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Park SY, Woo S, Park KJ, Westphalen AC. A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice. Abdom Radiol (NY) 2024; 49:3190-3205. [PMID: 38704782 DOI: 10.1007/s00261-024-04273-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/02/2024] [Accepted: 03/03/2024] [Indexed: 05/07/2024]
Abstract
Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.
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Affiliation(s)
- Sung Yoon Park
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA.
| | - Sungmin Woo
- Department of Radiology, NYU Langone Health, New York, NY, 10016, USA
| | - Kye Jin Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Antonio C Westphalen
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
- Department of Urology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
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2
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Tammisetti VS, Jacobs MA. Evidence-based Diagnostic Performance Benchmarks in Prostate MRI: An Unmet Clinical Need. Radiology 2024; 312:e241792. [PMID: 39136559 DOI: 10.1148/radiol.241792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Affiliation(s)
- Varaha S Tammisetti
- From the Department of Diagnostic and Interventional Imaging, McGovern Medical School at The University of Texas Health Science Center, 6431 Fannin St, Room R172, Houston, TX 77030 (V.S.T., M.A.J.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, Md (M.A.J.); Graduate School of Biomedical Sciences, MD Anderson Cancer Center, The University of Texas, Houston, Tex (M.A.J.); and Department of Computer Science, Rice University, Houston, Tex (M.A.J.)
| | - Michael A Jacobs
- From the Department of Diagnostic and Interventional Imaging, McGovern Medical School at The University of Texas Health Science Center, 6431 Fannin St, Room R172, Houston, TX 77030 (V.S.T., M.A.J.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, Md (M.A.J.); Graduate School of Biomedical Sciences, MD Anderson Cancer Center, The University of Texas, Houston, Tex (M.A.J.); and Department of Computer Science, Rice University, Houston, Tex (M.A.J.)
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3
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Asai S, Kobayashi M, Fukuda S, Kimura K, Yoshida S, Fujii Y. Significance of atypical nodules upgraded to category 3 in Prostate Imaging Reporting and Data System version 2.1 for prostate cancer diagnosis. Int J Urol 2024; 31:693-695. [PMID: 38345162 DOI: 10.1111/iju.15421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 10/11/2023] [Accepted: 12/31/2024] [Indexed: 06/06/2024]
Affiliation(s)
- Shintaro Asai
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masaki Kobayashi
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shohei Fukuda
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koichiro Kimura
- Department of Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Soichiro Yoshida
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yasuhisa Fujii
- Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan
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4
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Jin L, Yu Z, Gao F, Li M. T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study. Insights Imaging 2024; 15:111. [PMID: 38713377 PMCID: PMC11076444 DOI: 10.1186/s13244-024-01682-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 03/23/2024] [Indexed: 05/08/2024] Open
Abstract
OBJECTIVES To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. METHODS Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. RESULTS For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. CONCLUSION Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. CRITICAL RELEVANCE STATEMENT Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. KEY POINTS Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.
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Affiliation(s)
- Liang Jin
- Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai, 200040, China
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Zhuo Yu
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Feng Gao
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Ming Li
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China.
- Institute of Functional and Molecular Medical Imaging, Shanghai, 200040, China.
