODS 212 patients with mpMRI-visible unilateral intraprostatic lesions undergoing MRI/TRUS fusion-guided targeted biopsy (TBx) and systematic biopsy (SBx) were included. Cancer detection rates of TBx + SBx, as determined by highest Gleason Grade Group (GG), were compared to 3 reduced-core biopsy schemes: TBx alone, TBx + ipsilateral systematic biopsy (IBx; MRI-positive hemigland), and TBx + contralateral systematic biopsy (CBx; MRI-negative hemigland). Patient-level and biopsy core-level data were analyzed using descriptive statistics with confidence intervals. Univariable and multivariable logistic regression analysis was conducted to identify predictors of csPCa (≥ GG2) detected in MRI-negative hemiglands at p < 0.05. RESULTS Overall, 43.4% (92/212) of patients had csPCa and 66.0% (140/212) of patients had any PCa detected by TBx + SBx. Of patients with csPCa, 81.5% had exclusively ipsilateral involvement (MRI-positive), 7.6% had only contralateral involvement (MRI-negative), and 10.9% had bilateral involvement. The csPCa detection rates of reduced-core biopsy schemes were 35.4% (75/212), 40.1% (85/212), and 39.6% (84/212) for TBx alone, TBx + IBx, and TBx + CBx, respectively, with detection sensitivities of 81.5%, 92.4%, and 91.3% compared to TBx + SBx. CONCLUSION Reduced-core prostate biopsy strategies confined to the ipsilateral hemigland underestimate csPCa burden by at least 8% in patients with unilateral mpMRI-visible intraprostatic lesions. The combined TBx + SBx strategy maximizes csPCa detection.
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Affiliation(s)
- Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Joanna H Shih
- Biometric Research Program, National Cancer Institute, NIH, Rockville, MD, USA
| | - Charisse Garcia
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Lindsey A Hazen
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Antoun Toubaji
- Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
- Molecular Imaging Branch, National Cancer Institute, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA.
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22
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Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, Turkbey B. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. J Am Coll Radiol 2023; 20:134-145. [PMID: 35922018 PMCID: PMC9887098 DOI: 10.1016/j.jacr.2022.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research. MATERIALS AND METHODS This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons. RESULTS In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation. CONCLUSION A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.
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Affiliation(s)
- Mason J Belue
- Medical Research Scholars Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan S Lay
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Intramural Research Training Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Tim E Phelps
- Postdoctoral Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Artificial Intelligence Resource, Chief of Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Senior Clinician/Director, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
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23
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Belue MJ, Harmon SA, Patel K, Daryanani A, Yilmaz EC, Pinto PA, Wood BJ, Citrin DE, Choyke PL, Turkbey B. Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra. Acad Radiol 2022; 29:1404-1412. [PMID: 35183438 PMCID: PMC9339453 DOI: 10.1016/j.acra.2022.01.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/10/2022] [Accepted: 01/12/2022] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVE The combined use of prostate cancer radiotherapy and MRI planning is increasingly being used in the treatment of clinically significant prostate cancers. The radiotherapy dosage quantity is limited by toxicity in organs with de-novo genitourinary toxicity occurrence remaining unperturbed. Estimation of the urethral radiation dose via anatomical contouring may improve our understanding of genitourinary toxicity and its related symptoms. Yet, urethral delineation remains an expert-dependent and time-consuming procedure. In this study, we aim to develop a fully automated segmentation tool for the prostatic urethra. MATERIALS AND METHODS This study incorporated 939 patients' T2-weighted MRI scans (train/validation/test/excluded: 657/141/140/1 patients), including in-house and public PROSTATE-x datasets, and their corresponding ground truth urethral contours from an expert genitourinary radiologist. The AI model was developed using MONAI framework and was based on a 3D-UNet. AI model performance was determined by Dice score (volume-based) and the Centerline Distance (CLD) between the prediction and ground truth centers (slice-based). All predictions were compared to ground truth in a systematic failure analysis to elucidate the model's strengths and weaknesses. The Wilcoxon-rank sum test was used for pair-wise comparison of group differences. RESULTS The overall organ-adjusted Dice score for this model was 0.61 and overall CLD was 2.56 mm. When comparing prostates with symmetrical (n = 117) and asymmetrical (n = 23) benign prostate hyperplasia (BPH), the AI model performed better on symmetrical prostates compared to asymmetrical in both Dice score (0.64 vs. 0.51 respectively, p < 0.05) and mean CLD (2.3 mm vs. 3.8 mm respectively, p < 0.05). When calculating location-specific performance, the performance was highest at the apex and lowest at the base location of the prostate for Dice and CLD. Dice location dependence: symmetrical (Apex, Mid, Base: 0.69 vs. 0.67 vs. 0.54 respectively, p < 0.05) and asymmetrical (Apex, Mid, Base: 0.68 vs. 0.52 vs. 0.39 respectively, p < 0.05). CLD location dependence: symmetrical (Apex, Mid, Base: 1.43 mm vs. 2.15 mm vs. 3.28 mm, p < 0.05) and asymmetrical (Apex, Mid, Base: 1.83 mm vs. 3.1 mm vs. 6.24 mm, p < 0.05). CONCLUSION We developed a fully automated prostatic urethra segmentation AI tool yielding its best performance in prostate glands with symmetric BPH features. This system can potentially be used to assist treatment planning in patients who can undergo whole gland radiation therapy or ablative focal therapy.
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Affiliation(s)
- Mason J Belue
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Stephanie A Harmon
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Krishnan Patel
- Radiation Oncology Branch (K.P., D.E.C.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Enis Cagatay Yilmaz
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Peter A Pinto
- Urologic Oncology Branch (P.A.P.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Bradford J Wood
- Center for Interventional Oncology (B.J.W.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland; Department of Radiology (B.J.W.), Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Deborah E Citrin
- Radiation Oncology Branch (K.P., D.E.C.), National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Baris Turkbey
- Molecular Imaging Branch (M.J.B., S.A.H., A.D., E.C.Y., P.L.C., B.T.), National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland.
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24
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Mehralivand S, Yang D, Harmon SA, Xu D, Xu Z, Roth H, Masoudi S, Sanford TH, Kesani D, Lay NS, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Turkbey B. A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging. Acad Radiol 2022; 29:1159-1168. [PMID: 34598869 PMCID: PMC10575564 DOI: 10.1016/j.acra.2021.08.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/08/2021] [Accepted: 08/21/2021] [Indexed: 01/08/2023]
Abstract
RATIONALE AND OBJECTIVES Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out. MATERIALS AND METHODS T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n = 1043 in-house and n = 347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist. Suspicious lesions were contoured and assigned a PI-RADS category. A 3D U-Net-based deep neural network was used to train an algorithm for automated detection and segmentation of prostate MRI lesions. Two 3D residual neural network were used for a 4-class classification task to predict PI-RADS categories 2 to 5 and BPH. Training and validation used 89% (n = 1290 scans) of the data using 5 fold cross-validation, the remaining 11% (n = 150 scans) were used for independent testing. Algorithm performance at lesion level was assessed using sensitivities, positive predictive values (PPV), false discovery rates (FDR), classification accuracy, Dice similarity coefficient (DSC). Additional analysis was conducted to compare AI algorithm's lesion detection performance with targeted biopsy results. RESULTS Median age was 66 years (IQR = 60-71), PSA 6.7 ng/ml (IQR = 4.7-9.9) from in-house cohort. In the independent test set, algorithm correctly detected 111 of 198 lesions leading to 56.1% (49.3%-62.6%) sensitivity. PPV was 62.7% (95% CI 54.7%-70.7%) with FDR of 37.3% (95% CI 29.3%-45.3%). Of 79 true positive lesions, 82.3% were tumor positive at targeted biopsy, whereas of 57 false negative lesions, 50.9% were benign at targeted biopsy. Median DSC for lesion segmentation was 0.359. Overall PI-RADS classification accuracy was 30.8% (95% CI 24.6%-37.8%). CONCLUSION Our cascaded U-Net, residual network architecture can detect, classify cancer suspicious lesions at prostate MRI with good detection, reasonable classification performance metrics.
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Affiliation(s)
- Sherif Mehralivand
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Dong Yang
- NVIDIA Corporation, Santa Clara, California
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Daguang Xu
- NVIDIA Corporation, Santa Clara, California
| | - Ziyue Xu
- NVIDIA Corporation, Santa Clara, California
| | | | - Samira Masoudi
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Thomas H Sanford
- Department of Urology, SUNY Upstate Medical University, Syracuse, New Yor
| | - Deepak Kesani
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland.
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Mehralivand S, Yang D, Harmon SA, Xu D, Xu Z, Roth H, Masoudi S, Kesani D, Lay N, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Turkbey B. Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI. Abdom Radiol (NY) 2022; 47:1425-1434. [PMID: 35099572 PMCID: PMC10506420 DOI: 10.1007/s00261-022-03419-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 01/09/2022] [Accepted: 01/10/2022] [Indexed: 11/01/2022]
Abstract
PURPOSE To present fully automated DL-based prostate cancer detection system for prostate MRI. METHODS MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics. RESULTS 525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0-6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0-5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0-7), 1.40 (range 0-6) in UNet, AHNet models, respectively. CONCLUSION We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.
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Affiliation(s)
| | - Dong Yang
- NVIDIA Corporation, Santa Clara, CA, USA
| | | | - Daguang Xu
- NVIDIA Corporation, Santa Clara, CA, USA
| | - Ziyue Xu
- NVIDIA Corporation, Santa Clara, CA, USA
| | | | | | - Deepak Kesani
- Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | - Nathan Lay
- Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | | | - Bradford J Wood
- Center for Interventional Oncology, NCI, NIH, Bethesda, MD, USA
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | | | - Baris Turkbey
- Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA.
- Molecular Imaging Branch, National Cancer Institute, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, MD, 20892-1088, USA.
