Tertiary Lymphoid Structures as Predictors of Recurrence in Colorectal Cancer: Development and Validation of a Machine Learning‐Based Scoring Model
Xian‐Hua Lei, Rong Li, Dong‐Mei Wang, Li Liu, Zhi‐Qiang Wang, Hui‐Juan LiABSTRACT
Background
Tertiary lymphoid structures (TLS) are prognostic immune aggregates in the tumor microenvironment, but the value of location‐specific TLS features for predicting colorectal cancer (CRC) recurrence remains unclear. This study developed and internally validated a machine‐learning (ML) model integrating TLS features for recurrence prediction in stage II‐III CRC.
Methods
We retrospectively included 224 patients with stage II‐III CRC after curative resection and split them into training ( n = 156) and held‐out internal validation ( n = 68) cohorts. TLS at intratumoral, invasive‐front, and peritumoral sites were semi‐quantitatively scored by two blinded pathologists. LASSO selected predictors from 39 candidate variables. Five ML algorithms were trained with stratified cross‐validation, predefined tuning, and training‐only preprocessing. Performance was evaluated using discrimination, calibration, decision‐curve/clinical‐impact analyses, reclassification indices, SHAP interpretation, and Kaplan–Meier risk stratification.
Results
Recurrence occurred in 89 patients (39.7%). LASSO retained peritumoral TLS score, invasive‐front TLS score, and preoperative CEA. TLS scoring showed high interobserver agreement (weighted kappa: 0.82–0.86). LightGBM provided the most balanced performance, with AUCs of 0.872 (95% CI: 0.814–0.923) in training and 0.718 (95% CI: 0.572–0.839) in internal validation. SHAP ranked invasive‐front TLS score as the most influential predictor. The Youden cutoff (0.5017) separated high‐ and low‐risk groups with significantly different disease‐free survival (log‐rank p < 0.0001; hazard ratio = 5.132, 95% CI: 3.263–8.073). In exploratory comparison, validation AUCs were 0.727 for TLS‐only, 0.542 for clinical‐only, and 0.737 for combined models.
Conclusions
A TLS‐based ML model showed moderate internal validation performance for CRC recurrence prediction. Location‐specific TLS features may support postoperative risk stratification, but external multicenter and molecularly integrated validation is required before clinical use.