Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET

Kevin H Leung; Steven P Rowe; Jeffrey P Leal; Saeed Ashrafinia; Mohammad S Sadaghiani; Hyun Woo Chung; Pejman Dalaie; Rima Tulbah; Yafu Yin; Ryan VanDenBerg; Rudolf A Werner; Kenneth J Pienta; Michael A Gorin; Yong Du; Martin G Pomper

doi:10.1186/s13550-022-00948-1

Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET

EJNMMI Res. 2022 Dec 29;12(1):76. doi: 10.1186/s13550-022-00948-1.

Authors

Kevin H Leung^{1

2}, Steven P Rowe^{3

4}, Jeffrey P Leal³, Saeed Ashrafinia³, Mohammad S Sadaghiani³, Hyun Woo Chung⁵, Pejman Dalaie³, Rima Tulbah³, Yafu Yin⁶, Ryan VanDenBerg³, Rudolf A Werner⁷, Kenneth J Pienta⁴, Michael A Gorin⁸, Yong Du³, Martin G Pomper^{9

3

4}

Affiliations

¹ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA. kleung8@jhmi.edu.
² The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. kleung8@jhmi.edu.
³ The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴ The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁵ Department of Nuclear Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
⁶ Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁷ Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany.
⁸ The Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁹ Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA.

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.

Keywords: Classification; Deep learning; PSMA PET; PSMA-RADS; Prostate cancer; t-SNE.

Abstract

Grants and funding