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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] [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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Li Y, Imami MR, Zhao L, Amindarolzarbi A, Mena E, Leal J, Chen J, Gafita A, Voter AF, Li X, Du Y, Zhu C, Choyke PL, Zou B, Jiao Z, Rowe SP, Pomper MG, Bai HX. An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01104-y. [PMID: 38587770 DOI: 10.1007/s10278-024-01104-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 01/22/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024]
Abstract
Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy.
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Affiliation(s)
- Yang Li
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, China
| | - Maliha R Imami
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Linmei Zhao
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Alireza Amindarolzarbi
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Esther Mena
- National Institutes of Health, Bethesda, 20892, USA
| | - Jeffrey Leal
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Junyu Chen
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Andrei Gafita
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Andrew F Voter
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Xin Li
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Yong Du
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Chengzhang Zhu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | | | - Beiji Zou
- School of Informatics, Hunan University of Chinese Medicine, Changsha, 410208, China
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Zhicheng Jiao
- Warren Alpert Medical School of Brown University, Providence, 02903, USA
| | - Steven P Rowe
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Martin G Pomper
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA
| | - Harrison X Bai
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA.
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Rowe SP, Sadaghiani MS, Gafita A, Sheikhbahaei S, Pomper MG, Young J, Spitz A, Werner RA, Oldan JD, Solnes LB. Prostate-Specific Membrane Antigen-Ligand Therapy: What the Radiologist Needs to Know. Radiol Clin North Am 2024; 62:177-187. [PMID: 37973242 DOI: 10.1016/j.rcl.2023.07.003] [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: 11/19/2023]
Abstract
The discovery and clinical development of radiolabeled small-molecule ligands targeting prostate-specific membrane antigen (PSMA) has had a profound influence on the field of nuclear medicine. Such agents have been successfully deployed for both imaging and therapeutic applications. In particular, PSMA radioligand therapy (PRLT) has been shown to be a life-prolonging therapy for men with metastatic, castration-resistant prostate cancer and has also brought nuclear medicine physicians and nuclear radiologists into the forefront of direct patient care. In this review, we will discuss the clinical study data regarding the efficacy and toxicities related to PRLT, outline the key personnel that any center offering PRLT should have, offer salient clinical examples, and provide an overview of future directions for PRLT. As PRLT continues to evolve as a treatment modality, it is paramount that nuclear medicine physicians and nuclear radiologists understand the clinical context, management implications, and practical aspects so as to best deliver high-value care to patients.
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Affiliation(s)
- Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, USA.
| | - Mohammad S Sadaghiani
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, USA
| | - Andrei Gafita
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, USA
| | - Sara Sheikhbahaei
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, USA
| | - Martin G Pomper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, USA
| | - Jeffrey Young
- Johns Hopkins Hospital, 600 North Wolfe Street, Baltimore, MD 21287, USA
| | - Avery Spitz
- Sidney Kimmell Comprehensive Cancer Center, Johns Hopkins University School of Medicine, 401 North Broadway Street, Baltimore, MD 21231, USA
| | - Rudolf A Werner
- Department of Nuclear Medicine, University Hospital Würzburg Oberdürrbacherstraße 6, 97080 Würzburg, Germany
| | - Jorge D Oldan
- Department of Radiology, University of North Carolina, 101 Manning Drive, Chapel Hill, NC 27514, USA
| | - Lilja B Solnes
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street, Baltimore, MD 21287, USA
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Hartrampf PE, Serfling SE, Michalski K, Buck AK, Werner RA. PSMA PET/CT for Response Assessment of 177Lu-PSMA Therapy. Semin Nucl Med 2024; 54:69-76. [PMID: 37357025 DOI: 10.1053/j.semnuclmed.2023.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 06/27/2023]
Abstract
Prostate-specific membrane antigen (PSMA) PET/CT has been widely integrated into the management of prostate cancer (PCa) patients with biochemical recurrence, is increasingly used for initial staging in high-risk patients prior to surgery or to identify candidates for PSMA-targeted radioligand therapy (RLT). To date, monitoring response in PCa patients in prospective studies remains the domain of conventional imaging, such as magnetic resonance/CT or bone scintigraphy. With the increasing use of PSMA-targeted PET/CT in PCa, however, varying criteria based on molecular imaging have been established to define progressive disease, including "PSMA PET Progression Criteria," "Response evaluation criteria in PSMA PET/CT (RECIP 1.0)" or consensus statements of respective societies. In the present review, we will discuss the current status of PSMA PET/CT for response monitoring, focusing on PSMA RLT with [177Lu]Lu-labeled PSMA ligands, along with a head-to-head comparison of recently published response criteria.
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Affiliation(s)
- Philipp E Hartrampf
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany.
