A multi-modal deep learning framework for predicting PSA progression-free survival in metastatic prostate cancer using PSMA PET/CT imaging

Abstract PSMA PET/CT imaging has been increasingly utilized in the management of patients with metastatic prostate cancer (mPCa). Imaging biomarkers derived from PSMA PET may provide improved prognostication and prediction of treatment response for mPCa patients. This study investigates a novel deep learning-derived imaging biomarker framework for outcome prediction using multi-modal PSMA PET/CT and clinical features. A single institution cohort of 99 mPCa patients with 396 lesions was evaluated. Imaging features were extracted from cropped lesion areas and combined with clinical variables including body mass index, ECOG performance status, prostate specific antigen (PSA) level, Gleason score, and treatments received. The PSA progression-free survival (PFS) model was trained using a ResNet architecture with a Cox proportional hazards loss function using five-fold cross-validation. Performance was assessed using concordance index (C-index) and Kaplan-Meier survival analysis. Among evaluated model architectures, the ResNet-18 backbone offered the best performance. The multi-modal deep learning framework achieved a 5-fold cross-validation C-index ranging from 0.75 to 0.94, outperforming models incorporating imaging only (0.70–0.89) and clinical features only (0.53–0.65). Kaplan-Meir survival analysis performed on the deep learning-derived predictions demonstrated clear risk stratification, with a median PSA progression free survival (PFS) of 19.7 months in the high-risk group and 26 months in the low-risk group (P < 0.001). Deep learning-derived imaging biomarker based on PSMA PET/CT can effectively predict PSA PFS for mPCa patients. Further clinical validation in prospective cohorts is warranted. Competing Interest Statement Dr. Martin Pomper is a coinventor on a U.S. patent covering PYLARIFY and, as such, is entitled to a portion of the licensing fees and royalties generated by this technology. This arrangement has been reviewed and approved by Johns Hopkins University in accordance with its conflict-of-interest policies. Dr. Tian Zhang reports receiving research funding (as site principal investigator) from Merck, Janssen, AstraZeneca, Pfizer, Astellas, Eli Lilly, Tempus, ALX Oncology, Janux Therapeutics, OncoC4, and Bayer. Dr. Zhang has also served on advisory boards for Merck, Exelixis, Sanofi-Aventis, Janssen, AstraZeneca, Pfizer, Amgen, Bristol Myers Squibb, Eisai, Aveo, Eli Lilly, Bayer, Gilead, Novartis, EMD Serono, Dendreon, and Xencor. Additionally, Dr. Zhang has received consulting fees and honoraria from MJH Associates, Vaniam, Aptitude Health, PeerView, Mashup, Bonum CE, and SignifyMD. Dr. Zhang's spouse is a founder of Capio Biosciences and Archimmune Therapeutics. Dr. Orhan Oz serves as principal investigator (PI) on several industry-sponsored research trials, including those funded by Novartis, Telix Pharmaceuticals, Fusion Pharmaceuticals, Clarity Pharmaceuticals, and Point Biopharma. He also receives PI grant support from the American Diabetes Association (ADA) and serves as a co-investigator (Co-I) on grants funded by the National Institutes of Health (NIH) and the Department of Defense (DOD). All other authors declare no competing interests. Funding Statement This work is supported by UT Southwestern Medical Center Dean's Clinical Scholar Award and the Dedman Scholar Award. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of the University of Texas Southwestern Medical Center gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The data are not publicly available due to privacy or ethical restrictions. The data underlying this article may be made available on request from the corresponding author.