Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan

Aug 5, 2026

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The study compared radiomic analysis with machine learning models against a three-dimensional convolutional neural network (3D CNN) for differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs) using chest computed tomography. A retrospective cohort drew clinical variables and pathology-based labels, then separated unenhanced and contrast-enhanced CT datasets. Performance was assessed with macro F1-score and ROC analysis, showing consistent advantages for a LightGBM model with Extra Trees across both imaging settings.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Purpose

Methods

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Conclusion

TYPE Original Research PUBLISHED 18 April 2023 DOI 10.3389/fonc.2023.1105100 OPEN ACCESS EDITED BY Alla Reznik, Lakehead University, Canada REVIEWED BY YuChuan Hu, Fourth Military Medical University, China Wei Yang, Beijing Cancer Hospital, Peking University, China *CORRESPONDENCE Yi-Ting Yen [b85401067@gmail.com](b85401067@gmail.com)[ ](b85401067@gmail.com)Mi-Chia Ma [mcma@mail.ncku.edu.tw](mcma@mail.ncku.edu.tw) †These authors have contributed equally to this work and share ﬁrst authorship SPECIALTY SECTION This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology RECEIVED 22 November 2022 ACCEPTED 27 March 2023 PUBLISHED 18 April 2023 CITATION Chang C-C, Tang E-K, Wei Y-F, Lin C-Y, Wu F-Z, Wu M-T, Liu Y-S, Yen Y-T, Ma M-C and Tseng Y-L (2023) Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan. Front. Oncol. 13:1105100 . doi: 10.3389/fonc.2023.1105100 COPYRIGHT © 2023 Chang, Tang, Wei, Lin, Wu, Wu, Liu, Yen, Ma and Tseng. 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) and the copyright owner(s) 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. Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan Chao-Chun Chang 1†, En-Kuei Tang2†, Yu-Feng Wei 3,4†, Chia-Ying Lin 5†, Fu-Zong Wu6,7,8, Ming-Ting Wu 6,9,10, Yi-Sheng Liu 5, Yi-Ting Yen 1,11*, Mi-Chia Ma 12* and Yau-Lin Tseng 1 1 Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan, 2 Division of Thoracic Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 3School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan, 4 Division of Chest Medicine, Department of Internal Medicine, E-Da Cancer Hospital, Kaohsiung, Taiwan, 5 Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan, 6 Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 7 Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 8 Institute of Education, National Sun Yat-sen University, Kaohsiung, Taiwan, 9School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 10 Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 11 Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan, 12 Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan, Taiwan Purpose: To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs) . Methods: A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019 . Clinical

FAQs

What is the main goal of this research?

To compare diagnostic performance of radiomics-based machine learning versus a 3D CNN model for differentiating thymic epithelial tumors from other prevascular mediastinal tumors using chest CT.

How were the imaging datasets prepared for analysis?

The study used unenhanced CT (UECT) and contrast-enhanced CT (CECT) datasets, then built and evaluated radiomics and 3D CNN models on each dataset.

Which approach performed better according to the results?

Across both UECT and CECT datasets, the radiomics model using LightGBM with Extra Trees achieved higher macro F1-scores and ROC-AUC values than the 3D CNN model.

Aug 5, 2026

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