Development of multi-algorithm machine learning models integrating novel serum biomarkers for survival prediction in colorectal cancer: a retrospective cohort study

Abstract

Background Colorectal cancer (CRC) prognosis prediction remains challenging due to tumor heterogeneity, and existing machine learning-based prognostic studies often rely on single algorithms or limited feature types. This study aims to employ a multi-algorithm collaborative strategy to identify critical prognostic factors for CRC patients and develop integrated prognostic nomograms that combine multi-dimensional features. Methods Deep machine learning analysis was conducted in the R environment using the randomForest, glmnet (for LASSO regression), and caret packages. Three algorithms (LASSO regression, XGBoost, Random Forest) were applied to screen 73 clinicopathological features, with key hub features identified as the intersection of results from the three methods. Survival analysis was performed using the Kaplan-Meier method (log-rank test), and Cox regression analysis confirmed independent prognostic associations. Nomogram performance was validated via calibration curves, time-dependent AUC, C-index, decision curve analysis (DCA), and internal validation cohorts. Results The multi-algorithm intersection identified 6 key hub features encompassing pathological (M stage, N stage), serological (Cystatin C, Homocysteine, γ-Glutamyl Transferase), and demographic (age) factors. Cox regression confirmed these as independent predictors of overall survival (OS) and progression-free survival (PFS) in CRC patients. Prognostic nomograms developed for 1–5 year OS and PFS showed excellent calibration (consistent predicted vs. observed survival) and high accuracy (OS C-index: 0.730, 95% CI: 0.707-0.753; PFS C-index: 0.722, 95% CI: 0.700-0.744; AUCs >75% for OS, >77% for PFS). DCA demonstrated superior clinical utility of the nomograms over traditional pathological staging. Internal validation cohorts confirmed robust predictive performance. Conclusions Our study innovatively uses a multi-algorithm cross-validation strategy to identify 6 multi-dimensional hub features. It is the first to comprehensively integrate clinical, pathological, serological, and demographic factors into CRC prognostic nomograms, which outperform traditional staging and enable more personalized, clinically actionable prognosis prediction for CRC patients.