| ISSN |
1948-5204 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Ming-Cheng Liu, Yung-Yin Cheng, Shao-Chieh Lin, Chih-Hung Lin, Cheng-Yen Chuang, Wen-Hsien Chen, Chun-Han Liao, Chia-Hong Hsieh, Mei-Fang Hsieh and Yi-Jui Liu |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Taiwan National Science and Technology Council |
NSTC114-2221-E-035-036 |
| Taichung Veterans General Hospital/Feng Chia University Joint Research Program |
TCVGH-FCU1148207 |
|
| Corresponding Author |
Yi-Jui Liu, PhD, Professor, Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Road, Seatwen, Taichung 407, Taiwan. erliu@fcu.edu.tw |
| Key Words |
Esophageal cancer; Radiomics; Body composition; Computed tomography image; Sarcopenia; Machine learning |
| Core Tip |
This study introduces a novel prognostic approach using radiomics and body composition analysis features extracted at the T12 vertebral level from pretreatment and follow-up computed tomography scans in esophageal cancer patients. Unlike conventional methods relying on L3-level imaging, this model incorporates T12-based skeletal muscle and adipose metrics - readily available in standard chest computed tomography - combined with clinical data to predict overall survival. The combined model incorporating clinical, body composition analysis, and radiomic data achieved excellent prognostic accuracy (area under the time-dependent receiver operating characteristic curve = 0.91) in 2-year survival prediction. This method supports non-invasive, automated, and personalized risk stratification, especially when follow-up imaging lacks L3 coverage. |
| Publish Date |
2025-12-11 08:20 |
| Citation |
Liu MC, Cheng YY, Lin SC, Lin CH, Chuang CY, Chen WH, Liao CH, Hsieh CH, Hsieh MF, Liu YJ. Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography. World J Gastrointest Oncol 2025; 17(12): 112873 |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i12/112873.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i12.112873 |
