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Articles Published Processes
10/14/2025 7:42:35 AM | Browse: 29 | Download: 29
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Received |
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2025-06-30 06:40 |
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Peer-Review Started |
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2025-06-30 06:40 |
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To Make the First Decision |
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Return for Revision |
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2025-07-04 11:12 |
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Revised |
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2025-07-15 15:31 |
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Second Decision |
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2025-09-02 02:37 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-09-02 05:51 |
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Articles in Press |
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2025-09-02 05:51 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2025-09-07 17:52 |
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Typeset the Manuscript |
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2025-09-23 12:36 |
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Publish the Manuscript Online |
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2025-10-14 07:42 |
| 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 |
Minireviews |
| Article Title |
Radiomics meets sarcopenia: Machine learning-based multimodal modeling for esophageal cancer outcomes
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Cheng-Ming Peng, Chun-Wen Chen, Chia-Hong Hsieh, Yung-Yin Cheng, Chun-Han Liao, Mei-Fang Hsieh, Shao-Chieh Lin, Ming-Cheng Liu and Yi-Jui Liu |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Feng Chia University/Chung Shan Medical University |
FCU/CSMU 112-001 |
| Taiwan National Science and Technology Council |
111-2314-B-035-001-MY3 |
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| Corresponding Author |
Yi-Jui Liu, PhD, Professor, Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Road, Taichung 407, Taiwan. erliu@fcu.edu.tw |
| Key Words |
Esophageal cancer; Gastroesophageal cancer; Sarcopenia; Radiomics; Machine learning; Outcome prediction |
| Core Tip |
This review highlights recent advances in machine learning models that integrate radiomics and sarcopenia biomarkers for outcome prediction in esophageal and gastroesophageal cancers. Multimodal models consistently outperform single-feature models, offering more accurate and personalized prognostic assessments. The integration of radiomics-derived tumor features and sarcopenia-related body composition indices offers deeper insights into tumor biology and patient health status. However, achieving clinical translation requires addressing methodological variability and ensuring rigorous validation. These advanced imaging analytics hold significant promise for personalized patient management in esophageal cancer. |
| Publish Date |
2025-10-14 07:42 |
| Citation |
<p>Peng CM, Chen CW, Hsieh CH, Cheng YY, Liao CH, Hsieh MF, Lin SC, Liu MC, Liu YJ. Radiomics meets sarcopenia: Machine learning-based multimodal modeling for esophageal cancer outcomes. <i>World J Gastrointest Oncol</i> 2025; 17(10): 111399</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i10/111399.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i10.111399 |
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