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Chen Y, Meng T, Cao W, Zhang W, Ling J, Wen Z, Qian L, Guo Y, Lin J, Wang H. Histogram analysis of MR quantitative parameters: are they correlated with prognostic factors in prostate cancer? Abdom Radiol (NY) 2024; 49:1534-1544. [PMID: 38546826 DOI: 10.1007/s00261-024-04227-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/27/2024] [Accepted: 01/29/2024] [Indexed: 05/22/2024]
Abstract
PURPOSE To investigate the correlation between quantitative MR parameters and prognostic factors in prostate cancer (PCa). METHOD A total of 186 patients with pathologically confirmed PCa who underwent preoperative multiparametric MRI (mpMRI), including synthetic MRI (SyMRI), were enrolled from two medical centers. The histogram metrics of SyMRI [T1, T2, proton density (PD)] and apparent diffusion coefficient (ADC) values were extracted. The Mann‒Whitney U test or Student's t test was employed to determine the association between these histogram features and the prognostically relevant factors. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the differentiation performance. Spearman's rank correlation coefficients were calculated to determine the correlations between histogram parameters and the International Society of Urological Pathology (ISUP) grade group as well as pathological T stage. RESULTS Significant correlations were found between the histogram parameters and the ISUP grade as well as pathological T stage of PCa. Among these histogram parameters, ADC_minimum had the strongest correlation with the ISUP grade (r = - 0.481, p < 0.001), and ADC_Median showed the strongest association with pathological T stage (r = - 0.285, p = 0.008). The ADC_10th percentile exhibited the highest performance in identifying clinically significant prostate cancer (csPCa) (AUC 0.833; 95% CI 0.771-0.883). When discriminating between the status of different prognostically relevant factors, a significant difference was observed between extraprostatic extension-positive and -negative cancers with regard to histogram parameters of the ADC map (10th percentile, 90th percentile, mean, median, minimum) and T1 map (minimum) (p = 0.002-0.032). Moreover, histogram parameters of the ADC map (90th percentile, maximum, mean, median), T2 map (10th percentile, median), and PD map (10th percentile, median) were significantly lower in PCa with perineural invasion (p = 0.009-0.049). The T2 values were significantly lower in patients with seminal vesicle invasion (minimum, p = 0.036) and positive surgical margin (10th percentile, 90th percentile, mean, median, and minimum, p = 0.015-0.025). CONCLUSION Quantitative histogram parameters derived from synthetic MRI and ADC maps may have great potential for predicting the prognostic features of PCa.
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Affiliation(s)
- Yanling Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Tiebao Meng
- Department of Radiology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, Guangdong, People's Republic of China
| | - Wenxin Cao
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Weijing Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, No. 651 Dongfeng East Road, Guangzhou, Guangdong, People's Republic of China
| | - Jian Ling
- Department of Radiology, The Eastern Hospital of the First Affiliated Hospital, Sun Yat-sen University, No.183 Huangpu Eastern Road, Guangzhou, Guangdong, People's Republic of China
| | - Zhihua Wen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, People's Republic of China
| | - Long Qian
- MR Research, GE Healthcare, Beijing, People's Republic of China
| | - Yan Guo
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, People's Republic of China.
| | - Jinhua Lin
- Division of Interventional Ultrasound, Department of Medical Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, Guangdong, People's Republic of China.
| | - Huanjun Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, People's Republic of China.
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Turkbey B. Use of Molecular Imaging to Further Investigate PI-RADS 3 Lesions. Radiology 2024; 311:e240401. [PMID: 38771180 PMCID: PMC11140525 DOI: 10.1148/radiol.240401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 05/22/2024]
Affiliation(s)
- Baris Turkbey
- From the Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr, Room B3B85, Bethesda, MD 20892
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7
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de Oliveira Correia ET, Purysko AS, Paranhos BM, Shoag JE, Padhani AR, Bittencourt LK. PI-RADS Upgrading Rules: Impact on Prostate Cancer Detection and Biopsy Avoidance of MRI-Directed Diagnostic Pathways. AJR Am J Roentgenol 2024; 222:e2330611. [PMID: 38353450 DOI: 10.2214/ajr.23.30611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
BACKGROUND. PI-RADS incorporates rules by which ancillary sequence findings upgrade a dominant score to a higher final category. Evidence on the upgrading rules' impact on diagnostic pathways remains scarce. OBJECTIVE. The purpose of this article was to evaluate the clinical net benefit of the PI-RADS upgrading rules in MRI-directed diagnostic pathways. METHODS. This study was a retrospective analysis of a prospectively maintained clinical registry. The study included patients without known prostate cancer who underwent prostate MRI followed by prostate biopsy from January 2016 to May 2020. Clinically significant prostate cancer (csPCa) was defined as International Society of Urological Pathology (ISUP) grade group 2 and higher. csPCa detection was compared between dominant (i.e., no upgrade rule applied) and upgraded lesions. Decision-curve analysis was used to compare the net benefit, considering the trade-off of csPCa detection and biopsy avoidance, of MRI-directed pathways in scenarios considering and disregarding PI-RADS upgrading rules. These included a biopsy-all pathway, MRI-focused pathway (no biopsy for PI-RADS ≤ 2), and risk-based pathway (use of PSA density ≥ 0.15 ng/mL2 to select patients with PI-RADS ≤ 3 for biopsy). RESULTS. The sample comprised 716 patients (mean age, 64.9 years; 93 with a PI-RADS ≤ 2 examination, 623 with total of 780 PI-RADS ≥ 3 lesions). Frequencies of csPCa were not significantly different between dominant and upgraded PI-RADS 3 transition zone lesions (20% vs 19%, respectively), dominant and upgraded PI-RADS 4 transition zone lesions (33% vs 26%), and dominant and upgraded PI-RADS 4 peripheral zone lesions (58% vs 45%) (p > .05). In the biopsy-all, per-guideline MRI-focused, MRI-focused disregarding upgrading rules, per-guideline risk-based, and risk-based disregarding upgrading rules pathways, csPCa frequency was 53%, 52%, 51%, 52%, and 48% and biopsy avoidance was 0%, 13%, 16%, 19%, and 25%, respectively. Disregarding upgrading rules yielded 5.5 and 1.9 biopsies avoided per missed csPCa for MRI-focused and risk-based pathways, respectively. At probability thresholds for biopsy selection of 7.5-30.0%, net benefit was highest for the per-guideline risk-based pathway. CONCLUSION. Disregarding PI-RADS upgrading rules reduced net clinical bene fit of the risk-based MRI-directed diagnostic pathway when considering trade-offs between csPCa detection and biopsy avoidance. CLINICAL IMPACT. This study supports the application of PI-RADS upgrading rules to optimize biopsy selection, particularly in risk-based pathways.
