Redefining Prognostic Risk in Colorectal Cancer: Calibrated Deep Learning Reclassifies High-Risk Mortality and Mitigates Overtreatment

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

Accurate prognostic risk stratification is critical for colorectal cancer (CRC), yet traditional linear models are limited by complex non-linear multi-omics interactions. We compared three mainstream survival models (regularized Cox, random survival forests [RSF], and DeepSurv) via multi-scenario simulations spanning linear to strongly non-linear risks, with rigorous validation in TCGA (n = 610) and independent GEO (n = 566) cohorts using a five-dimensional evaluation framework, plus blinded isotonic regression for model calibration. DeepSurv showed significant predictive superiority in non-linear scenarios, achieving a global C-index of 0.7820 in TCGA (vs 0.7610 for regularized Cox), 42.18% net reclassification improvement for high-risk mortality patients, and 20.60% reduced prediction error after calibration, with robust external validation performance. The regularized Cox model remained robust for linear low-dimensional data. In conclusion, calibrated DeepSurv is optimal for high-risk CRC identification in complex multi-omics data, providing a standardized paradigm for survival model selection.

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