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Articles Published Processes
9/19/2025 7:57:21 AM | Browse: 320 | Download: 50
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Received |
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2025-06-27 02:53 |
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Peer-Review Started |
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2025-06-27 02:53 |
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To Make the First Decision |
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Return for Revision |
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2025-07-15 10:31 |
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Revised |
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2025-07-28 12:28 |
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Second Decision |
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2025-08-21 02:40 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-08-21 06:26 |
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Articles in Press |
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2025-08-21 06:26 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-09-11 06:44 |
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Publish the Manuscript Online |
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2025-09-19 07:57 |
| ISSN |
1007-9327 (print) and 2219-2840 (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2024. 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 |
Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Atsushi Hirata, Koichi Hayano, Toru Tochigi, Yoshihiro Kurata, Tadashi Shiraishi, Nobufumi Sekino, Akira Nakano, Yasunori Matsumoto, Takeshi Toyozumi, Masaya Uesato and Gaku Ohira |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Koichi Hayano, FACS, MD, PhD, Department of Frontier Surgery, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba 260-8677, Japan. hayatin1973@yahoo.co.jp |
| Key Words |
Esophageal cancer; Diffusion weighted imaging; Chemoradiation therapy; Radiomics; Machine learning |
| Core Tip |
Accurately predicting pathological complete response (pCR) to chemoradiotherapy in esophageal squamous cell carcinoma remains a critical clinical challenge. This study introduces a novel artificial intelligence-based model leveraging radiomics features from pre-treatment diffusion-weighted magnetic resonance imaging. By integrating semi-automated three dimensions tumor segmentation with an automated machine learning framework, our model demonstrated high predictive accuracy for pCR (area under the curve = 0.85) and successfully stratified patients into distinct prognostic groups based on relapse-free survival. This non-invasive biomarker is a promising tool for constructing optimal treatment strategies, thereby advancing personalized medicine and significantly improving patient outcomes. |
| Publish Date |
2025-09-19 07:57 |
| Citation |
<p>Hirata A, Hayano K, Tochigi T, Kurata Y, Shiraishi T, Sekino N, Nakano A, Matsumoto Y, Toyozumi T, Uesato M, Ohira G. Predicting pathological complete response to chemoradiotherapy using artificial intelligence-based magnetic resonance imaging radiomics in esophageal squamous cell carcinoma. <i>World J Gastroenterol</i> 2025; 31(36): 111293</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i36/111293.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i36.111293 |
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