Fig. 2.

The overall pipeline of this study. A The framework takes an arterial CT scan as input and includes a three-dimensional (3D) tumor area segmentation network with nnU-Net architecture. We obtained voxel-level segmentation labels and cropped the three-dimensional ROI of the tumor. B We extracted the radiomic features of the ROI. The LASSO method was used to select the features. Following feature selection, we constructed an MLP Classifier based on the radiomic features and clinical characteristics. C We utilized deep transfer learning technology based on the 2D ResNet50 network. LASSO was applied in sequence for dimensionality reduction and feature selection. Finally, an MLP Classifier was constructed based on the 2D deep features and clinical characteristics. D We utilized deep transfer learning technology based on the 3D ResNet50 network. The pre-trained weights were obtained from 3D MedicalNet, a medical network, and used to extract the 3D deep-learning features of the ROI. Subsequently, LASSO was applied in sequence for dimensionality reduction and feature selection. Finally, an MLP Classifier was constructed based on the 3D deep features and clinical characteristics. E In the testing cohort, the predicted probabilities of high and low risk of tumor were the outputs. We performed result analysis using ROC curves, the AUC, and decision curve analysis