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26
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Ma K, Harmon SA, Klyuzhin IS, Rahmim A, Turkbey B. Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer. PET Clin 2021; 17:137-143. [PMID: 34809863 DOI: 10.1016/j.cpet.2021.09.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PET imaging with targeted novel tracers has been commonly used in the clinical management of prostate cancer. The use of artificial intelligence (AI) in PET imaging is a relatively new approach and in this review article, we will review the current trends and categorize the currently available research into the quantification of tumor burden within the organ, evaluation of metastatic disease, and translational/supplemental research which aims to improve other AI research efforts.
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Affiliation(s)
- Kevin Ma
- Artificial Intelligence Resource, Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | - Stephanie A Harmon
- Artificial Intelligence Resource, Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA
| | - Ivan S Klyuzhin
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada; Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada; Department of Physics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Baris Turkbey
- Artificial Intelligence Resource, Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA.
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Harmon SA, Sanford TH, Brown GT, Yang C, Mehralivand S, Jacob JM, Valera VA, Shih JH, Agarwal PK, Choyke PL, Turkbey B. Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer. JCO Clin Cancer Inform 2021; 4:367-382. [PMID: 32330067 DOI: 10.1200/cci.19.00155] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To develop an artificial intelligence (AI)-based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors and train the model using cystectomy specimens available from The Cancer Genome Atlas (TCGA) Project; patients from our institution were included for validation of the leave-out test cohort. METHODS In all, 307 patients were identified for inclusion in the study (TCGA, n = 294; in-house, n = 13). Deep learning models were trained from image patches at 2.5×, 5×, 10×, and 20× magnifications, and spatially resolved prediction maps were combined with microenvironment (lymphocyte infiltration) features to derive a final patient-level AI score (probability of LN metastasis). Training and validation included 219 patients (training, n = 146; validation, n = 73); 89 patients (TCGA, n = 75; in-house, n = 13) were reserved as an independent testing set. Multivariable logistic regression models for predicting LN status based on clinicopathologic features alone and a combined model with AI score were fit to training and validation sets. RESULTS Several patients were determined to have positive LN metastasis in TCGA (n = 105; 35.7%) and in-house (n = 3; 23.1%) cohorts. A clinicopathologic model that considered using factors such as age, T stage, and lymphovascular invasion demonstrated an area under the curve (AUC) of 0.755 (95% CI, 0.680 to 0.831) in the training and validation cohorts compared with the cross validation of the AI score (likelihood of positive LNs), which achieved an AUC of 0.866 (95% CI, 0.812 to 0.920; P = .021). Performance in the test cohort was similar, with a clinicopathologic model AUC of 0.678 (95% CI, 0.554 to 0.802) and an AI score of 0.784 (95% CI, 0.702 to 0.896; P = .21). In addition, the AI score remained significant after adjusting for clinicopathologic variables (P = 1.08 × 10-9), and the combined model significantly outperformed clinicopathologic features alone in the test cohort with an AUC of 0.807 (95% CI, 0.702 to 0.912; P = .047). CONCLUSION Patients who are at higher risk of having positive LNs during cystectomy can be identified on primary tumor samples using novel AI-based methodologies applied to digital hematoxylin and eosin-stained slides.
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Affiliation(s)
- Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD.,Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD
| | - Thomas H Sanford
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD.,Department of Urology, Upstate Medical University, Syracuse, NY
| | - G Thomas Brown
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD.,National Library of Medicine, National Institutes of Health, Bethesda, MD
| | - Chris Yang
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD
| | | | - Joseph M Jacob
- Department of Urology, Upstate Medical University, Syracuse, NY
| | | | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis, Biometric Research Program, National Cancer Institute, Bethesda, MD
| | - Piyush K Agarwal
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD
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28
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Abdul Sater H, Marté JL, Donahue RN, Walter-Rodriguez B, Heery CR, Steinberg SM, Cordes LM, Chun G, Karzai F, Bilusic M, Harmon SA, Turkbey IB, Choyke PL, Schlom J, Dahut WL, Madan RA, Pinto PA, Gulley JL. Neoadjuvant PROSTVAC prior to radical prostatectomy enhances T-cell infiltration into the tumor immune microenvironment in men with prostate cancer. J Immunother Cancer 2021; 8:jitc-2020-000655. [PMID: 32269146 PMCID: PMC7174144 DOI: 10.1136/jitc-2020-000655] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2020] [Indexed: 02/07/2023] Open
Abstract
Background Clinical trials have shown the ability of therapeutic vaccines to generate immune responses to tumor-associated antigens (TAAs). What is relatively less known is if this translates into immune-cell (IC) infiltration into the tumor microenvironment. This study examined whether neoadjuvant prostate-specific antigen (PSA)-targeted vaccination with PROSTVAC could induce T-cell immunity, particularly at the tumor site. Methods An open-label, phase II study of neoadjuvant PROSTVAC vaccine enrolled 27 patients with localized prostate cancer awaiting radical prostatectomy (RP). We evaluated increases in CD4 and CD8 T-cell infiltrates (RP tissue vs baseline biopsies) using a six-color multiplex immunofluorescence Opal method. Antigen-specific responses were assessed by intracellular cytokine staining after in vitro stimulation of peripheral blood mononuclear cells with overlapping 15-mer peptide pools encoding the TAAs PSA, brachyury and MUC-1. Results Of 27 vaccinated patients, 26 had matched prevaccination (biopsy) and postvaccination (RP) prostate samples available for non-compartmentalized analysis (NCA) and compartmentalized analysis (CA). Tumor CD4 T-cell infiltrates were significantly increased in postvaccination RP specimens compared with baseline biopsies by NCA (median 176/mm² vs 152/mm²; IQR 136–317/mm² vs 69–284/mm²; p=0.0249; median ratio 1.20; IQR 0.64–2.25). By CA, an increase in both CD4 T-cell infiltrates at the tumor infiltrative margin (median 198/mm² vs 151/mm²; IQR 123–500/mm² vs 85–256/mm²; p=0.042; median ratio 1.44; IQR 0.59–4.17) and in CD8 T-cell infiltrates at the tumor core (median 140/mm² vs 105/mm²; IQR 91–175/mm² vs 83–163/mm²; p=0.036; median ratio 1.25; IQR 0.88–2.09) were noted in postvaccination RP specimens compared with baseline biopsies. A total of 13/25 patients (52%) developed peripheral T-cell responses to any of the three tested TAAs (non-neoantigens); five of these had responses to more than one antigen of the three evaluated. Conclusion Neoadjuvant PROSTVAC can induce both tumor immune response and peripheral immune response. Trial registration number NCT02153918.
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Affiliation(s)
- Houssein Abdul Sater
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jennifer L Marté
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Renee N Donahue
- Laboratory of Tumor Immunology and Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Beatriz Walter-Rodriguez
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Seth M Steinberg
- Biostatistics and Data Management Section, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Lisa M Cordes
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Guinevere Chun
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Fatima Karzai
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marijo Bilusic
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stephanie A Harmon
- Molecular Imaging Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.,Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland, USA
| | - Ismail Baris Turkbey
- Molecular Imaging Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter L Choyke
- Molecular Imaging Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jeffrey Schlom
- Laboratory of Tumor Immunology and Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ravi A Madan
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter A Pinto
- Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - James L Gulley
- Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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Masoudi S, Mehralivand S, Harmon SA, Lay N, Lindenberg L, Mena E, Pinto PA, Citrin DE, Gulley JL, Wood BJ, Dahut WL, Madan RA, Bagci U, Choyke PL, Turkbey B. Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans. IEEE Access 2021; 9:87531-87542. [PMID: 34733603 PMCID: PMC8562651 DOI: 10.1109/access.2021.3074051] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
In this study, we formulated an efficient deep learning-based classification strategy for characterizing metastatic bone lesions using computed tomography scans (CTs) of prostate cancer patients. For this purpose, 2,880 annotated bone lesions from CT scans of 114 patients diagnosed with prostate cancer were used for training, validation, and final evaluation. These annotations were in the form of lesion full segmentation, lesion type and labels of either benign or malignant. In this work, we present our approach in developing the state-of-the-art model to classify bone lesions as benign or malignant, where (1) we introduce a valuable dataset to address a clinically important problem, (2) we increase the reliability of our model by patient-level stratification of our dataset following lesion-aware distribution at each of the training, validation, and test splits, (3) we explore the impact of lesion texture, morphology, size, location, and volumetric information on the classification performance, (4) we investigate the functionality of lesion classification using different algorithms including lesion-based average 2D ResNet-50, lesion-based average 2D ResNeXt-50, 3D ResNet-18, 3D ResNet-50, as well as the ensemble of 2D ResNet-50 and 3D ResNet-18. For this purpose, we employed a train/validation/test split equal to 75%/12%/13% with several data augmentation methods applied to the training dataset to avoid overfitting and to increase reliability. We achieved an accuracy of 92.2% for correct classification of benign vs. malignant bone lesions in the test set using an ensemble of lesion-based average 2D ResNet-50 and 3D ResNet-18 with texture, volumetric information, and morphology having the greatest discriminative power respectively. To the best of our knowledge, this is the highest ever achieved lesion-level accuracy having a very comprehensive data set for such a clinically important problem. This level of classification performance in the early stages of metastasis development bodes well for clinical translation of this strategy.
