Multimodal artificial intelligence prediction of abiraterone efficacy in nonmetastatic very high-risk prostate cancer

Long-term androgen deprivation therapy (LT-ADT) with radiotherapy is standard of care for high-risk localized prostate cancer, with abiraterone added for clinically very high-risk disease. Given the toxicity and cost of abiraterone, a predictive biomarker to refine patient selection is needed. Post hoc analysis of two sequential abiraterone trials in the STAMPEDE platform control evaluated a digital pathology multimodal artificial intelligence (MMAI; based on digital pathology images, PSA, tumor stage, and age) model for prediction of abiraterone efficacy. The primary endpoint was metastasis-free survival (MFS). In total, 1137 patients randomized to LT-ADT (n=583) or LT-ADT plus abiraterone (n=554) were included. The MMAI very high-risk group (n=268) demonstrated signifcant MFS improvement from adding abiraterone (HR 0.47; 95% CI 0.31-0.70), with 5-year MFS rate increasing from 62% with LT-ADT to 81% with LT-ADT plus abiraterone. In the MMAI standard high-risk group (n=869), limited abiraterone benefit was observed (HR 0.83; 95% CI 0.63-1.09) with a 5-year MFS rate of 82% versus 84%. This differential effect was consistent in local node-negative and node-positive subgroups. The authors conclude that the MMAI test displayed a strong prognostic association and predicted abiraterone efficacy in very high-risk, non-metastatic prostate cancer.