Association of a novel AI-based digital pathology (CHAI) biomarker with progression free survival (PFS) in patients (pts) with metastatic hormone sensitive prostate cancer (mHSPC).

204 Background: In mHSPC, no baseline biomarker reliably predicts progression-free survival (PFS). Identifying pts unlikely to benefit from current therapies could enable personalized management and better pt selection for clinical trials. Herein, we investigate the capacity of a novel AI-based digital pathology platform to prognosticate in mHSPC. Methods: Pts diagnosed with mHSPC with available pathology slides and undergoing systemic therapy at the Huntsman Cancer Institute, University of Utah were eligible. Whole slide images were generated from digital scans of diagnostic H&E histopathology specimens reviewed by board-certified genitourinary pathologists. The CHAI (Correlative Histologic AI) platform was used to segment nuclei and extract morphologic and spatial features. Association with PFS was assessed across extracted features, and a biomarker signature was generated. Kaplan Meier estimators, log-rank tests and t-tests were used to compare outcomes among those with and without the biomarker signature for time-to-event and binarized endpoints respectively. PFS was defined from the start of therapy for mHSPC to biochemical or radiographic progression (per PCWG2) or death. Multivariate analysis for PFS was conducted using the Cox proportional hazards model, evaluating the biomarker signature and adjusting for age, Gleason score, baseline PSA, de novo status, disease volume, and ADT intensification. Results: 86 pts were eligible and included. Median age: 65 years (IQR 60-72), median baseline PSA: 29.9 ng/ml (IQR 7.2-23.2) and 64% pts received ADT intensification. 43 pts were categorized as biomarker positive (+) and 43 as biomarker negative (-). Biomarker + pts had a significantly longer median PFS of 67.5 months as compared to 26.9 months in biomarker – pts (HR 0.39, 95% CI HR 0.29 - 0.52, p < 0.001). Biomarker + group was significantly associated with PSA nadir < 0.2 ng/ml (p < 0.001). On multivariate analysis, biomarker + signature was significantly associated with better PFS (Table). Conclusions: CHAI, a novel AI-based digital pathology biomarker is independently associated with PFS in mHSPC setting. After external validation it may serve as a prognostic tool to guide clinical decision-making. Further analysis with a larger sample size will be conducted to evaluate the association with OS. This analysis exemplifies the power of integrating artificial intelligence into medical practice, with a potential to improve outcomes in our patients. Characteristic PFS HR (95% CI) p value Biomarker (+ vs. -) 0.47 (0.24 – 0.91) 0.025 ADT intensification (Yes vs. No) 0.3 (0.16 – 0.7) <0.001 Disease Volume (Low vs. High) 0.37 (0.19 – 0.7) 0.003 Gleason score 1.56 (1.07 – 2.27) 0.021 Age 0.97 (0.93 – 1.00) 0.075 Baseline PSA 1.00 (1.00 – 1.00) 0.061 De novo (Yes vs. No) 1.1 (0.53 – 2.29) 0.793