Development and internal validation of an explainable machine learning model for predicting textbook outcome after...

To develop and internally validate an explainable machine learning model for predicting textbook outcome (TO) after free flap reconstruction in oral cancer surgery.

This single-center retrospective study included 752 patients with oral cancer who underwent radical resection with immediate free flap reconstruction between August 2017 and August 2025. TO was defined as no 31-day mortality, no 31-day unplanned readmission, postoperative length of stay ≤ 21 days, no major complications, negative surgical margins, and adequate unilateral neck dissection (≥16 lymph nodes retrieved). Patients were randomly divided into a training cohort (n = 526) and a validation cohort (n = 226). Feature selection was performed using false discovery rate-based collinearity filtering and random forest recursive feature elimination. Seven machine learning models were developed and compared. SHapley Additive exPlanations (SHAP) were used to improve interpretability.

The overall TO achievement rate was 38.2%. Among all candidate models, XGBoost showed the best performance in the validation cohort, with an area under the receiver operating characteristic curve of 0.843 (95% CI, 0.790-0.890), an accuracy of 0.764, a sensitivity of 0.733, and a Brier score of 0.152. Albumin, age, operative time, tumor T stage, and neutrophil-to-lymphocyte ratio were the most important predictors. SHAP analysis identified clinically relevant threshold effects at age > 50 years, albumin > 45 g/L, and operative time > 400 min.

The XGBoost model demonstrated good performance for predicting TO after free flap reconstruction in oral cancer and may support perioperative risk stratification and individualized management.