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Articles Published Processes
5/15/2025 10:28:51 AM | Browse: 35 | Download: 90
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Received |
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2024-12-04 08:23 |
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Peer-Review Started |
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2024-12-04 08:23 |
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To Make the First Decision |
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Return for Revision |
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2025-02-14 00:41 |
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Revised |
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2025-02-20 12:29 |
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Second Decision |
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2025-02-26 02:39 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-02-26 07:11 |
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Articles in Press |
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2025-02-26 07:11 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2025-03-04 00:14 |
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Typeset the Manuscript |
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2025-04-10 03:37 |
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Publish the Manuscript Online |
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2025-05-15 10:28 |
| 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: 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 |
Gastroenterology & Hepatology |
| Manuscript Type |
Systematic Reviews |
| Article Title |
Predicting gastric cancer survival using machine learning: A systematic review
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Hong-Niu Wang, Jia-Hao An, Fu-Qiang Wang, Wen-Qing Hu and Liang Zong |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Liang Zong, MD, PhD, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, The Affiliated Hospital of Changzhi Medical College, No. 502 Changxing Middle Road, Changzhi 046000, Shanxi Province, China. 250537471@qq.com |
| Key Words |
Gastric cancer; Machine learning; Deep learning; Survival prediction; Artificial intelligence |
| Core Tip |
Machine learning offers significant promise for predicting gastric cancer patients' survival, but challenges such as data quality, model interpretability, and generalizability must be addressed. This review highlights the importance of integrating diverse data types, robust data preprocessing, and advanced feature-selection techniques to improve prediction accuracy. While open-access and private datasets each have their advantages, ensuring the timeliness and relevance of data is essential for the development of clinically applicable models. |
| Publish Date |
2025-05-15 10:28 |
| Citation |
<p>Wang HN, An JH, Wang FQ, Hu WQ, Zong L. Predicting gastric cancer survival using machine learning: A systematic review. <i>World J Gastrointest Oncol</i> 2025; 17(5): 103804</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i5/103804.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i5.103804 |
© 2004-2025 Baishideng Publishing Group Inc. All rights reserved. 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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