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Affiliation(s)
- Samira Masoudi
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sherif Mehralivand
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Nathan Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Liza Lindenberg
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Esther Mena
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter A Pinto
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Deborah E Citrin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - James L Gulley
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Bradford J Wood
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - William L Dahut
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ravi A Madan
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ulas Bagci
- Department of Radiology, Northwestern University, Chicago, IL 60611, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Harmon SA, Gesztes W, Young D, Mehralivand S, McKinney Y, Sanford T, Sackett J, Cullen J, Rosner IL, Srivastava S, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Dobi A, Sesterhenn IA, Turkbey B. Prognostic Features of Biochemical Recurrence of Prostate Cancer Following Radical Prostatectomy Based on Multiparametric MRI and Immunohistochemistry Analysis of MRI-guided Biopsy Specimens. Radiology 2021; 299:613-623. [PMID: 33847515 DOI: 10.1148/radiol.2021202425] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Although prostate MRI is routinely used for the detection and staging of localized prostate cancer, imaging-based assessment and targeted molecular sampling for risk stratification are an active area of research. Purpose To evaluate features of preoperative MRI and MRI-guided biopsy immunohistochemistry (IHC) findings associated with biochemical recurrence (BCR) of prostate cancer after surgery. Materials and Methods In this retrospective case-control study, patients underwent multiparametric MRI before MRI-guided biopsy followed by radical prostatectomy between 2008 and 2016. Lesions were retrospectively scored with the Prostate Imaging Reporting and Data System (PI-RADS) (version 2) by radiologists who were blinded to the clinical-pathologic results. The IHC staining, including stains for the ETS-related gene, phosphatase and tensin homolog, androgen receptor, prostate specific antigen, and p53, was performed with targeted biopsy specimens of the index lesion (highest suspicion at MRI and pathologic grade) and scored by pathologists who were blinded to clinical-pathologic outcomes. Cox proportional hazards regression analysis was used to evaluate associations with recurrence-free survival (RFS). Results The median RFS was 31.7 months (range, 1-101 months) for 39 patients (median age, 62 years; age range, 47-76 years) without BCR and 14.6 months (range, 1-61 months) for 40 patients (median age, 59 years; age range, 47-73 years) with BCR. MRI features that showed a significant relationship with the RFS interval included an index lesion with a PI-RADS score of 5 (hazard ratio [HR], 2.10; 95% CI: 1.05, 4.21; P = .04); index lesion burden, defined as ratio of index lesion volume to prostate volume (HR, 1.55; 95% CI: 1.2, 2.1; P = .003); and suspicion of extraprostatic extension (EPE) (HR, 2.18; 95% CI: 1.1, 4.2; P = .02). Presurgical multivariable analysis indicated that suspicion of EPE at MRI (adjusted HR, 2.19; 95% CI: 1.1, 4.3; P = .02) and p53 stain intensity (adjusted HR, 2.22; 95% CI: 1.0, 4.7; P = .04) were significantly associated with RFS. Conclusion MRI features, including Prostate Imaging Reporting and Data System score, index lesion burden, extraprostatic extension, and preoperative guided biopsy p53 immunohistochemistry stain intensity are associated with biochemical relapse of prostate cancer after surgery. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Costa in this issue.
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Affiliation(s)
- Stephanie A Harmon
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - William Gesztes
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Denise Young
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Sherif Mehralivand
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Yolanda McKinney
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Thomas Sanford
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Jonathan Sackett
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Jennifer Cullen
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Inger L Rosner
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Shiv Srivastava
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Maria J Merino
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Bradford J Wood
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Peter A Pinto
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Peter L Choyke
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Albert Dobi
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Isabell A Sesterhenn
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
| | - Baris Turkbey
- From the Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute (S.A.H.); Molecular Imaging Branch (S.A.H., S.M., Y.M., T.S., J.S., P.L.C., B.T.), Laboratory of Pathology (M.J.M.), Center for Interventional Oncology (B.J.W.), and Urologic Oncology Branch (S.M., P.A.P.), National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, Room B3B85, Bethesda, Md 20892; Center for Prostate Disease Research, John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University of the Health Sciences (W.G., D.Y., J.C., I.L.R., S.S., A.D., I.A.S.) and Urology Service (I.L.R.), Walter Reed National Military Medical Center, Bethesda, Md; and Department of Genitourinary Pathology, Joint Pathology Center, Silver Spring, Md (I.A.S.)
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Wilkinson S, Ye H, Karzai F, Harmon SA, Terrigino NT, VanderWeele DJ, Bright JR, Atway R, Trostel SY, Carrabba NV, Whitlock NC, Walker SM, Lis RT, Abdul Sater H, Capaldo BJ, Madan RA, Gulley JL, Chun G, Merino MJ, Pinto PA, Salles DC, Kaur HB, Lotan TL, Venzon DJ, Choyke PL, Turkbey B, Dahut WL, Sowalsky AG. Nascent Prostate Cancer Heterogeneity Drives Evolution and Resistance to Intense Hormonal Therapy. Eur Urol 2021; 80:746-757. [PMID: 33785256 DOI: 10.1016/j.eururo.2021.03.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Patients diagnosed with high risk localized prostate cancer have variable outcomes following surgery. Trials of intense neoadjuvant androgen deprivation therapy (NADT) have shown lower rates of recurrence among patients with minimal residual disease after treatment. The molecular features that distinguish exceptional responders from poor responders are not known. OBJECTIVE To identify genomic and histologic features associated with treatment resistance at baseline. DESIGN, SETTING, AND PARTICIPANTS Targeted biopsies were obtained from 37 men with intermediate- to high-risk prostate cancer before receiving 6 mo of ADT plus enzalutamide. Biopsy tissues were used for whole-exome sequencing and immunohistochemistry (IHC). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS We assessed the relationship of molecular features with final pathologic response using a cutpoint of 0.05 cm3 for residual cancer burden to compare exceptional responders to incomplete and nonresponders. We assessed intratumoral heterogeneity at the tissue and genomic level, and compared the volume of residual disease to the Shannon diversity index for each tumor. We generated multivariate models of resistance based on three molecular features and one histologic feature, with and without multiparametric magnetic resonance imaging estimates of baseline tumor volume. RESULTS AND LIMITATIONS Loss of chromosome 10q (containing PTEN) and alterations to TP53 were predictive of poor response, as were the expression of nuclear ERG on IHC and the presence of intraductal carcinoma of the prostate. Patients with incompletely and nonresponding tumors harbored greater tumor diversity as estimated via phylogenetic tree reconstruction from DNA sequencing and analysis of IHC staining. Our four-factor binary model (area under the receiver operating characteristic curve [AUC] 0.89) to predict poor response correlated with greater diversity in our cohort and a validation cohort of 57 Gleason score 8-10 prostate cancers from The Cancer Genome Atlas. When baseline tumor volume was added to the model, it distinguished poor response to NADT with an AUC of 0.98. Prospective use of this model requires further retrospective validation with biopsies from additional trials. CONCLUSIONS A subset of prostate cancers exhibit greater histologic and genomic diversity at the time of diagnosis, and these localized tumors have greater fitness to resist therapy. PATIENT SUMMARY Some prostate cancer tumors do not respond well to a hormonal treatment called androgen deprivation therapy (ADT). We used tumor volume and four other parameters to develop a model to identify tumors that will not respond well to ADT. Treatments other than ADT should be considered for these patients.
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Affiliation(s)
- Scott Wilkinson
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Huihui Ye
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Pathology and Department of Urology, University of California-Los Angeles, Los Angeles, CA, USA
| | - Fatima Karzai
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA; Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Nicholas T Terrigino
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - David J VanderWeele
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA; Department of Medicine, Feinberg School of Medicine, Chicago, IL, USA
| | - John R Bright
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Rayann Atway
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Shana Y Trostel
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Nicole V Carrabba
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Nichelle C Whitlock
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | | | - Rosina T Lis
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | | | - Brian J Capaldo
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - James L Gulley
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Guinevere Chun
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, Bethesda, MD, USA
| | - Daniela C Salles
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Harsimar B Kaur
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Tamara L Lotan
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - David J Venzon
- Biostatistics and Data Management Section, National Cancer Institute, Rockville, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD, USA
| | - Adam G Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, Bethesda, MD, USA.
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Sackett J, Shih JH, Reese SE, Brender JR, Harmon SA, Barrett T, Coskun M, Madariaga M, Marko J, Law YM, Turkbey EB, Mehralivand S, Sanford T, Lay N, Pinto PA, Wood BJ, Choyke PL, Turkbey B. Quality of Prostate MRI: Is the PI-RADS Standard Sufficient? Acad Radiol 2021; 28:199-207. [PMID: 32143993 DOI: 10.1016/j.acra.2020.01.031] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 12/22/2022]
Abstract
RATIONALE AND OBJECTIVE The Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) published a set of minimum technical standards (MTS) to improve image quality and reduce variability in multiparametric prostate MRI. The effect of PIRADSv2 MTS on image quality has not been validated. We aimed to determine whether adherence to PI-RADSv2 MTS improves study adequacy and perceived quality. MATERIALS AND METHODS Sixty-two prostate MRI examinations including T2 weighted (T2W) and diffusion weighted image (DWI) consecutively referred to our center from 62 different institutions within a 12-month period (September 2017 to September 2018) were included. Six readers assessed images as adequate or inadequate for use in PCa detection and a numerical image quality ranking was given using a 1-5 scale. The PI-RADSv2 MTS were synthesized into sets of seven and 10 rules for T2W and DWI, respectively. Image adherence was assessed using Digital Imaging and Communications in Medicine (DICOM) metadata. Statistical analysis of survey results and image adherence was performed based on reader quality scoring (Kendall Rank tau-b) and reader adequate scoring (Wilcoxon test for association) for T2 and DWI quality assessment. RESULTS Out of 62 images, 52 (83%) T2W and 38 (61%) DWIs were rated to be adequate by a majority of readers. Reader adequacy scores showed no significant association with adherence to PI-RADSv2. There was a weak (tau-b = 0.22) but significant (p value = 0.01) correlation between adherence to PIRADSv2 MTS and image quality for T2W. Studies following all PI-RADSv2 T2W rules achieved a higher median average quality score (3.58 for 7/7 vs. 3.0 for <7/7, p = 0.012). No statistical relationship with PI-RADSv2 MTS adherence and DWI quality was found. CONCLUSION Among 62 sites performing prostate MRI, few were considered of high quality, but the majority were considered adequate. DWI showed considerably lower rates of adequate studies in the sample. Adherence to PI-RADSv2 MTS did not increase the likelihood of having a qualitatively adequate T2W or DWI.