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Affiliation(s)
| | - Andrei S Purysko
- Department of Radiology, Abdominal Imaging Section, Cleveland Clinic, Cleveland, OH
| | - Bruno Merz Paranhos
- Department of Radiology, Diagnosticos da America S.A, Rio de Janeiro, Brazil
| | - Jonathan E Shoag
- Case Western Reserve University, Cleveland, OH
- Case Comprehensive Cancer Center, Cleveland, OH
- Department of Urology, University Hospitals Cleveland Medical Center, Cleveland, OH
- Department of Urology, Weill Cornell Medicine, New York, NY
| | - Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Hospital, Middlesex, United Kingdom
| | - Leonardo K Bittencourt
- Department of Radiology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, Ohio 44106
- Case Western Reserve University, Cleveland, OH
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8
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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, Atzen S. Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI. Radiology 2024; 311:e230750. [PMID: 38713024 PMCID: PMC11140533 DOI: 10.1148/radiol.230750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 01/24/2024] [Accepted: 03/18/2024] [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.)
| | - Sarah Atzen
- 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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9
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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] [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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10
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Sun M, Xu L, Zhang X, Cao L, Chen W, Liu K, Wu H, Xie D. PI-RADS v2.1 evaluation of prostate "nodule in nodule" variants: clinical, imaging, and pathological features. Insights Imaging 2024; 15:79. [PMID: 38499703 PMCID: PMC10948663 DOI: 10.1186/s13244-024-01651-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/10/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES To analyze the correlation among the imaging features of prostate "nodule in nodule," clinical prostate indices, and pathology results. METHODS We retrospectively analyzed the prostate images from 47 male patients who underwent MRI scans and pathological biopsy from January 2022 to July 2023. Two radiologists (R1/R2) evaluated the morphology and signal intensity of the "nodule in nodule" in a double-blind manner and calculated the PI-RADS v2.1 score, which was compared with clinical prostate indices and pathological results. RESULTS 34.04% (16/47) of patients were pathologically diagnosed with clinically significant prostate cancer (csPCa). Total prostate-specific antigen (tPSA), free/t PSA, PSA density (PSAD), and prostate gland volume (PGV) were significantly different between csPCa patients and benign prostatic hyperplasia (BPH) patients with prostate "nodule in nodule". R1/R2 detected 17/17 prostate "nodule in nodule" pathologically confirmed as csPCa on MRI; 10.60% (16/151) (R1) and 11.11% (17/153) (R2) had diffusion-weighted imaging (DWI) PI-RADS v2.1 score of 4, and 0.66% (1/151) (R1) had a score of 3. The percentages of encapsulated, circumscribed, and atypical nodules and obscured margins were 0.00% (0/151), 0.00% (0/151), 5.96% (9/151), and 5.30% (8/151), respectively, for R1, and 0.00% (0/153), 0.00% (0/153), 5.88% (9/153), and 4.58% (7/153) for R2. CONCLUSION When the inner nodules of "nodule in nodule" lesions in PI-RADS v2.1 category 1 in the TZ show incomplete capsulation or obscured margins, they are considered atypical nodules and might be upgraded to PI-RADS v2.1 category 3 if they exhibit marked diffusion restriction. However, further validation is needed. CRITICAL RELEVANCE STATEMENT This study first analyzed the relationship between clinical and pathological findings and the size, margin, and multimodal MRI manifestations of the prostate "nodule in nodule." These findings could improve the diagnostic accuracy of PI-RADS v2.1 for prostate lesions. KEY POINTS • The margin of the prostate inner nodules affects the PI-RADS v2.1 score. • The morphology of prostate "nodule in nodule" is related to their pathology. • The PI-RADS v2.1 principle requires consideration of prostate "nodule in nodule" variants.
