Morphology-Aware Prognostic Model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole-Slide Images: A Study Using Multi-Center Clinical Trial Cohort.

Colorectal cancer (CRC) remains the third most prevalent malignancy globally, with approximately 154,000 new cases and 54,000 projected deaths anticipated for 2025. The recent advancement of foundation models in computational pathology has been largely propelled by task-agnostic methodologies that overlook organ-specific crucial morphological patterns that represent distinct biological processes that fundamentally influence tumor behavior, therapeutic response, and outcomes. In this study, we develop a novel, interpretable AI model, PRISM (Prognostic Representation of Integrated Spatial Morphology), that incorporates a continuous variability spectrum within each distinct morphology to reflect the principle that malignant transformation occurs through incremental evolutionary processes. PRISM is trained on 15 million histological images extracted from surgical resection specimens of 2957 patients. PRISM achieved superior prognostic performance for five-year OS (AUC = 0.70 ± 0.04; accuracy = 68.37% ± 4.75%; HR = 3.21, 95% CI = 2.18-4.72; < 0.0001 using multi-variate cox-proportional hazards model), outperforming existing CRC-specific methods by 15% and AI foundation models by ~23% accuracy. It showed sex-agnostic robustness (AUC Δ = 0.02; accuracy Δ = 0.15%) and stable performance across clinicopathological subgroups, with minimal accuracy fluctuation (Δ = 1.44%) between 5FU/LV and CPT-11/5FU/LV regimens, replicating the Alliance cohort finding of no survival difference between treatments. These results establish PRISM as a promising, interpretable tool for AI-driven prognostication, with potential for future extension to other cancer types and stages.