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Affiliation(s)
- Jonathan Sackett
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA; Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Sarah E Reese
- General Dynamics Information Technology, Falls Church, VA, USA
| | - Jeffrey R Brender
- Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephanie A Harmon
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA; Leidos Biomedical Research, Inc., NCI Campus at Frederick, Clinical Research Directorate/Clinical Monitoring Research Program, Bethesda, MD, USA
| | - Tristan Barrett
- University of Cambridge School of Clinical Medicine, Cambridge UK
| | - Mehmet Coskun
- Department of Radiology, Dr. Behcet Uz Child Disease and Pediatric Surgery Training and Research Hospital, University of Health Sciences, izmir, Turkey
| | | | - Jamie Marko
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Yan Mee Law
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Evrim B Turkbey
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Thomas Sanford
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Nathan Lay
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Bradford J Wood
- Department of Radiology, Clinical Center, NIH, Bethesda, MD, USA; Center for Interventional Oncology, National Cancer Institute, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA.
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Masoudi S, Harmon SA, Mehralivand S, Walker SM, Raviprakash H, Bagci U, Choyke PL, Turkbey B. Quick guide on radiology image pre-processing for deep learning applications in prostate cancer research. J Med Imaging (Bellingham) 2021; 8:010901. [PMID: 33426151 PMCID: PMC7790158 DOI: 10.1117/1.jmi.8.1.010901] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 12/04/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose: Deep learning has achieved major breakthroughs during the past decade in almost every field. There are plenty of publicly available algorithms, each designed to address a different task of computer vision in general. However, most of these algorithms cannot be directly applied to images in the medical domain. Herein, we are focused on the required preprocessing steps that should be applied to medical images prior to deep neural networks. Approach: To be able to employ the publicly available algorithms for clinical purposes, we must make a meaningful pixel/voxel representation from medical images which facilitates the learning process. Based on the ultimate goal expected from an algorithm (classification, detection, or segmentation), one may infer the required pre-processing steps that can ideally improve the performance of that algorithm. Required pre-processing steps for computed tomography (CT) and magnetic resonance (MR) images in their correct order are discussed in detail. We further supported our discussion by relevant experiments to investigate the efficiency of the listed preprocessing steps. Results: Our experiments confirmed how using appropriate image pre-processing in the right order can improve the performance of deep neural networks in terms of better classification and segmentation. Conclusions: This work investigates the appropriate pre-processing steps for CT and MR images of prostate cancer patients, supported by several experiments that can be useful for educating those new to the field (https://github.com/NIH-MIP/Radiology_Image_Preprocessing_for_Deep_Learning).
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Affiliation(s)
- Samira Masoudi
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Stephanie A. Harmon
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Sherif Mehralivand
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Stephanie M. Walker
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Harish Raviprakash
- National Institutes of Health, Department of Radiology and Imaging Sciences, Bethesda, Maryland, United States
| | - Ulas Bagci
- University of Central Florida, Orlando, Florida, United States
| | - Peter L. Choyke
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
| | - Baris Turkbey
- National Cancer Institute, National Institutes of Health, Molecular Imaging Branch, Bethesda, Maryland, United States
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34
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O'Connor LP, Wang AZ, Yerram NK, Long L, Ahdoot M, Lebastchi AH, Gurram S, Zeng J, Harmon SA, Mehralivand S, Merino MJ, Parnes HL, Choyke PL, Shih JH, Wood BJ, Turkbey B, Pinto PA. Changes in Magnetic Resonance Imaging Using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation Criteria to Detect Prostate Cancer Progression for Men on Active Surveillance. Eur Urol Oncol 2020; 4:227-234. [PMID: 33867045 DOI: 10.1016/j.euo.2020.09.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 09/07/2020] [Accepted: 09/17/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND The ability of serial magnetic resonance imaging (MRI) to capture pathologic progression during active surveillance (AS) remains in question. OBJECTIVE To determine whether changes in MRI are associated with pathologic progression for patients on AS. DESIGN, SETTING, AND PARTICIPANTS From July 2007 through January 2020, we identified all patients evaluated for AS at our institution. Following confirmatory biopsy, a total of 391 patients who underwent surveillance MRI and biopsy at least once were identified (median follow-up of 35.6 mo, interquartile range 19.7-60.6). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS All MRI intervals were scored using the "Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation" (PRECISE) criteria, with PRECISE scores =4 considered a positive change in MRI. A generalized estimating equation-based logistic regression analysis was conducted for all intervals with a PRECISE score of <4 to determine the predictors of Gleason grade group (GG) progression despite stable MRI. RESULTS AND LIMITATIONS A total of 621 MRI intervals were scored by PRECISE and validated by biopsy. The negative predictive value of stable MRI (PRECISE score <4) was greatest for detecting GG1 to?=?GG3 disease (0.94 [0.91-0.97]). If 2-yr surveillance biopsy were performed exclusively for a positive change in MRI, 3.7% (4/109) of avoided biopsies would have resulted in missed progression from GG1 to?=?GG3 disease. Prostate-specific antigen (PSA) density (odds ratio 1.95 [1.17-3.25], p?=? 0.01) was a risk factor for progression from GG1 to =GG3 disease despite stable MRI. CONCLUSIONS In patients with GG1 disease and stable MRI (PRECISE score <4) on surveillance, grade progression to?=?GG3 disease is not common. In patients with grade progression detected on biopsy despite stable MRI, elevated PSA density appeared to be a risk factor for progression to?=?GG3 disease. PATIENT SUMMARY For patients with low-risk prostate cancer on active surveillance, the risk of progressing to grade group 3 disease is low with a stable magnetic resonance image (MRI) after 2?yr. Having higher prostate-specific antigen density increases the risk of progression, despite having a stable MRI.
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Affiliation(s)
- Luke P O'Connor
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Alex Z Wang
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nitin K Yerram
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lori Long
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael Ahdoot
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amir H Lebastchi
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Johnathan Zeng
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Harmon
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Howard L Parnes
- Division of Cancer Prevention, National Cancer Institutes, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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Karzai F, Walker SM, Wilkinson S, Madan RA, Shih JH, Merino MJ, Harmon SA, VanderWeele DJ, Cordes LM, Carrabba NV, Bright JR, Terrigino NT, Chun G, Bilusic M, Couvillon A, Hankin A, Williams MN, Lis RT, Ye H, Choyke PL, Gulley JL, Sowalsky AG, Turkbey B, Pinto PA, Dahut WL. Sequential Prostate Magnetic Resonance Imaging in Newly Diagnosed High-risk Prostate Cancer Treated with Neoadjuvant Enzalutamide is Predictive of Therapeutic Response. Clin Cancer Res 2020; 27:429-437. [PMID: 33023952 DOI: 10.1158/1078-0432.ccr-20-2344] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/26/2020] [Accepted: 10/01/2020] [Indexed: 11/16/2022]
Abstract
PURPOSE For high-risk prostate cancer, standard treatment options include radical prostatectomy (RP) or radiotherapy plus androgen deprivation therapy (ADT). Despite definitive therapy, many patients will have disease recurrence. Imaging has the potential to better define characteristics of response and resistance. In this study, we evaluated prostate multiparametric MRI (mpMRI) before and after neoadjuvant enzalutamide plus ADT. PATIENTS AND METHODS Men with localized intermediate- or high-risk prostate cancer underwent a baseline mpMRI and mpMRI-targeted biopsy followed by a second mpMRI after 6 months of enzalutamide and ADT prior to RP. Specimens were sectioned in the same plane as mpMRI using patient-specific 3D-printed molds to permit mpMRI-targeted biopsies to be compared with the same lesion from the RP. Specimens were analyzed for imaging and histologic correlates of response. RESULTS Of 39 patients enrolled, 36 completed imaging and RP. Most patients (92%) had high-risk disease. Fifty-eight lesions were detected on baseline mpMRI, of which 40 (69%) remained measurable at 6-month follow-up imaging. Fifty-five of 59 lesions (93%) demonstrated >50% volume reduction on posttreatment mpMRI. Three of 59 lesions (5%) demonstrated growth in size at follow-up imaging, with two lesions increasing more than 3-fold in volume. On whole-mount pathology, 15 patients demonstrated minimal residual disease (MRD) of <0.05 cc or pathologic complete response. Low initial mpMRI relative tumor burden was most predictive of MRD on final pathology. CONCLUSIONS Low relative lesion volume at baseline mpMRI was predictive of pathologic response. A subset of patients had limited response. Selection of patients based on these metrics may improve outcomes in high-risk disease.
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Affiliation(s)
- Fatima Karzai
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | | | - Scott Wilkinson
- Laboratory for Genitourinary Cancer Pathogenesis, NCI, NIH, Bethesda, Maryland
| | - Ravi A Madan
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis, Biometric Research Program, NCI, NIH, Rockville, Maryland
| | | | - Stephanie A Harmon
- Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., NCI Campus at Frederick, Frederick, Maryland
| | - David J VanderWeele
- Laboratory for Genitourinary Cancer Pathogenesis, NCI, NIH, Bethesda, Maryland
| | - Lisa M Cordes
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Nicole V Carrabba
- Laboratory for Genitourinary Cancer Pathogenesis, NCI, NIH, Bethesda, Maryland
| | - John R Bright
- Laboratory for Genitourinary Cancer Pathogenesis, NCI, NIH, Bethesda, Maryland
| | - Nicolas T Terrigino
- Laboratory for Genitourinary Cancer Pathogenesis, NCI, NIH, Bethesda, Maryland
| | - Guinevere Chun
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Marijo Bilusic
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Anna Couvillon
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Amy Hankin
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Monique N Williams
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Rosina T Lis
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Huihui Ye
- Department of Pathology, Ronald Reagan UCLA Medical Center, Los Angeles, California
| | | | - James L Gulley
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Adam G Sowalsky
- Laboratory for Genitourinary Cancer Pathogenesis, NCI, NIH, Bethesda, Maryland
| | - Baris Turkbey
- Molecular Imaging Program, NCI, NIH, Bethesda, Maryland
| | - Peter A Pinto
- Urologic Oncology Branch, NCI, NIH, Bethesda, Maryland
| | - William L Dahut
- Genitourinary Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
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Tuncer S, Mehralivand S, Harmon SA, Sanford T, Brown GT, Rowe LS, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Turkbey B. Apical periurethral transition zone lesions: MRI and histology findings. Abdom Radiol (NY) 2020; 45:3258-3264. [PMID: 31468153 DOI: 10.1007/s00261-019-02194-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE Apical periurethral transition zone (TZ) cancers can pose unique problems for surgery and radiation therapy. Here, we describe the appearance of such cancers on multiparametric MRI (mpMRI) and correlate this with histopathology derived from MRI-targeted biopsy. MATERIALS AND METHODS Between May 2011 and January 2019, a total of 4381 consecutive patients underwent 3 T mpMRI. Of these, 53 patients with 58 apical periurethral TZ lesions underwent TRUS/MRI fusion-guided biopsy and 12-core systematic TRUS-guided biopsy. Correlation was made with patient age, PSA, PSA density, whole prostate volume, and Gleason scores. RESULTS A total 53 men (median age 68 years, median PSA 7.94 ng/ml) were identified as having at least one apical periurethral TZ lesion on mpMRI and 5 (9%) patients had more than one apical periurethral lesion. Thus, 58 lesions were identified in 53 patients. Of these 37/53 patients (69%) and 40/58 lesions were positive at biopsy for prostate cancer. Seven were diagnosed by 12-core systematic TRUS-guided biopsy and 34 were diagnosed by TRUS/MRI fusion-guided biopsy. Gleason score was ≥ 3 + 4 in 34/58 (58%) lesions. CONCLUSION Identification of apical periurethral TZ prostate cancers is important to help guide surgical and radiation therapy as these tumors are adjacent to critical structures. Because of the tendency to undersample the periurethral zone during TRUS biopsy, MRI-guided biopsy is particularly helpful for detecting apical periurethral TZ prostate cancers many of which prove to be clinically significant.