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Affiliation(s)
- MingHua Sun
- Department of Radiology, the Fuyang Hospital of Anhui Medical University, Fuyang, People's Republic of China
| | - Li Xu
- Department of Radiology, the Fuyang Hospital of Anhui Medical University, Fuyang, People's Republic of China
| | - XiaoYan Zhang
- Department of Radiology, the Fuyang Hospital of Anhui Medical University, Fuyang, People's Republic of China
| | - LiYu Cao
- Department of Pathology, the Fuyang Hospital of Anhui Medical University, Fuyang, People's Republic of China
| | - WenBao Chen
- Medical Imaging Center, The Fuyang Tumor Hospital, Fuyang, People's Republic of China
| | - Kai Liu
- Department of Radiology, the Fuyang Hospital of Anhui Medical University, Fuyang, People's Republic of China
| | - Hao Wu
- Department of Radiology, the Fuyang Hospital of Anhui Medical University, Fuyang, People's Republic of China
| | - DongDong Xie
- Department of Urology, the Fuyang Hospital of Anhui Medical University, Yingzhou District, No. 99, Mount Huangshan Road, Fuhe Modern Industrial Park, Fuyang, Anhui Province, 236000, People's Republic of China.
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11
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Ortner G, Mavridis C, Fritz V, Schachtner J, Mamoulakis C, Nagele U, Tokas T. The Added Value of MRI-Based Targeted Biopsy in Biopsy-Naïve Patients: A Propensity-Score Matched Comparison. J Clin Med 2024; 13:1355. [PMID: 38592166 PMCID: PMC10931596 DOI: 10.3390/jcm13051355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/14/2024] [Accepted: 02/18/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Multiparametric Magnetic Resonance Imaging (mpMRI)-based targeted biopsy has shown to be beneficial in detecting Clinically Significant Prostate Cancer (csPCa) and avoiding diagnosis of Non-csPCa (ncsPCa); however, its role in the treatment of biopsy-naïve patients is still under discussion. METHODS After identifying predictors for the diagnosis of csPCa via Multivariate Logistic Regression Analysis (MLRA), a propensity-score (1:1 nearest neighbor) matched comparison was performed between a Systematic-Only Biopsy (SOB) cohort and a mpMRI-based Combined (systematic + targeted) Biopsy (CB) cohort from two tertiary urologic centers (SOB: Department of Urology, University General Hospital of Heraklion, University of Crete, School of Medicine, Heraklion, Crete, Greece; CB: LKH Hall in Tirol, Austria). Only biopsy-naïve patients were included in the study. The study period for the included patients was from February 2018 to July 2023 for the SOB group and from July 2017 to June 2023 for the CB group. The primary outcome was the diagnosis of csPCa (≥ISUP 2); secondary outcomes were overall cancer detection, the added value of targeted biopsy in csPCa detection, and the reduction in ncsPCa diagnosis with CB compared to SOB. To estimate the Average Treatment effect of the Treated groups (ATT), cluster-robust standard errors were used to perform g-computation in the matched sample. p-values < 0.05 with a two-sided 95% confidence interval were considered statistically significant. RESULTS Matching achieved well-balanced groups (each n = 140 for CB and SOB). In the CB group, 65/140 (46.4%) patients were diagnosed with csPCa compared to 44/140 (31.4%) in the SOB group (RR 1.48, 95%-CI: 1.09-2.0, p = 0.01). In the CB group, 4.3% (6/140) and 1.4% (2/140) of csPCa cases were detected with targeted-only and systematic-only biopsy cores, respectively. In the CB group, 22/140 (15.7%) patients were diagnosed with ncsPCa compared to 33/140 (23.6%) in the SOB group (RR = 0.67, 95% CI: 0.41-1.08, p = 0.1). When comparing SOB to CB (ATT), the marginal OR was 0.56 (95% CI: 0.38-0.82, p = 0.003) for the diagnosis of csPCa and 0.75 (95% CI: 0.47-1.05, p = 0.085) for the diagnosis of overall cancer (≥ISUP 1). CONCLUSION The CB approach was superior to the SOB approach in detecting csPCa, while no additional detection of ncsPCa was seen. Our results support the application of mpMRI for biopsy-naïve patients with suspicions of prostate cancer.
