Optimal 10-mm window: Integrating peri-ablation radiomics with preoperative features to predict early hepatocellular carcinoma recurrence after thermal ablation

Figure 6 Performance comparison on the external validation set of the seven machine learning models based on the “pre-operative + peri-necrotic 10-mm” feature set, using receiver operating characteristic curves. ROC: Receiver operating characteristic; XGBoost: Extreme gradient boosting; RF: Random forest; GBDT: Gradient boosting decision tree; SVM: Support vector machine; DT: Decision tree; LR: Logistic regression; KNN: K-nearest neighbors; AUC: Area under the curve.