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Affiliation(s)
- Sena Tuncer
- Department of Radiology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Sherif Mehralivand
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Stephanie A Harmon
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, NCI, NIH, Bethesda, MD, USA
| | - Thomas Sanford
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - G Thomas Brown
- Cognitive Science Branch, National Library of Medicine, Bethesda, MD, USA
| | | | | | - Bradford J Wood
- Center for Interventional Oncology, NCI and Radiology and Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Baris Turkbey
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA.
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Harmon SA, Sanford TH, Xu S, Turkbey EB, Roth H, Xu Z, Yang D, Myronenko A, Anderson V, Amalou A, Blain M, Kassin M, Long D, Varble N, Walker SM, Bagci U, Ierardi AM, Stellato E, Plensich GG, Franceschelli G, Girlando C, Irmici G, Labella D, Hammoud D, Malayeri A, Jones E, Summers RM, Choyke PL, Xu D, Flores M, Tamura K, Obinata H, Mori H, Patella F, Cariati M, Carrafiello G, An P, Wood BJ, Turkbey B. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun 2020; 11:4080. [PMID: 32796848 PMCID: PMC7429815 DOI: 10.1038/s41467-020-17971-2] [Citation(s) in RCA: 254] [Impact Index Per Article: 63.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/13/2020] [Indexed: 02/06/2023] Open
Abstract
Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.
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Affiliation(s)
- Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Thomas H Sanford
- State University of New York-Upstate Medical Center, Syracuse, NY, USA
| | - Sheng Xu
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Evrim B Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | | | - Ziyue Xu
- NVIDIA Corporation, Bethesda, MD, USA
| | - Dong Yang
- NVIDIA Corporation, Bethesda, MD, USA
| | | | - Victoria Anderson
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Amel Amalou
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Maxime Blain
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Michael Kassin
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Dilara Long
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Nicole Varble
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
- Philips Research North America, Cambridge, MA, USA
| | - Stephanie M Walker
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ulas Bagci
- Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA
| | - Anna Maria Ierardi
- Department of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico Milano, Milan, Italy
| | - Elvira Stellato
- Department of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico Milano, Milan, Italy
| | - Guido Giovanni Plensich
- Department of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico Milano, Milan, Italy
| | - Giuseppe Franceschelli
- Diagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, San Paolo Hospital, Milan, Italy
| | - Cristiano Girlando
- Postgraduation School in Radiodiagnostics, Università Degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università Degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Dominic Labella
- State University of New York-Upstate Medical Center, Syracuse, NY, USA
| | - Dima Hammoud
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Ashkan Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Ronald M Summers
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | - Kaku Tamura
- Self-Defense Forces Central Hospital, Tokyo, Japan
| | | | - Hitoshi Mori
- Self-Defense Forces Central Hospital, Tokyo, Japan
| | - Francesca Patella
- Diagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, San Paolo Hospital, Milan, Italy
| | - Maurizio Cariati
- Diagnostic and Interventional Radiology Service, ASST Santi Paolo e Carlo, San Paolo Hospital, Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Radiology Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico Milano, Milan, Italy
- Department of Health Sciences, University of Milano, Milan, Italy
| | - Peng An
- Department of Radiology, Xiangyang NO.1 People's Hospital Affiliated to Hubei University of Medicine Xiangyang, Hubei, China
| | - Bradford J Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, NIH Clinical Center and National Cancer Institute, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA.
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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Lindenberg L, Mena E, Turkbey B, Shih JH, Reese SE, Harmon SA, Lim I, Lin F, Ton A, McKinney YL, Eclarinal P, Citrin DE, Dahut W, Madan R, Wood BJ, Krishnasamy V, Chang R, Levy E, Pinto P, Eary JF, Choyke PL. Evaluating Biochemically Recurrent Prostate Cancer: Histologic Validation of 18F-DCFPyL PET/CT with Comparison to Multiparametric MRI. Radiology 2020; 296:564-572. [PMID: 32633674 DOI: 10.1148/radiol.2020192018] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background Prostate cancer recurrence is found in up to 40% of men with prior definitive (total prostatectomy or whole-prostate radiation) treatment. Prostate-specific membrane antigen PET agents such as 2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine 3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid (18F-DCFPyL) may improve detection of recurrence compared with multiparametric MRI; however, histopathologic validation is lacking. Purpose To determine the sensitivity, specificity, and positive predictive value (PPV) of 18F-DCFPyL PET/CT based on histologic analysis and to compare with pelvic multiparametric MRI in men with biochemically recurrent prostate cancer. Materials and Methods Men were prospectively recruited after prostatectomy and/or radiation therapy with rising prostate-specific antigen level (median, 2.27 ng/mL; range, 0.2-27.45 ng/mL) and a negative result at conventional imaging (bone scan and/or CT). Participants underwent 18F-DCFPyL PET/CT imaging and 3.0-T pelvic multiparametric MRI. Statistical analysis included Wald and modified χ2 tests. Results A total of 323 lesions were visualized in 77 men by using 18F-DCFPyL or multiparametric MRI, with imaging detection concordance of 25% (82 of 323) when including all lesions in the MRI field of view and 53% (52 of 99) when only assessing prostate bed lesions. 18F-DCFPyL depicted more pelvic lymph nodes than did MRI (128 vs 23 nodes). Histologic validation was obtained in 80 locations with sensitivity, specificity, and PPV of 69% (25 of 36; 95% confidence interval [CI]: 51%, 88%), 91% (40 of 44; 95% CI: 74%, 98%), and 86% (25 of 29; 95% CI: 73%, 97%) for 18F-DCFPyL and 69% (24 of 35; 95% CI: 50%, 86%), 74% (31 of 42; 95% CI: 42%, 89%), and 69% (24 of 35; 95% CI: 50%, 88%) for multiparametric MRI (P = .95, P = .14, and P = .07, respectively). In the prostate bed, sensitivity, specificity, and PPV were 57% (13 of 23; 95% CI: 32%, 81%), 86% (18 of 21; 95% CI: 73%, 100%), and 81% (13 of 16; 95% CI: 59%, 100%) for 18F-DCFPyL and 83% (19 of 23; 95% CI: 59%, 100%), 52% (11 of 21; 95% CI: 29%, 74%), and 66% (19 of 29; 95% CI: 44%, 86%) for multiparametric MRI (P = .19, P = .02, and P = .17, respectively). The addition of 18F-DCFPyL to multiparametric MRI improved PPV by 38% overall (P = .02) and by 30% (P = .09) in the prostate bed. Conclusion Findings with 2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine 3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid (18F-DCFPyL) were histologically validated and demonstrated high specificity and positive predictive value. In the pelvis, 18F-DCFPyL depicted more lymph nodes and improved positive predictive value and specificity when added to multiparametric MRI. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zukotynski and Rowe in this issue.
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Affiliation(s)
- Liza Lindenberg
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Esther Mena
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Baris Turkbey
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Joanna H Shih
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Sarah E Reese
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Stephanie A Harmon
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Ilhan Lim
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Frank Lin
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Anita Ton
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Yolanda L McKinney
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Philip Eclarinal
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Deborah E Citrin
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - William Dahut
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Ravi Madan
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Bradford J Wood
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Venkatesh Krishnasamy
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Richard Chang
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Elliot Levy
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Peter Pinto
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Janet F Eary
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
| | - Peter L Choyke
- From the Molecular Imaging Program, National Cancer Institute, Building 10, Room B3B47A, Bethesda, MD 20892 (L.L., E.M., B.T., I.L., F.L., A.T., Y.L.M., P.E., P.L.C.); Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.H.S.); National Cancer Institute Biometrics Research Program Contract, General Dynamics Information Technology, Falls Church, Va (S.E.R.); Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, Bethesda, Md (S.A.H.); Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Md (D.E.C.); Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (W.D., R.M.); Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.J.W., V.K., R.C., E.L.); Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (P.P.); and Cancer Imaging Program, National Cancer Institute, Bethesda, Md (J.F.E.)
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Masoudi S, Anwar SM, Harmon SA, Choyke PL, Turkbey B, Bagci U. Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:1624-1628. [PMID: 33018306 PMCID: PMC8972795 DOI: 10.1109/embc44109.2020.9176009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Abdominal fat quantification is critical since multiple vital organs are located within this region. Although computed tomography (CT) is a highly sensitive modality to segment body fat, it involves ionizing radiations which makes magnetic resonance imaging (MRI) a preferable alternative for this purpose. Additionally, the superior soft tissue contrast in MRI could lead to more accurate results. Yet, it is highly labor intensive to segment fat in MRI scans. In this study, we propose an algorithm based on deep learning technique(s) to automatically quantify fat tissue from MR images through a cross modality adaptation. Our method does not require supervised labeling of MR scans, instead, we utilize a cycle generative adversarial network (C-GAN) to construct a pipeline that transforms the existing MR scans into their equivalent synthetic CT (s-CT) images where fat segmentation is relatively easier due to the descriptive nature of HU (hounsfield unit) in CT images. The fat segmentation results for MRI scans were evaluated by expert radiologist. Qualitative evaluation of our segmentation results shows average success score of 3.80/5 and 4.54/5 for visceral and subcutaneous fat segmentation in MR images*.
