Related Experiment Video
Updated: Jun 8, 2026
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Published on: June 10, 2025
Multimodal artificial intelligence prediction of abiraterone efficacy in two STAMPEDE phase III trials of
C T A Parker1, H-C Huang2, E Grist1
1Cancer Institute, University College London, London, UK.
Background:
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 potential toxicity and cost of abiraterone, a predictive biomarker to refine patient selection is needed. We evaluated a digital pathology multimodal artificial intelligence (MMAI) model, previously validated as a prognostic biomarker, for prediction of abiraterone benefit among nonmetastatic clinically very high-risk prostate cancer.
Patients And Methods:
MMAI scores were generated for patients enrolled in two sequential abiraterone trials (no shared controls) in the STAMPEDE (Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficacy) platform protocol (NCT00268476) using digital pathology images, prostate-specific antigen, tumor stage, and age. We applied the previously established 75th percentile threshold to classify patients as MMAI very high-risk or standard high-risk. The primary endpoint was metastasis-free survival (MFS). Treatment effects and risk estimates were obtained using Cox regression and Kaplan-Meier method, respectively. Prediction was assessed using a treatment-by-biomarker interaction Cox model.
Results:
In total, 1137 patients randomly assigned to LT-ADT (N = 583) or LT-ADT with abiraterone (N = 554) were included. The MMAI very high-risk group (N = 268) demonstrated significant MFS improvement from adding abiraterone [hazard ratio (HR) 0.47, 95% confidence interval (CI) 0.31-0.70], with 5-year MFS increasing from 62% (95% CI 54% to 70%) in LT-ADT to 81% (95% CI 74% to 88%) in LT-ADT with abiraterone. Limited abiraterone benefit was observed in the MMAI standard high-risk group (N = 869; HR 0.83, 95% CI 0.63-1.09), with a 5-year MFS of 82% (95% CI 78% to 85%) versus 84% (95% CI 80% to 87%, interaction P-value = 0.02). This differential effect was consistent in local node-negative and node-positive subgroups.
Conclusion:
In this post hoc study of randomized clinical trial data, a locked digital pathology MMAI test displayed a strong prognostic association and predicted abiraterone efficacy in very high-risk, nonmetastatic prostate cancer. This biomarker could be implemented clinically to maximize benefit from treatment intensification while avoiding unnecessary toxicity.
