Development and Validation of a Computational Histology Artificial Intelligence–Powered Biomarker in Metastatic Hormone-Sensitive Prostate Cancer on Randomized Phase III Trials

PURPOSE Metastatic hormone-sensitive prostate cancer (mHSPC) is a heterogeneous disease state with multiple treatment options. Biomarkers are needed for risk stratification and to guide treatment intensification or deintensification. We used a computational histology artificial intelligence–based platform (CHAI) to develop and validate a digital image–only prognostic biomarker in mHSPC with participant-level data from two prospective, phase III randomized controlled trials. METHODS The CHAI platform extracted quantitative histomorphologic features from whole-slide images of hematoxylin and eosin–stained diagnostic specimens. Data from CHAARTED were used to construct a signature of features associated with overall survival (OS). A continuous risk score was dichotomized into favorable- and unfavorable-risk groups. The performance of the locked model was assessed in ENZAMET as an independent validation cohort using Kaplan-Meier methods and multivariable Cox proportional hazards models. Institutional review board approval was obtained for each participating data set. Given all patient information was deidentified, the study was considered institutional review board–exempt and consent waived. RESULTS Overall, 1,191 participants were included: 507 in development (CHAARTED) and 684 in validation (ENZAMET). In the validation cohort, CHAI unfavorable-risk participants had worse OS (hazard ratio [HR], 2.6 [95% CI, 2.0 to 3.4]; P < .001) and progression-free survival (PFS; HR, 2.2 [95% CI, 1.7 to 2.7]; P < .001). Five-year OS was 39% for unfavorable-risk versus 69% for favorable-risk participants. In adjusting for clinical variables, the biomarker remained prognostic for OS and PFS ( P < .001). Among those who received doublet (androgen-deprivation therapy [ADT] plus androgen receptor pathway inhibitor [ARPI]) versus triplet systemic therapy (ADT plus docetaxel plus ARPI), there was a significant interaction between biomarker and treatment intensification for OS ( P = .003) and PFS ( P < .001), suggesting that patients at higher risk may benefit more from triplet therapy. CONCLUSION We developed and validated an image-only AI-based biomarker associated with clinical outcomes and potential benefit from treatment escalation in mHSPC independent of conventional clinicopathologic risk factors.