An exploratory multimodal pipeline for recurrence prediction in CRC with XELOX therapy

Colorectal cancer(CRC) recurrence remains a challenge despite curative surgery and adjuvant therapy. Integrating clinicopathological and liquid biopsy data may improve risk stratification, but evidence in small‑sample settings is lacking. This exploratory study assessed the feasibility of a multimodal machine learning pipeline for recurrence prediction in a well‑defined XELOX‑treated cohort. We analyzed 86 CRC patients(17 recurrences) receiving uniform XELOX adjuvant therapy. A three‑step pipeline was applied: (1) LASSO regression for coarse variable selection (10‑fold cross‑validation); (2) gradient boosting machine and random forest was then employed for further variable refinement; (3) The common variables selected by both methods was then used for a Firth penalized logistic regression model. The final model performance was evaluated using bootstrap‑corrected AUC, calibration plot, and decision curve analysis(DCA). The intersection of GBM and RF selected four predictors: ctDNA positivity, perineural invasion, high‑grade tumor budding, and T stage (T5 vs. T1-4). The final Firth model yielded an optimism‑corrected AUC of 0.695(95% bootstrap CI: 0.58-0.81). Wide confidence intervals (e.g., ctDNA OR = 3.95; 95% CI: 0.83-8.61) and an unexpected coefficient direction for T stage indicated statistical instability due to the limited event count. Although Calibration showed systematic deviations, DCA suggested potential net benefit for thresholds 0.10-0.30. This exploratory study demonstrates the feasibility of a multimodal machine learning pipeline for recurrence prediction in a small CRC cohort. All findings are hypothesis‑generating, and the model is not clinically ready. External validation in larger prospective cohorts is required before any clinical application can be considered.