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

Figure 1 Study inclusion and exclusion flowchart. HCC: Hepatocellular carcinoma; TA: Thermal ablation; US: Ultrasound; CEUS: Contrast-enhanced ultrasound.

Figure 2 Flowchart of the process from region of interest delineation on original ultrasound images to final prediction model construction. ROI: Region of interest; US: Ultrasound; CEUS: Contrast-enhanced ultrasound; SVM: Support vector machine; GBDT: Gradient boosting decision tree; XGBoost: Extreme gradient boosting; DT: Decision tree; RF: Random forest; LR: Logistic regression; KNN: K-nearest neighbors.

Figure 3 Pre-operative target lesion regions of interest were delineated and analyzed. A: Gray-scale ultrasound; B: The arterial phase of contrast-enhanced ultrasound; C: The early portal venous phase; D: The late portal venous phase; E: The delayed phase.

Figure 4 Delineation of the ablation zone margin and feature extraction from the peri-ablation region. Automated delineation of peri-ablation rims for feature extraction as yellow annuli. A: 5 mm peri-ablation rim; B: 10 mm peri-ablation rim; C: 15 mm peri-ablation rim; D: 20 mm peri-ablation rim.

Figure 5 Comparison of receiver operating characteristic curves for different models. A: Training set; B: Internal validation set; C: External validation set. AUC: Area under the curve.

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.

Figure 7 Decision curve analysis of the support vector machine model developed using the “pre-operative + peri-necrotic 10-mm” feature set. A: Decision curves on the training set; B: Decision curves on the internal validation set; C: Decision curves on the external validation set.

Figure 8 Model calibration was evaluated in the training and external validation set. SVM: Support vector machine.

Figure 9 Feature selection and visualization of radiomics showed the “pre-operative + peri-necrotic 10-mm” model’s interpretation. A: The importance ranking of the 15 factors based on the support vector machine classifier; B: SHapley Additive exPlanations values showed the model interpretation, the red part represented the higher values. SHAP: SHapley Additive exPlanations.