Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics

Figure 1 The workflow of radiomics analysis. T2WI: T2-weighted imaging.

Figure 2 The names and descriptions of the selected features.

Figure 3 Performance evaluation of radiomics models. A and B: Receiver operating characteristic curve analysis comparing model performance in the training (A) and validation cohort (B). AUC: Area under the curve; KNN: K-nearest neighbor; GBM: Light gradient boosting machine; LR: Logistic regression; MLP: Multilayer perceptron; SVM: Support vector machines; XGBoost: Extreme gradient boosting.

Figure 4 Decision curve analysis demonstrating clinical utility across probability thresholds in the validation cohort. The Y-axis indicates net benefit standardized by patient population. The blue curve corresponds to the radiomics model, the black solid curve represents the all-patients-treated scenario, and the black dashed line indicates the no-patients-treated scenario. DCA: Decision curve analysis.