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Sanford T, Harmon SA, Turkbey EB, Kesani D, Tuncer S, Madariaga M, Yang C, Sackett J, Mehralivand S, Yan P, Xu S, Wood BJ, Merino MJ, Pinto PA, Choyke PL, Turkbey B. Deep-Learning-Based Artificial Intelligence for PI-RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study. J Magn Reson Imaging 2020; 52:1499-1507. [PMID: 32478955 DOI: 10.1002/jmri.27204] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND The Prostate Imaging Reporting and Data System (PI-RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from high intra/interreader variability. PURPOSE To develop an artificial intelligence (AI) solution for PI-RADS classification and compare its performance with an expert radiologist using targeted biopsy results. STUDY TYPE Retrospective study including data from our institution and the publicly available ProstateX dataset. POPULATION In all, 687 patients who underwent mpMRI of the prostate and had one or more detectable lesions (PI-RADS score >1) according to PI-RADSv2. FIELD STRENGTH/SEQUENCE T2 -weighted, diffusion-weighted imaging (DWI; five evenly spaced b values between b = 0-750 s/mm2 ) for apparent diffusion coefficient (ADC) mapping, high b-value DWI (b = 1500 or 2000 s/mm2 ), and dynamic contrast-enhanced T1 -weighted series were obtained at 3.0T. ASSESSMENT PI-RADS lesions were segmented by a radiologist. Bounding boxes around the T2 /ADC/high-b value segmentations were stacked and saved as JPEGs. These images were used to train a convolutional neural network (CNN). The PI-RADS scores obtained by the CNN were compared with radiologist scores. The cancer detection rate was measured from a subset of patients who underwent biopsy. STATISTICAL TESTS Agreement between the AI and the radiologist-driven PI-RADS scores was assessed using a kappa score, and differences between categorical variables were assessed with a Wald test. RESULTS For the 1034 detection lesions, the kappa score for the AI system vs. the expert radiologist was moderate, at 0.40. However, there was no significant difference in the rates of detection of clinically significant cancer for any PI-RADS score in 86 patients undergoing targeted biopsy (P = 0.4-0.6). DATA CONCLUSION We developed an AI system for assignment of a PI-RADS score on segmented lesions on mpMRI with moderate agreement with an expert radiologist and a similar ability to detect clinically significant cancer. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Thomas Sanford
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stephanie A Harmon
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland, USA
| | - Evrim B Turkbey
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Deepak Kesani
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sena Tuncer
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Manuel Madariaga
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Chris Yang
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Jonathan Sackett
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Pingkun Yan
- Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Sheng Xu
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA.,Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Bradford J Wood
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA.,Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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41
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Wilkinson S, Harmon SA, Terrigino NT, Karzai F, Pinto PA, Madan RA, VanderWeele DJ, Lake R, Atway R, Bright JR, Carrabba NV, Trostel SY, Lis RT, Chun G, Gulley JL, Merino MJ, Choyke PL, Ye H, Dahut WL, Turkbey B, Sowalsky AG. A case report of multiple primary prostate tumors with differential drug sensitivity. Nat Commun 2020; 11:837. [PMID: 32054861 PMCID: PMC7018822 DOI: 10.1038/s41467-020-14657-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 01/23/2020] [Indexed: 12/04/2022] Open
Abstract
Localized prostate cancers are genetically variable and frequently multifocal, comprising spatially distinct regions with multiple independently-evolving clones. To date there is no understanding of whether this variability can influence management decisions for patients with prostate tumors. Here, we present a single case from a clinical trial of neoadjuvant intense androgen deprivation therapy. A patient was diagnosed with a large semi-contiguous tumor by imaging, histologically composed of a large Gleason score 9 tumor with an adjacent Gleason score 7 nodule. DNA sequencing demonstrates these are two independent tumors, as only the Gleason 9 tumor harbors single-copy losses of PTEN and TP53. The PTEN/TP53-deficient tumor demonstrates treatment resistance, selecting for subclones with mutations to the remaining copies of PTEN and TP53, while the Gleason 7 PTEN-intact tumor is almost entirely ablated. These findings indicate that spatiogenetic variability is a major confounder for personalized treatment of patients with prostate cancer.
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Affiliation(s)
- Scott Wilkinson
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Stephanie A Harmon
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, 8560 Progress Drive, Frederick, MD, 21701, USA
| | - Nicholas T Terrigino
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Fatima Karzai
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - David J VanderWeele
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
- Department of Medicine, Feinberg School of Medicine, 420 E. Superior Street, Chicago, IL, 60611, USA
| | - Ross Lake
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Rayann Atway
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - John R Bright
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Nicole V Carrabba
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Shana Y Trostel
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Rosina T Lis
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Guinevere Chun
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - James L Gulley
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Huihui Ye
- Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA
- Department of Pathology and Department of Urology, University of California Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA, 90095, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Adam G Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA.
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Harmon SA, Brown GT, Sanford T, Mehralivand S, Shih JH, Xu S, Merino MJ, Choyke PL, Pinto PA, Wood BJ, McKenney JK, Turkbey B. Spatial density and diversity of architectural histology in prostate cancer: influence on diffusion weighted magnetic resonance imaging. Quant Imaging Med Surg 2020; 10:326-339. [PMID: 32190560 DOI: 10.21037/qims.2020.01.06] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To assess the influence of specific histopathologic patterns on MRI diffusion characteristics by performing rigorous whole-mount/imaging registration and correlating histologic architectures observed in prostate cancer with diffusion characteristics in prostate MRIs. Methods Fifty-two whole-mount pathology blocks from 15 patients who underwent multiparametric MRI (mpMRI) at a single institution prior to radical prostatectomy were retrospectively analyzed. Regions containing individual morphologic patterns (N=21 patterns, including variations of cribriforming, expansile sheets, single cells, patterns of early intraluminal complexity, and mucin rupture patterns) were digitally annotated by an expert genitourinary pathologist. Distinct tumor foci on each slide were also assigned a Gleason grade and scored as having any high-risk histologic pattern. Digital sections were aligned to MRI using a patient-specific mold and registered using local mean weighted piecewise transformation based on anatomic control points. Density and presence of morphological patterns was correlated to apparent diffusion coefficient (ADC) signal intensity using mixed effects model accounting for nested intra-foci, intra-patient correlation. Influence of intra-tumoral heterogeneity was assessed by affinity propagation clustering (APC) of morphology features and correlated to foci- and cluster-level ADC metrics. Results One hundred eleven distinct tumor foci were evaluated. Beta diversity, reflecting average morphology representation across inter- and intra-foci areas, demonstrated higher intra-tumor diversity within high-risk foci (P<0.05). ADC signal demonstrated an inverse correlation with foci-level Gleason grade (P>0.05), which was strengthened in cluster-level analysis for intra-foci regions containing high-risk morphologies (P=0.017). In voxel-based analysis, dense regions demonstrate lower ADC, but the presence and density for each morphology influenced ADC independently (ANOVA P<0.001). Conclusions Architectural features influence ADC characteristics of MRI, with more complex tumors having lower ADC values regulated by presence and density of specific morphologies.
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Affiliation(s)
- Stephanie A Harmon
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Bethesda, MD, USA.,Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - G Thomas Brown
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Bethesda, MD, USA.,National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Thomas Sanford
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sheng Xu
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jesse K McKenney
- Department of Anatomic Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Mena E, Lindenberg ML, Turkbey IB, Shih JH, Harmon SA, Lim I, Lin F, Adler S, Eclarinal P, McKinney YL, Citrin D, Dahut W, Wood BJ, Krishnasamy V, Chang R, Levy E, Merino M, Pinto P, Eary JF, Choyke PL. 18F-DCFPyL PET/CT Imaging in Patients with Biochemically Recurrent Prostate Cancer After Primary Local Therapy. J Nucl Med 2019; 61:881-889. [PMID: 31676732 DOI: 10.2967/jnumed.119.234799] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 10/07/2019] [Indexed: 11/16/2022] Open
Abstract
Our objective was to investigate the lesion detection rate of 18F-DCFPyL PET/CT, a prostate-specific membrane antigen (PSMA)-targeted PET agent, in patients with biochemically relapsed prostate cancer after primary local therapy. Methods: This was a prospective institutional review board-approved study of 90 patients with documented biochemical recurrence (median prostate-specific antigen [PSA], 2.5 ng/mL; range, 0.21-35.5 ng/mL) and negative results on conventional imaging after primary local therapies, including radical prostatectomy (n = 38), radiation (n = 27), or a combination of the two (n = 25). Patients on androgen deprivation therapy were excluded. Patients underwent whole-body 18F-DCFPyL PET/CT (299.9 ± 15.5 MBq) at 2 h after injection. The PSMA PET lesion detection rate was correlated with PSA, PSA kinetics, and original primary tumor grade. Results: Seventy patients (77.8%) showed positive PSMA PET results, with a total of 287 lesions identified: 37 prostate bed foci, 208 lesions in lymph nodes, and 42 in distant sites in bones or organs, Eleven patients had negative results, and 9 patients showed indeterminate lesions, which were considered negative in this study. The detection rates were 47.6% (n = 10/21), 50% (n = 5/10), 88.9% (n = 8/9), and 94% (n = 47/50) for PSA levels of >0.2 to <0.5, 0.5 to <1.0, 1 to <2.0, and ≥2.0 ng/mL, respectively. In postsurgical patients, PSA, PSA doubling time, and PSA velocity correlated with PET results, but the same was not true for postradiation patients. These parameters also correlated with the extent of disease on PET (intrapelvic vs. extrapelvic). There was no significant difference in the rate of positive scans between patients with higher-grade and lower-grade primary tumors (Gleason score of ≥4 + 3 vs. <3 + 4). Tumor recurrence was histology-confirmed in 40% (28/70) of patients. On a per-patient basis, positive predictive value was 93.3% (95% confidence interval, 77.6%-99.2%) by histopathologic validation and 96.2% (95% confidence interval, 86.3%-99.7%) by the combination of histology and imaging/clinical follow-up. Conclusion: 18F-DCFPyL PET/CT imaging offers high detection rates in biochemically recurrent prostate cancer patients and is positive in about 50% of patients with a PSA level of less than 0.5 ng/mL, which could substantially impact clinical management. In postsurgical patients, 18F-DCFPyL PET/CT correlates with PSA, PSA doubling time, and PSA velocity, suggesting it may have prognostic value. 18F-DCFPyL PET/CT is highly promising for localizing sites of recurrent prostate cancer.
