Development and validation of an interpretable predictive model for mismatch between endoscopic ultrasonography and...

Endoscopic ultrasonography (EUS) is important for evaluating colorectal subepithelial lesions (SELs), but EUS-histopathology mismatch may complicate preoperative management. Evidence for predicting mismatch risk in colorectal SELs remains limited.

We retrospectively included patients with colorectal SELs who underwent EUS and had histopathological confirmation. Candidate predictors were selected using LASSO, and seven machine-learning algorithms were developed and compared. Performance was assessed by discrimination, calibration, decision curve analysis, and repeated stratified 10-fold cross-validation. SHAP was used for model interpretation, and the final model was deployed as a web-based calculator.

Among 301 SELs, 222 were concordant and 79 showed mismatch (26.2%). LASSO selected lesion location, echogenicity, and EUS layer of origin. The SVM model achieved the best overall test-set performance, with an AUC of 0.818 (95% CI, 0.673-0.964), accuracy of 0.883, sensitivity of 0.538, specificity of 0.979, PPV of 0.875, NPV of 0.885, F1-score of 0.667, and Brier score of 0.109. Repeated cross-validation yielded an SVM AUC of 0.713 (95% interval, 0.537-0.884). SHAP identified lesion location as the strongest contributor to mismatch risk, followed by echogenicity and layer of origin.

An interpretable SVM model based on routinely available EUS features may help identify colorectal SELs at higher risk of EUS-histopathology mismatch and support post-EUS risk alerting. Prospective multicenter validation is required before broader clinical implementation.