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Articles Published Processes
8/20/2025 8:41:27 AM | Browse: 173 | Download: 466
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Received |
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2025-05-27 02:01 |
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Peer-Review Started |
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2025-05-27 02:01 |
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First Decision by Editorial Office Director |
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2025-06-05 10:15 |
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Return for Revision |
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2025-06-07 08:36 |
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Revised |
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2025-06-20 15:23 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-07-17 02:40 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-07-17 08:34 |
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Articles in Press |
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2025-07-17 08:34 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-07-31 01:27 |
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Publish the Manuscript Online |
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2025-08-20 08:41 |
| ISSN |
2218-4333 (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) 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 |
Multidisciplinary Sciences |
| Manuscript Type |
Letter to the Editor |
| Article Title |
Integrating tumor location into artificial intelligence-based prognostic models in cancer
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Chen Wang, Meng-Yan Chen, Yu-Gang Wang and Min Shi |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality |
No. 23ZR1458300 |
| Key Discipline Project of Shanghai Municipal Health System |
No. 2024ZDXK0004 |
| Doctoral Innovation Talent Base Project for Diagnosis and Treatment of Chronic Liver Diseases |
No. RCJD2021B02 |
| Pujiang Project of Shanghai Magnolia Talent Plan |
No. 24PJD098 |
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| Corresponding Author |
Min Shi, Chief Physician, MD, Professor, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai 200336, China. sm1790@shtrhospital.com |
| Key Words |
Tumor location; Prognosis; Artificial intelligence; Artificial intelligence-based prognostic tools; Clinical prediction models |
| Core Tip |
This study highlights the prognostic significance of tumor location in gastric cancer (GC), showing that proximal tumors are associated with worse survival outcomes. Gender differences, particularly in carbohydrate antigen 72-4 expression, further influence prognosis. The letter proposes integrating tumor location into artificial intelligence-based clinical prediction models to improve prognostic accuracy. It outlines a stepwise framework for model development, multicenter validation, and clinical implementation, while addressing critical technical, ethical, and interoperability challenges for real-world application. |
| Publish Date |
2025-08-20 08:41 |
| Citation |
Wang C, Chen MY, Wang YG, Shi M. Integrating tumor location into artificial intelligence-based prognostic models in cancer. World J Clin Oncol 2025; 16(8): 109934
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| URL |
https://www.wjgnet.com/2218-4333/full/v16/i8/109934.htm |
| DOI |
https://dx.doi.org/10.5306/wjco.v16.i8.109934 |
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