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Affiliation(s)
- Gernot Ortner
- Department of Urology and Andrology, General Hospital Hall i.T., 6060 Hall in Tirol, Austria; (G.O.); (V.F.); (J.S.); (U.N.)
- Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group, 6060 Hall in Tirol, Austria;
| | - Charalampos Mavridis
- Department of Urology, University General Hospital of Heraklion, 71110 Heraklion, Greece;
- School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Veronika Fritz
- Department of Urology and Andrology, General Hospital Hall i.T., 6060 Hall in Tirol, Austria; (G.O.); (V.F.); (J.S.); (U.N.)
- Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group, 6060 Hall in Tirol, Austria;
| | - Jörg Schachtner
- Department of Urology and Andrology, General Hospital Hall i.T., 6060 Hall in Tirol, Austria; (G.O.); (V.F.); (J.S.); (U.N.)
- Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group, 6060 Hall in Tirol, Austria;
| | - Charalampos Mamoulakis
- Department of Urology, University General Hospital of Heraklion, 71110 Heraklion, Greece;
- School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Udo Nagele
- Department of Urology and Andrology, General Hospital Hall i.T., 6060 Hall in Tirol, Austria; (G.O.); (V.F.); (J.S.); (U.N.)
- Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group, 6060 Hall in Tirol, Austria;
| | - Theodoros Tokas
- Training and Research in Urological Surgery and Technology (T.R.U.S.T.)-Group, 6060 Hall in Tirol, Austria;
- Department of Urology, University General Hospital of Heraklion, 71110 Heraklion, Greece;
- School of Medicine, University of Crete, 71003 Heraklion, Greece
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12
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Kwe J, Baunacke M, Boehm K, Platzek I, Thomas C, Borkowetz A. PI-RADS upgrading as the strongest predictor for the presence of clinically significant prostate cancer in patients with initial PI-RADS-3 lesions. World J Urol 2024; 42:84. [PMID: 38363332 PMCID: PMC10873230 DOI: 10.1007/s00345-024-04776-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/08/2024] [Indexed: 02/17/2024] Open
Abstract
PURPOSE Unclear lesions on multiparametric magnetic resonance tomography (mpMRI) are challenging for the indication of biopsy in patients with clinical suspicion of prostate cancer (PCa). The aim of this study is the validation of the detection rate of clinically significant PCa (csPCa) in patients with PI-RADS 3 findings and to determine the appropriate follow-up strategy. METHODS In this retrospective single-center study, patients with maximum PI-RADS 3 lesions underwent targeted MRI/ultrasound-fusion biopsy (tPbx) combined with systematic 12-core biopsy (sPbx) and follow-up mpMRI with further control biopsy. We assessed the evolution of MRI findings (PI-RADS, volume of the lesion), clinical parameters and histopathology in follow-up MRI and biopsies. The primary objective is the detection rate of csPCa, defined as ISUP ≥ 2 findings. RESULTS A total of 126 patients (median PSA 6.65 ng/ml; median PSA-density (PSAD) 0.13 ng/ml2) were included. The initial biopsy identified low-risk PCa in 24 cases (19%). During follow-up biopsy, 22.2% of patients showed PI-RADS upgrading (PI-RADS > 3), and 29 patients (23%) exhibited a tumor upgrading. Patients with PI-RADS upgrading had a higher risk of csPCa compared to those without PI-RADS upgrading (42.9% vs. 9.18%, p < 0.05). PI-RADS upgrading was identified as an independent predictor for csPCa in follow-up biopsy (OR 16.20; 95% CI 1.17-224.60; p = 0.038). CONCLUSION Patients with stable PI-RADS 3 findings may not require a follow-up biopsy. Instead, it is advisable to schedule an MRI, considering that PI-RADS upgrading serves as an independent predictor for csPCa.
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Affiliation(s)
- Jeremy Kwe
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Martin Baunacke
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Katharina Boehm
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Ivan Platzek
- Department of Radiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Christian Thomas
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Angelika Borkowetz
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany.
