Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography

Figure 4 Receiver operating characteristic curves of radiomics-based discrimination models developed using five machine learning algorithms (support vector machine, random forest, k-nearest neighbor, gradient boosting decision tree, and extreme gradient boosting). A: Pancreatic parenchyma in the training cohort; B: Pancreatic parenchyma in the test cohort; C: Peripancreatic necrotic collections in the training cohort; D: Peripancreatic necrotic collections in the test cohort; E: Combined pancreatic parenchyma with peripancreatic necrotic collections in the training cohort; F: Combined pancreatic parenchyma with peripancreatic necrotic collections in the test cohort. SVM: Support vector machine; AUC: Area under the curve; RF: Random forest; KNN: k-nearest neighbor; GBDT: Gradient boosting decision tree; XGBoost: Extreme gradient boosting.