| | | | - Kerstin Michalski
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Andreas K Buck
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Rudolf A Werner
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany; The Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, Baltimore, MD
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Kendrick J, Francis RJ, Hassan GM, Rowshanfarzad P, Ong JS, Jeraj R, Barry N, Hagan T, Ebert MA. Prospective inter- and intra-tracer repeatability analysis of radiomics features in [ 68Ga]Ga-PSMA-11 and [ 18F]F-PSMA-1007 PET scans in metastatic prostate cancer. Br J Radiol 2023; 96:20221178. [PMID: 37751168 PMCID: PMC10646662 DOI: 10.1259/bjr.20221178] [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: 12/16/2022] [Revised: 07/14/2023] [Accepted: 08/25/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVE This study aimed to quantify both the intra- and intertracer repeatability of lesion-level radiomics features in [68Ga]Ga-prostate-specific membrane antigen (PSMA)-11 and [18F]F-PSMA-1007 positron emission tomography (PET) scans. METHODS Eighteen patients with metastatic prostate cancer (mPCa) were prospectively recruited for the study and randomised to one of three test-retest groups: (i) intratracer [68Ga]Ga-PSMA-11 PET, (ii) intratracer [18F]F-PSMA-1007 PET or (iii) intertracer between [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET. Four conventional PET metrics (standardised uptake value (SUV)max, SUVmean, SUVtotal and volume) and 107 radiomics features were extracted from 75 lesions and assessed using the repeatability coefficient (RC) and the ICC. Radiomic feature repeatability was also quantified after the application of 16 filters to the PET image. RESULTS Test-retest scans were taken a median of 5 days apart (range: 2-7 days). SUVmean demonstrated the lowest RC limits of the conventional features, with RCs of 7.9%, 14.2% and 24.7% for the [68Ga]Ga-PSMA-11 PET, [18F]F-PSMA-1007 PET, and intertracer groups, respectively. 69%, 66% and 9% of all radiomics features had good or excellent ICC values (ICC ≥ 0.75) for the same groups. Feature repeatability therefore diminished considerably for the intertracer group relative to intratracer groups. CONCLUSION In this study, robust biomarkers for each tracer group that can be used in subsequent clinical studies were identified. Overall, the repeatability of conventional and radiomic features were found to be substantially lower for the intertracer group relative to both intratracer groups, suggesting that assessing patient response quantitatively should be done using the same radiotracer where possible. ADVANCES IN KNOWLEDGE Intertracer biomarker repeatability limits are significantly larger than intratracer limits.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | | | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | - Jeremy S.L. Ong
- Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, Australia
| | | | - Nathaniel Barry
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia
| | - Tammy Hagan
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, Australia
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Werner RA, Habacha B, Lütje S, Bundschuh L, Kosmala A, Essler M, Derlin T, Higuchi T, Lapa C, Buck AK, Pienta KJ, Lodge MA, Eisenberger MA, Markowski MC, Pomper MG, Gorin MA, Frey EC, Rowe SP, Bundschuh RA. Lack of repeatability of radiomic features derived from PET scans: Results from a 18 F-DCFPyL test-retest cohort. Prostate 2023; 83:547-554. [PMID: 36632656 DOI: 10.1002/pros.24483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/06/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023]
Abstract
OBJECTIVES PET-based radiomic metrics are increasingly utilized as predictive image biomarkers. However, the repeatability of radiomic features on PET has not been assessed in a test-retest setting. The prostate-specific membrane antigen-targeted compound 18 F-DCFPyL is a high-affinity, high-contrast PET agent that we utilized in a test-retest cohort of men with metastatic prostate cancer (PC). METHODS Data of 21 patients enrolled in a prospective clinical trial with histologically proven PC underwent two 18 F-DCFPyL PET scans within 7 days, using identical acquisition and reconstruction parameters. Sites of disease were segmented and a set of 29 different radiomic parameters were assessed on both scans. We determined repeatability of quantification by using Pearson's correlations, within-subject coefficient of variation (wCOV), and Bland-Altman analysis. RESULTS In total, 230 lesions (177 bone, 38 lymph nodes, 15 others) were assessed on both scans. For all investigated radiomic features, a broad range of inter-scan correlation was found (r, 0.07-0.95), with acceptable reproducibility for entropy and homogeneity (wCOV, 16.0% and 12.7%, respectively). On Bland-Altman analysis, no systematic increase or decrease between the scans was observed for either parameter (±1.96 SD: 1.07/-1.30, 0.23/-0.18, respectively). The remaining 27 tested radiomic metrics, however, achieved unacceptable high wCOV (≥21.7%). CONCLUSION Many common radiomic features derived from a test-retest PET study had poor repeatability. Only Entropy and homogeneity achieved good repeatability, supporting the notion that those image biomarkers may be incorporated in future clinical trials. Those radiomic features based on high frequency aspects of images appear to lack the repeatability on PET to justify further study.
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Affiliation(s)
- Rudolf A Werner
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Bilêl Habacha
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Susanne Lütje
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Lena Bundschuh
- Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Alekandser Kosmala
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Thorsten Derlin
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Takahiro Higuchi
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Constantin Lapa
- Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Andreas K Buck
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Kenneth J Pienta
- Department of Urology, The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Martin A Lodge
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mario A Eisenberger
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Mark C Markowski
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Martin G Pomper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Urology, The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Michael A Gorin
- Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric C Frey
- Radiopharmaceutical Imaging and Dosimetry, LLC, Baltimore, Maryland, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Urology, The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Ralph A Bundschuh
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
- Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany
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7
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Voter AF, Werner RA, Pienta KJ, Gorin MA, Pomper MG, Solnes LB, Rowe SP. Piflufolastat F-18 ( 18F-DCFPyL) for PSMA PET imaging in prostate cancer. Expert Rev Anticancer Ther 2022; 22:681-694. [DOI: 10.1080/14737140.2022.2081155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Andrew F. Voter
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Transitional Year Residency Program, Aurora St. Luke’s Medical Center, Advocate Aurora Health, Milwaukee, WI, USA
| | - Rudolf A. Werner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Kenneth J. Pienta
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael A. Gorin
- Urology Associates and UPMC Western Maryland, Cumberland, MD, USA
- Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Martin G. Pomper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lilja B. Solnes
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Steven P. Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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