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13
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Liang Z, Feng T, Zhou Y, Yang Y, Sun Y, Zhou Z, Yan W, Cao F. Nomograms for predicting clinically significant prostate cancer in men with PI-RADS-3 biparametric magnetic resonance imaging. Am J Cancer Res 2024; 14:73-85. [PMID: 38323293 PMCID: PMC10839314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/04/2023] [Indexed: 02/08/2024] Open
Abstract
This study aimed to construct nomograms for predicting the likelihood of clinically significant prostate cancer (csPCa) in patients with lesions rated as Prostate Imaging Reporting and Data System (PI-RADS) 3 on biparametric magnetic resonance imaging (bpMRI). We retrospectively analyzed a cohort of 457 patients from the Peking Union Medical College Hospital (January 2017-July 2021) to develop the model and externally validated it with a cohort of 238 patients from the Second Hospital of Tianjin Medical University (September 2017-September 2021). Univariate and multivariate logistic regression analyses identified significant predictors of csPCa, defined by tumor volumes ≥ 0.5 cm3, Gleason score ≥ 7, or presence of extracapsular extension. Diagnostic performance for the peripheral zone (PZ) and transitional zone (TZ) was compared using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Through univariate and multivariate logistic regression analyses, we identified age, prostate-specific antigen (PSA), and prostate volume (PV) as predictors of csPCa for the PZ, and age, serum-free to total PSA ratio (f/t PSA), and PSA density (PSAD) for the TZ. The nomograms demonstrated robust discriminative ability, with an area under the ROC curve (AUC) of 0.819 for PZ and 0.804 for TZ. The external validation corroborated the model's high predictive accuracy (AUC of 0.831 for PZ and 0.773 for TZ). Calibration curves indicated excellent agreement between predicted and observed outcomes, and DCA underscored the nomogram's clinical utility for both PZ and TZ. Overall, the nomograms offer high predictive accuracy for csPCa at initial biopsy, potentially reducing unnecessary biopsies in clinical settings.
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Affiliation(s)
- Zhen Liang
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China
| | - Tianrui Feng
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China
| | - Yi Zhou
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China
| | - Yongjiao Yang
- Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin Medical UniversityTianjin, China
| | - Yujiao Sun
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China
| | - Zhien Zhou
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China
| | - Weigang Yan
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China
| | - Fenghong Cao
- Department of Urology, North China University of Science and Technology Affiliated HospitalNo. 73 Jianshe South Road, Tangshan, Hebei, China
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14
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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] [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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15
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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] [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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16
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Lin Y, Johnson LA, Fennessy FM, Turkbey B. Prostate Cancer Local Staging with Magnetic Resonance Imaging. Radiol Clin North Am 2024; 62:93-108. [PMID: 37973247 PMCID: PMC10656475 DOI: 10.1016/j.rcl.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Accurate determination of the local stage of prostate cancer is crucial for treatment planning and prognosis. The primary objective of local staging is to distinguish between organ-confined and locally advanced disease, with the latter carrying a worse clinical prognosis. The presence of locally advanced disease features of prostate cancer, such as extra-prostatic extension, seminal vesicle invasion, and positive surgical margin, can impact the choice of treatment. Over the past decade, multiparametric MRI (mpMRI) has become the preferred imaging modality for the local staging of prostate cancer and has been shown to provide accurate information on the location and extent of disease. It has demonstrated superior performance compared to staging based on traditional clinical nomograms. Despite being a relatively new technique, mpMRI has garnered considerable attention and ongoing investigations. Therefore, in this review, we will discuss the current use of mpMRI on prostate cancer local staging.
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Affiliation(s)
- Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, MSC 1182, Building 10, Room B3B85, Bethesda, MD 20892, USA
| | - Latrice A Johnson
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, MSC 1182, Building 10, Room B3B85, Bethesda, MD 20892, USA
| | - Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, MSC 1182, Building 10, Room B3B85, Bethesda, MD 20892, USA.
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17
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Takahashi T. Prebiopsy Risk Calculators, Such as MRI and Prostate Cancer Screening, Are Early-phase Clinical Trials. Radiology 2023; 309:e231177. [PMID: 37962502 DOI: 10.1148/radiol.231177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Affiliation(s)
- Takeshi Takahashi
- Health and Welfare Bureau, Kitakyushu City Office, Jyonai 1-1, Kitakyushu 803-8501, Japan
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