Abstract
This study is aimed to develop predictive models for classifying thymic epithelial tumor (TET) histological subtypes (A/AB/B1, B2/B3, C) and WHO stages (I-IV) using radiomics features derived from contrast-enhanced CT scans. These models were validated on multicenter external datasets to improve preoperative diagnosis and guide treatment decisions. A total of 257 patients diagnosed with TET between January 2013 and April 2024 were retrospectively analyzed, with 181 cases from the First Affiliated Hospital of Soochow University served as the training cohort and 76 cases from the Second Affiliated Hospital used as an external test set. All patients underwent preoperative enhanced CT scans. After manual segmentation of the volume of interest (VOI), 1,038 radiomic features were extracted. Feature selection was performed using PCA and LASSO methods. Three models (clinical semantic, radiomics, and a fusion model combining both) were built using random forest algorithms. The fusion model achieved the highest performance in the external test set, with an accuracy of 0.908 and F1 score of 0.896 for histological subtype classification, and an accuracy of 0.803 and F1 score of 0.833 for WHO staging. The radiomics model shows slightly lower performance, while the clinical semantic model performs the weakest. Our findings suggest that machine learning models integrating radiomics and clinical features can effectively predict TET subtypes and stages, offering a non-invasive tool for accurate preoperative assessment with strong generalization ability.