Fig. 6: Model development pipeline.
From: Interpretable deep learning for multicenter gastric cancer T staging from CT images
A Portal-venous CT slice showing the largest tumor cross-section. B GTRNet architecture: modified ResNet-152 with parallel max-pool and center-crop branches. C End-to-end workflow linking the deep-learning Radscore to a clinical–radiomic nomogram. D Summary of model evaluation metrics, including ROC curve, confusion matrix, calibration curve, and decision curve analysis (DCA). In the DCA plot, the y-axis represents the net benefit and the x-axis represents the threshold probability, with comparisons among the treat-all, treat-none, and EUS-prediction strategies.
