ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1616816
This article is part of the Research TopicRadiomics and AI-Driven Deep Learning for Cancer Diagnosis and TreatmentView all 6 articles
Contrast-enhanced CT-based deep learning model assists in preoperative risk classification of thymic epithelial tumors
Provisionally accepted
Xuhui Zhao¹1Lingyu Zhang2Li Liang2Qi Zhang2Wencan Wang2Junlin Li3
Hua Zhang2
Chunhai Yu4*
Lingjie Wang2*- 1Department of Medical Imaging, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China
- 2Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- 3Department of Medical Imaging, Inner Mongolia People's Hospital, Hohhot, Inner Mongolia Autonomous Region, China
- 4Department of Medical Imaging, Shanxi Provincial Cancer Hospital, Taiyuan, Shanxi Province, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
This 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. Methods Involved 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. Results The 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). Conclusions This 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.
Keywords: deep learning, Convolutional neural network (CNN), Radiomics, Thymic Epithelial Tumors, computed tomography
Received: 23 Apr 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Zhao¹, Zhang, Liang, Zhang, Wang, Li, Zhang, Yu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Chunhai Yu, Department of Medical Imaging, Shanxi Provincial Cancer Hospital, Taiyuan, Shanxi Province, China
Lingjie Wang, Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.