Endoscopic ultrasound-based deep learning for predicting chemotherapy response in unresectable pancreatic ductal adenocarcinoma

Figure 3 Comparison of receiver operating characteristic performance between the deep learning models and the carbohydrate antigen 19-9-based model. Receiver operating characteristic curves comparing the predictive performance of four endoscopic ultrasound-based convolutional neural network models and the carbohydrate antigen 19-9-based model (random forest) in the independent test cohort are shown. The corresponding area under the receiver operating characteristic curve (AUC) values for each model are indicated in the figure legend. ResNet50 (AUC = 0.844) and ResNeXt50 (AUC = 0.848) achieved higher AUC values than the carbohydrate antigen 19-9-based random forest model (AUC = 0.673), indicating superior discriminatory performance for predicting chemotherapy response. AUC: Area under the receiver operating characteristic curve; ML: Machine learning; ROC: Receiver operating characteristic.