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Affiliation(s)
- Esther Mena
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Maria Liza Lindenberg
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Ismail Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Joanna H Shih
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Ilhan Lim
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Frank Lin
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephen Adler
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research Sponsored by the National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Philip Eclarinal
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Yolanda L McKinney
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Deborah Citrin
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - William Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Bradford J Wood
- Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Venkatesh Krishnasamy
- Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Richard Chang
- Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Elliot Levy
- Center of Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Maria Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland; and
| | - Janet F Eary
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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Mehralivand S, Shih JH, Rais-Bahrami S, Oto A, Bednarova S, Nix JW, Thomas JV, Gordetsky JB, Gaur S, Harmon SA, Siddiqui MM, Merino MJ, Parnes HL, Wood BJ, Pinto PA, Choyke PL, Turkbey B. A Magnetic Resonance Imaging-Based Prediction Model for Prostate Biopsy Risk Stratification. JAMA Oncol 2019; 4:678-685. [PMID: 29470570 DOI: 10.1001/jamaoncol.2017.5667] [Citation(s) in RCA: 121] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Importance Multiparametric magnetic resonance imaging (MRI) in conjunction with MRI-transrectal ultrasound (TRUS) fusion-guided biopsies have improved the detection of prostate cancer. It is unclear whether MRI itself adds additional value to multivariable prediction models based on clinical parameters. Objective To determine whether an MRI-based prediction model can reduce unnecessary biopsies in patients with suspected prostate cancer. Design, Setting, and Participants Patients underwent MRI, MRI-TRUS fusion-guided biopsy, and 12-core systematic biopsy in 1 session. The development cohort used to derive the prediction model consisted of 400 patients from 1 institution enrolled between May 14, 2015, and August 31, 2016, and the validation cohort included 251 patients from 2 independent institutions who underwent biopsies between April 1, 2013, and June 30, 2016, at 1 institution and between July 1, 2015, and October 31, 2016, at the other institution. The MRI model included MRI-derived parameters in addition to clinical variables. Area under the curve of receiver operating characteristic curves and decision curve analysis were performed. Main Outcomes and Measures Risk of clinically significant prostate cancer on biopsy, defined as a Gleason score of 3 + 4 or higher in at least 1 biopsy core. Results Overall, 193 (48.3%) of the 400 patients in the development cohort (mean [SD] age at biopsy, 64.3 [7.1] years) and 96 (38.2%) of the 251 patients in the validation cohort (mean [SD] age at biopsy, 64.9 [7.2] years) had clinically significant prostate cancer, defined as a Gleason score greater than or equal to 3 + 4. By applying the model to the external validation cohort, the area under the curve increased from 64% to 84% compared with the baseline model (P < .001). At a risk threshold of 20%, the MRI model had a lower false-positive rate than the baseline model (46% [95% CI, 32%-66%] vs 92% [95% CI, 70%-100%]), with only a small reduction in the true-positive rate (89% [95% CI, 85%-96%] vs 99% [95% CI, 89%-100%]). Eighteen of 100 fewer biopsies could have been performed, with no increase in the number of patients with missed clinically significant prostate cancers. Conclusions and Relevance The inclusion of MRI-derived parameters in a risk model could reduce the number of unnecessary biopsies while maintaining a high rate of diagnosis of clinically significant prostate cancers.
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Affiliation(s)
- Sherif Mehralivand
- Department of Urology and Pediatric Urology, University Medical Center, Mainz, Germany.,Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.,Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Rockville, Maryland
| | - Soroush Rais-Bahrami
- Department of Urology, University of Alabama at Birmingham.,Department of Radiology, University of Alabama at Birmingham
| | - Aytekin Oto
- Department of Radiology, University of Chicago Medical Center, Chicago, Illinois
| | - Sandra Bednarova
- Institute of Diagnostic Radiology, Department of Medical and Biological Sciences, University of Udine, Udine, Italy.,Center for Interventional Oncology, National Cancer Institute and Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Jeffrey W Nix
- Department of Urology, University of Alabama at Birmingham
| | - John V Thomas
- Department of Radiology, University of Alabama at Birmingham
| | | | - Sonia Gaur
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc, National Cancer Institute Campus at Frederick, Frederick, Maryland
| | | | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Howard L Parnes
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute and Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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Roth AR, Harmon SA, Perk TG, Eickhoff J, Choyke PL, Kurdziel KA, Dahut WL, Apolo AB, Morris MJ, Perlman SB, Liu G, Jeraj R. Impact of Anatomic Location of Bone Metastases on Prognosis in Metastatic Castration-Resistant Prostate Cancer. Clin Genitourin Cancer 2019; 17:306-314. [PMID: 31221545 DOI: 10.1016/j.clgc.2019.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/12/2019] [Accepted: 05/21/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Whole-body assessments of 18F-NaF positron emission tomography (PET)/computed tomography (CT) provide promising quantitative imaging biomarkers of metastatic castration-resistant prostate cancer (mCRPC). This study investigated whether the distribution of metastases across anatomic regions is prognostic of progression-free survival. PATIENTS AND METHODS Fifty-four mCRPC patients with osseous metastases received baseline NaF PET/CT. Patients received chemotherapy (n = 16) or androgen receptor pathway inhibitors (n = 38). Semiautomated analysis using Quantitative Total Bone Imaging software extracted imaging metrics for the whole, axial, and appendicular skeleton as well as 11 skeletal regions. Five PET metrics were extracted for each region: number of lesions (NL), standardized maximum uptake value (SUVmax), average uptake (SUVmean), sum of uptake (SUVtotal), and diseased fraction of the skeleton (volume fraction). Progression included that discovered by clinical, biochemical, or radiographic means. Univariate and multivariate Cox proportional hazard regression analyses were performed between imaging metrics and progression-free survival, and were assessed according to their hazard ratios (HR) and concordance (C)-indices. RESULTS The strongest univariate models of progression-free survival were pelvic NL and SUVmax with HR = 1.80 (NL: false discovery rate adjusted P = .001, SUVmax: adjusted P = .001). Three other region-specific metrics (axial NL: HR = 1.59, adjusted P = .02, axial SUVmax: HR = 1.61, adjusted P = .02, and skull SUVmax: HR = 1.58, adjusted P = .04) were found to be stronger prognosticators relative to their whole-body counterparts. Multivariate model including region-specific metrics (C-index = 0.727) outperformed that of whole-body metrics (C-index = 0.705). The best performance was obtained when region-specific and whole-body metrics were included (C-index = 0.742). CONCLUSION Quantitative characterization of metastatic spread by anatomic location on NaF PET/CT enhances potential prognostication. Further study is warranted to optimize the prognostic and predictive value of NaF PET/CT in mCRPC patients.
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Affiliation(s)
- Alison R Roth
- Department of Medical Physics, University of Wisconsin, Madison, WI.
| | | | - Timothy G Perk
- Department of Medical Physics, University of Wisconsin, Madison, WI
| | - Jens Eickhoff
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD
| | - Karen A Kurdziel
- Molecular Imaging Branch, National Cancer Institute, Bethesda, MD
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD
| | - Andrea B Apolo
- Genitourinary Malignancies Branch, National Cancer Institute, Bethesda, MD
| | | | | | - Glenn Liu
- Department of Medical Physics, University of Wisconsin, Madison, WI; University of Wisconsin Carbone Cancer Center, Madison, WI
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI; University of Wisconsin Carbone Cancer Center, Madison, WI
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Smith CP, Harmon SA, Barrett T, Bittencourt LK, Law YM, Shebel H, An JY, Czarniecki M, Mehralivand S, Coskun M, Wood BJ, Pinto PA, Shih JH, Choyke PL, Turkbey B. Intra- and interreader reproducibility of PI-RADSv2: A multireader study. J Magn Reson Imaging 2019; 49:1694-1703. [PMID: 30575184 PMCID: PMC6504619 DOI: 10.1002/jmri.26555] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 10/06/2018] [Accepted: 10/09/2018] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) has been in use since 2015; while interreader reproducibility has been studied, there has been a paucity of studies investigating the intrareader reproducibility of PI-RADSv2. PURPOSE To evaluate both intra- and interreader reproducibility of PI-RADSv2 in the assessment of intraprostatic lesions using multiparametric magnetic resonance imaging (mpMRI). STUDY TYPE Retrospective. POPULATION/SUBJECTS In all, 102 consecutive biopsy-naïve patients who underwent prostate MRI and subsequent MR/transrectal ultrasonography (MR/TRUS)-guided biopsy. FIELD STRENGTH/SEQUENCES Prostate mpMRI at 3T using endorectal with phased array surface coils (TW MRI, DW MRI with ADC maps and b2000 DW MRI, DCE MRI). ASSESSMENT Previously detected and biopsied lesions were scored by four readers from four different institutions using PI-RADSv2. Readers scored lesions during two readout rounds with a 4-week washout period. STATISTICAL TESTS Kappa (κ) statistics and specific agreement (Po ) were calculated to quantify intra- and interreader reproducibility of PI-RADSv2 scoring. Lesion measurement agreement was calculated using the intraclass correlation coefficient (ICC). RESULTS Overall intrareader reproducibility was moderate to substantial (κ = 0.43-0.67, Po = 0.60-0.77), while overall interreader reproducibility was poor to moderate (κ = 0.24, Po = 46). Readers with more experience showed greater interreader reproducibility than readers with intermediate experience in the whole prostate (P = 0.026) and peripheral zone (P = 0.002). Sequence-specific interreader agreement for all readers was similar to the overall PI-RADSv2 score, with κ = 0.24, 0.24, and 0.23 and Po = 0.47, 0.44, and 0.54 in T2 -weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE), respectively. Overall intrareader and interreader ICC for lesion measurement was 0.82 and 0.71, respectively. DATA CONCLUSION PI-RADSv2 provides moderate intrareader reproducibility, poor interreader reproducibility, and moderate interreader lesion measurement reproducibility. These findings suggest a need for more standardized reader training in prostate MRI. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2.
