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Objective: AI has demonstrated great promise in learning sophisticated features and relations in data that would otherwise remain hidden to the human eye. Here, we developed a proprietary AI-based multimodal approach to integrate clinical, digitized hematoxylin-eosin (H&E), and radiology bone scan (rBS) data for outcome prediction in ADT-treated nmCRPC patients. Identifying prostate cancer patients who may not benefit from ADT could improve the medical management of this disease beyond current definitive therapy. Methods: Patients in the ADT+placebo arm from SPARTAN clinical trial on nmCRPC with available clinical, H&E, and rBS were used (n=154). These patients were randomly divided into 70% (n=107) discovery and 30% (n=47) hold-out test datasets. Using the discovery set, we developed and trained a multimodal approach that combines survival convolutional neural networks (SCNNs1) and Cox proportional-hazards model (CPH) to learn ADT outcomes for overall survival (OS) and time to PSA progression (TTP) from the integration of imaging data and 11 traditional clinical features (e.g., tumor stage, Gleason score, PSA). The ability of the trained framework in predicting outcomes and risk stratification was evaluated on the hold-out set. Bootstrap analysis with Wilcoxon signed rank test was used to determine the significance of the multimodal framework’s performance improvement compared to clinical CPH. Results: The multimodal framework was predictive of ADT outcomes for OS and TTP in nmCRPC patients. In SPARTAN’s hold-out set, the multimodal framework significantly improved the predictive power of clinical CPH by 14%—16% across both outcomes (Wilcoxon signed rank P Conclusion: AI-based framework that learns from the integration of different data types improves outcome prediction in ADT-treated nmCRPC. The multimodal approach demonstrates promise in treatment decision support for the early use of androgen receptor-directed therapy and patient selection for clinical trials with novel treatment combinations. Reference: 1. Mobadersany, Pooya, et al. "Predicting cancer outcomes from histology and genomics using convolutional networks." Proceedings of the National Academy of Sciences 115.13 (2018): E2970-E2979. Conflict of interest statement P.M., J.L., D.G., C.A., S.M., S.B., M.K.Y., K.T., N.H., J.Z., J.G., N.K., and S.S.F.Y., are employees of Janssen Pharmaceutical, LLC. Citation Format: Pooya Mobadersany, Justin Lucas, Darshana Govind, Clemente Aguilar-Bonavides, Sharon McCarthy, Sabine Brookman-May, Margaret K. Yu, Ken Tian, Natalie Hutnick, Jose Zamalloa, Joel Greshock, Najat Khan, Stephen S.F. Yip. Artificial intelligence (AI)-based multimodal framework predicts androgen-deprivation therapy (ADT) outcomes in non-metastatic castration resistant prostate cancer (nmCRPC) from SPARTAN [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5053. |
