5023 Background: Reliable risk stratification across standard treatment pathways is essential for the clinical adoption of precision medicine biomarkers in localized prostate cancer. We previously developed and validated a multimodal artificial intelligence (MMAI) model that integrates digitized hematoxylin and eosin (H RP sHR 2.12, p < 0.001; RT sHR 2.73, p < 0.001). Both Image-only and MMAI scores were significantly associated with PCSM despite low event rates. Conclusions: Both Image-only and MMAI biomarkers demonstrate consistent prognostic performance across standard prostate cancer management strategies, including AS, RP, and RT, supporting their utility for risk stratification regardless of ultimate treatment selection. These findings highlight the robustness of image-derived prognostic information and demonstrate that routinely available H&E pathology alone captures clinically meaningful risk information that generalizes across treatment contexts. Together, these results support the use of AI-based digital pathology biomarkers for prognostication in localized prostate cancer.
Shen et al. (2026) studied this question.