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Affiliation(s)
- Clayton P. Smith
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, U.S.A
- Georgetown University School of Medicine, Washington, D.C., U.S.A
| | - Stephanie A. Harmon
- Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., NCI Campus at Frederick, Frederick, MD, U.S.A
| | - Tristan Barrett
- Department of Radiology, Addenbrooke’s Hospital and the University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Leonardo K. Bittencourt
- Department of Radiology, Fluminese Federal University, Rio de Janeiro, Brazil
- CDPI Clinics, DASA, Rio de Janeiro, Brazil
| | - Yan Mee Law
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore
| | - Haytham Shebel
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura City, Egypt
| | - Julie Y. An
- Northeast Ohio Medical University, Rootstown, OH, U.S.A
| | - Marcin Czarniecki
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, U.S.A
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, U.S.A
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, U.S.A
- Department of Urology and Pediatric Urology, University Medical Center, Mainz, Germany
| | - Mehmet Coskun
- Department of Radiology, Dr. Behcet Uz Child Disease and Pediatric Surgery Training and Research Hospital, University of Health Sciences, İzmir, Turkey
| | - Bradford J. Wood
- Department of Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD, U.S.A
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, U.S.A
| | - Joanna H. Shih
- Biometric Research Program, National Cancer Institute, NIH, Rockville, MD, U.S.A
| | - Peter L. Choyke
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, U.S.A
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, U.S.A
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Harmon SA, Tuncer S, Sanford T, Choyke PL, Türkbey B. Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol 2019; 25:183-188. [PMID: 31063138 PMCID: PMC6521904 DOI: 10.5152/dir.2019.19125] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 03/08/2019] [Accepted: 03/23/2019] [Indexed: 01/30/2023]
Abstract
Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.
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Affiliation(s)
- Stephanie A. Harmon
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Sena Tuncer
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Thomas Sanford
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Peter L. Choyke
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Barış Türkbey
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
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Harmon SA, Mena E, Shih JH, Adler S, McKinney Y, Bergvall E, Mehralivand S, Sowalsky AG, Couvillon A, Madan RA, Gulley JL, Eary J, Mease RC, Pomper MG, Dahut WL, Turkbey B, Lindenberg L, Choyke PL. A comparison of prostate cancer bone metastases on 18F-Sodium Fluoride and Prostate Specific Membrane Antigen ( 18F-PSMA) PET/CT: Discordant uptake in the same lesion. Oncotarget 2018; 9:37676-37688. [PMID: 30701023 PMCID: PMC6340866 DOI: 10.18632/oncotarget.26481] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/04/2018] [Indexed: 12/27/2022] Open
Abstract
Purpose Prostate-Specific Membrane Antigen (PSMA) PET/CT has been introduced as a sensitive method for characterizing metastatic prostate cancer. The purpose of this study is to compare the spatial concordance of 18F-NaF PET/CT and 18F-PSMA-targeted PET/CT within prostate cancer bone metastases. Methods Prostate cancer patients with known bone metastases underwent PSMA-targeted PET/CT (18F-DCFBC or 18F-DCFPyL) and 18F-NaF PET/CT. In pelvic and spinal lesions detected by both radiotracers, regions-of-interest (ROIs) derived by various thresholds of uptake intensity were compared for spatial colocalization. Overlap volume was correlated with uptake characteristics and disease status. Results The study included 149 lesions in 19 patients. Qualitatively, lesions exhibited a heterogeneous range of spatial concordance between PSMA and NaF uptake from completely matched to completely discordant. Quantitatively, overlap volume decreased as a function of tracer intensity. and disease status, where lesions from patients with castration-sensitive disease showed higher spatial concordance while lesions from patients with castration-resistant disease demonstrated more frequent spatial discordance. Conclusion As metastatic prostate cancer progresses from castration-sensitive to castration-resistant, greater discordance is observed between NaF PET and PSMA PET uptake. This may indicate a possible phenotypic shift to tumor growth that is more independent of bone remodeling via osteoblastic formation.
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Affiliation(s)
- Stephanie A Harmon
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA.,Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Esther Mena
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Joanna H Shih
- Biometric Research Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Stephen Adler
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA.,Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Yolanda McKinney
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Ethan Bergvall
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Adam G Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Anna Couvillon
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - James L Gulley
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Janet Eary
- Cancer Imaging Program, National Cancer Institute, NIH, Rockville, MD, USA
| | - Ronnie C Mease
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Martin G Pomper
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Liza Lindenberg
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD, USA
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An JY, Harmon SA, Mehralivand S, Czarniecki M, Smith CP, Peretti JA, Wood BJ, Pinto PA, Choyke PL, Shih JH, Turkbey B. Evaluating the size criterion for PI-RADSv2 category 5 upgrade: is 15 mm the best threshold? Abdom Radiol (NY) 2018; 43:3436-3444. [PMID: 29752491 DOI: 10.1007/s00261-018-1631-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE The purpose of the study was to determine if the ≥ 15 mm threshold currently used to define PIRADS 5 lesions is the optimal size threshold for predicting high likelihood of clinically significant (CS) cancers. MATERIALS Three hundred and fifty-eight lesions that may be changed from category 4 to 5 or vice versa on the basis of the size criterion (category 4: n = 288, category 5: n = 70) from 255 patients were evaluated. Kendall's tau-b statistic accounting for inter-lesion correlation, generalized estimation equation logistic regression, and receiver operating curve analysis evaluated two lesion size-metrics (lesion diameter and relative lesion diameter-defined as lesion diameter/prostate volume) for ability to identify CS (Gleason grade ≥ 3 + 4) cancer at targeted biopsy. Optimal cut-points were identified using the Youden index. Analyses were performed for the whole prostate (WP) and zone-specific sub-cohorts of lesions in the peripheral and transition zones (PZ and TZ). RESULTS Lesion diameter showed a modest correlation with Gleason grade (WP: τB = 0.21, p < 0.0001; PZ: τB = 0.13, p = 0.02; TZ: τB = 0.32, p = 0.001), and association with CS cancer detection (WP: AUC = 0.63, PZ: AUC = 0.59, TZ: AUC = 0.74). Empirically derived thresholds (WP: 14 mm, PZ: 13 mm, TZ: 16 mm) performed similarly to the current ≥ 15 mm standard. Lesion relative lesion diameter improved identification of CS cancers compared to lesion diameter alone (WP: τB = 0.30, PZ: τB = 0.24, TZ: τB = 0.42, all p < 0.0001). AUC also improved for WP and PZ lesions (WP: AUC = 0.70, PZ: AUC = 0.68, and TZ: AUC = 0.74). CONCLUSIONS The current ≥ 15 mm diameter threshold is a reasonable delineator of PI-RADS category 4 and category 5 lesions in the absence of extraprostatic extension to predict CS cancers. Additionally, relative lesion diameter can improve identification of CS cancers and may serve as another option for distinguishing category 4 and 5 lesions.
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Affiliation(s)
- Julie Y An
- Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA
| | - Stephanie A Harmon
- Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc, NCI Campus at Frederick, 1050 Boyle Street, Frederick, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA
- Department of Urology and Pediatric Urology, University Medical Center, Mainz, Germany
| | - Marcin Czarniecki
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Clayton P Smith
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Julie A Peretti
- Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Joanna H Shih
- Biometric Research Program, National Cancer Institute, National Institutes of Health, 6130 Executive Plaza, Rockville, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA.
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50
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Gaur S, Lay N, Harmon SA, Doddakashi S, Mehralivand S, Argun B, Barrett T, Bednarova S, Girometti R, Karaarslan E, Kural AR, Oto A, Purysko AS, Antic T, Magi-Galluzzi C, Saglican Y, Sioletic S, Warren AY, Bittencourt L, Fütterer JJ, Gupta RT, Kabakus I, Law YM, Margolis DJ, Shebel H, Westphalen AC, Wood BJ, Pinto PA, Shih JH, Choyke PL, Summers RM, Turkbey B. Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation. Oncotarget 2018; 9:33804-33817. [PMID: 30333911 PMCID: PMC6173466 DOI: 10.18632/oncotarget.26100] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 08/23/2018] [Indexed: 12/31/2022] Open
Abstract
For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.
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Affiliation(s)
- Sonia Gaur
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathan Lay
- Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A. Harmon
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Clinical Research Directorate/ Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sreya Doddakashi
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Urology and Pediatric Urology, University Medical Center Mainz, Mainz, Germany
| | - Burak Argun
- Department of Urology, Acibadem University, Istanbul, Turkey
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | | | | | - Ali Riza Kural
- Department of Urology, Acibadem University, Istanbul, Turkey
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | | | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | | | - Yesim Saglican
- Department of Pathology, Acibadem University, Istanbul, Turkey
| | | | - Anne Y. Warren
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | | | - Rajan T. Gupta
- Department of Radiology, Duke University, Durham, NC, USA
| | - Ismail Kabakus
- Department of Radiology, Hacettepe University, Ankara, Turkey
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | | | - Haytham Shebel
- Department of Radiology, Mansoura University, Mansoura, Egypt
| | - Antonio C. Westphalen
- UCSF Department of Radiology, University of California-San Francisco, San Francisco, CA, USA
| | - Bradford J. Wood
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanna H. Shih
- Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L. Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ronald M. Summers
- Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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