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5/15/2025 10:28:51 AM | Browse: 23 | Download: 47
Publication Name World Journal of Gastrointestinal Oncology
Manuscript ID 103804
Country China
Received
2024-12-04 08:23
Peer-Review Started
2024-12-04 08:23
To Make the First Decision
Return for Revision
2025-02-14 00:41
Revised
2025-02-20 12:29
Second Decision
2025-02-26 02:39
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief
2025-02-26 07:11
Articles in Press
2025-02-26 07:11
Publication Fee Transferred
Edit the Manuscript by Language Editor
2025-03-04 00:14
Typeset the Manuscript
2025-04-10 03:37
Publish the Manuscript Online
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
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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
Manuscript Source Invited Manuscript
All Author List Hong-Niu Wang, Jia-Hao An, Fu-Qiang Wang, Wen-Qing Hu and Liang Zong
ORCID
Author(s) ORCID Number
Hong-Niu Wang http://orcid.org/0000-0003-2572-4868
Jia-Hao An http://orcid.org/0009-0006-3018-4295
Wen-Qing Hu http://orcid.org/0000-0003-3364-1034
Liang Zong http://orcid.org/0000-0003-4139-4571
Funding Agency and Grant Number
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
Full Article (PDF) WJGO-17-103804-with-cover.pdf
PRISMA 2009 Checklist 103804-PRISMA-2009-Checklist.pdf
Manuscript File 103804_Auto_Edited_034215.docx
Answering Reviewers 103804-answering-reviewers.pdf
Audio Core Tip 103804-audio.m4a
Biostatistics Review Certificate 103804-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form 103804-conflict-of-interest-statement.pdf
Copyright License Agreement 103804-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate 103804-non-native-speakers.pdf
Supplementary Material 103804-supplementary-material.pdf
Peer-review Report 103804-peer-reviews.pdf
Scientific Misconduct Check 103804-scientific-misconduct-check.png
Scientific Editor Work List 103804-scientific-editor-work-list.pdf
CrossCheck Report 103804-crosscheck-report.pdf