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Leung KH, Rowe SP, Leal JP, Ashrafinia S, Sadaghiani MS, Chung HW, Dalaie P, Tulbah R, Yin Y, VanDenBerg R, Werner RA, Pienta KJ, Gorin MA, Du Y, Pomper MG. Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET. EJNMMI Res 2022; 12:76. [PMID: 36580220 PMCID: PMC9800682 DOI: 10.1186/s13550-022-00948-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/12/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Accurate classification of sites of interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation of prostate cancer (PCa) from foci of physiologic uptake. We developed a deep learning and radiomics framework to perform lesion-level and patient-level classification on PSMA PET images of patients with PCa. METHODS This was an IRB-approved, HIPAA-compliant, retrospective study. Lesions on [18F]DCFPyL PET/CT scans were assigned to PSMA reporting and data system (PSMA-RADS) categories and randomly partitioned into training, validation, and test sets. The framework extracted image features, radiomic features, and tissue type information from a cropped PET image slice containing a lesion and performed PSMA-RADS and PCa classification. Performance was evaluated by assessing the area under the receiver operating characteristic curve (AUROC). A t-distributed stochastic neighbor embedding (t-SNE) analysis was performed. Confidence and probability scores were measured. Statistical significance was determined using a two-tailed t test. RESULTS PSMA PET scans from 267 men with PCa had 3794 lesions assigned to PSMA-RADS categories. The framework yielded AUROC values of 0.87 and 0.90 for lesion-level and patient-level PSMA-RADS classification, respectively, on the test set. The framework yielded AUROC values of 0.92 and 0.85 for lesion-level and patient-level PCa classification, respectively, on the test set. A t-SNE analysis revealed learned relationships between the PSMA-RADS categories and disease findings. Mean confidence scores reflected the expected accuracy and were significantly higher for correct predictions than for incorrect predictions (P < 0.05). Measured probability scores reflected the likelihood of PCa consistent with the PSMA-RADS framework. CONCLUSION The framework provided lesion-level and patient-level PSMA-RADS and PCa classification on PSMA PET images. The framework was interpretable and provided confidence and probability scores that may assist physicians in making more informed clinical decisions.
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Affiliation(s)
- Kevin H. Leung
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD 21287 USA ,grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Steven P. Rowe
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA ,grid.21107.350000 0001 2171 9311The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Jeffrey P. Leal
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Saeed Ashrafinia
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Mohammad S. Sadaghiani
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Hyun Woo Chung
- grid.258676.80000 0004 0532 8339Department of Nuclear Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea
| | - Pejman Dalaie
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Rima Tulbah
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Yafu Yin
- grid.16821.3c0000 0004 0368 8293Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ryan VanDenBerg
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Rudolf A. Werner
- grid.411760.50000 0001 1378 7891Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Kenneth J. Pienta
- grid.21107.350000 0001 2171 9311The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Michael A. Gorin
- grid.59734.3c0000 0001 0670 2351The Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Yong Du
- grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Martin G. Pomper
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD 21287 USA ,grid.21107.350000 0001 2171 9311The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD USA ,grid.21107.350000 0001 2171 9311The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD USA
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