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.
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.
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.
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.
EJNMMI research. 2022 Dec 29*** epublish ***
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
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA. ., The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Department of Nuclear Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea., Department of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany., The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., The Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA.