AUTHOR=Zhao Xuhui , Zhang Lingyu , Liang Li , Zhang Qi , Wang Wencan , Li Junlin , Zhang Hua , Yu Chunhai , Wang Lingjie 

TITLE=Contrast-enhanced CT-based deep learning model assists in preoperative risk classification of thymic epithelial tumors

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1616816

DOI=10.3389/fonc.2025.1616816

ISSN=2234-943X

ABSTRACT=BackgroundThis study aimed to develop and evaluate a deep learning (DL) model utilizing contrast-enhanced computed tomography (CT) to assist radiologists in accurately stratifying the risk of thymic epithelial tumors (TETs) based on the World Health Organization (WHO) classification.MethodsInvolved retrospectively enrolling clinical data from 266 patients with histopathologically confirmed TETs from two centers: Center 1 (training set, n=205) and Center 2 (external testing set, n=61). Six DL models (DenseNet 121, ResNet 101, Inception V3, VGG 11, MobileNet V2, and ShuffleNet V2) were developed and evaluated using venous-phase CT images, alongside a traditional radiomic model using a support vector machine (SVM) for comparison. Diagnostic performance of junior and senior radiologists in distinguishing low-risk thymoma (LRT) from high-risk thymoma (HRT) was assessed with and without the assistance of the optimal DL model.ResultsThe ResNet 101 model emerged as the best performer among six DL models, achieving an AUC of 0.876, accuracy of 0.820, sensitivity of 0.878, specificity of 0.700, positive predictive value of 0.857, and negative predictive value of 0.737 in the external testing set, outperforming the traditional radiomic model (AUC, p < 0.05). Notably, DL model significantly improved junior radiologists’ diagnostic performance, with an average AUC of 0.822, approaching senior radiologists’ average AUC of 0.859 (p > 0.05).ConclusionsThis study demonstrated that a DL model based on contrast-enhanced CT can reliably assist radiologists in preoperative risk stratification of TETs, bridging the diagnostic performance gap between junior and senior radiologists and supporting clinical decision-making.