Development and validation of computational histology artificial intelligence (CHAI)–powered prognostic and predictive biomarkers in metastatic hormone-sensitive prostate cancer (mHSPC) using ENZAMET and CHAARTED prospective randomized phase 3 trials (RCT).

231 Background: Advanced biomarkers (BM) for mHSPC are needed to improve personalized treatment. The CHAI platform applies deep learning to extract quantitative histologic features from H 584 ENZAMET; and 88 RWD. In val, for ProgPC, unfavorable pts had worse PFS NCT02446405 . Biomarker Dev Val Biomarker N (%) OS HR (95%CI) P Interaction P ProgPC CHAARTED ENZAMET Favorable 465 (80) Unfavorable 119 (20) 2.9 (2.2-3.8) <0.01* PredDoce ENZAMET(no enzalutamide arm) CHAARTED Benefit 260 (60) 0.60 (0.41-0.87) <0.01 0.02** Less benefit 163 (40) 1.03 (0.69-1.55) 0.8 PredARPI RWD + ENZAMET(15%) ENZAMET(85%) Benefit 224 (65) 0.5 (0.32-0.97) <0.01 0.01 Less benefit 121 (35) 1.22 (0.70-2.12) 0.5 *Control for age, ECOG, Gleason, PSA, treatment, volume (low/high), timing (metachronous/synchronous). **Control for volume*treatment, and timing*treatment interaction.