Development and validation of a postoperative prediction model for residual stones after hepatectomy in patients with hepatolithiasis

Figure 4 Discriminative performance and calibration curves of the least absolute shrinkage and selection operator-logistic regression model. A: Receiver operating characteristic curves in the development cohort evaluated via out-of-fold (OOF) cross-validation; B: Receiver operating characteristic curves in the independent held-out validation cohort (n = 89). Shaded regions represent 95% confidence intervals (CIs) for the least absolute shrinkage and selection operator-logistic regression model, with comparator models (gradient boosting machine, eXtreme Gradient Boosting, and support vector machine with radial basis function kernel) shown for reference; C: Calibration plot in the development cohort based on OOF predictions; D: Calibration plot in the held-out validation cohort. The dashed 45-degree line represents perfect calibration. Solid colored lines and scatter points indicate observed vs predicted residual stone probabilities with bootstrap-derived 95%CIs. LASSO-LR: Least absolute shrinkage and selection operator-logistic regression; ROC: Receiver operating characteristic; XGBoost: EXtreme Gradient Boosting; GBM: Gradient boosting machine; AUC: Area under the receiver operating characteristic curve.