Publication Name	World Journal of Gastroenterology
Manuscript ID	104466
Country	China

Received	2024-12-23 12:45
Peer-Review Started	2024-12-23 12:45
To Make the First Decision
Return for Revision	2025-02-13 23:37
Revised	2025-02-26 18:59
Second Decision	2025-03-19 02:38
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-03-19 07:38
Articles in Press	2025-03-19 07:38
Publication Fee Transferred	2025-03-03 03:12
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-03-21 07:37
Publish the Manuscript Online	2025-04-02 10:54

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/
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Category	Oncology
Manuscript Type	Retrospective Study
Article Title	Machine learning-based reconstruction of prognostic staging for gastric cancer patients with different differentiation grades: A multicenter retrospective study
Manuscript Source	Unsolicited Manuscript
All Author List	Yong-Le Zhang, Hai-Bin Song and Ying-Wei Xue
ORCID	Author(s) ORCID Number Ying-Wei Xue http://orcid.org/0000-0002-8427-9736
Funding Agency and Grant Number	Funding Agency Grant Number Nn10 program of Harbin Medical University Cancer Hospital No. Nn10 PY 2017-03
Corresponding Author	Ying-Wei Xue, Chief Physician, MD, PhD, Postdoctoral Fellow, Professor, Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin 150081, Heilongjiang Province, China. xueyingwei@hrbmu.edu.cn
Key Words	Gastric cancer; Machine learning; Positive lymph nodes ratio; Prognostic staging system; Tumor differentiation
Core Tip	This study introduces new machine learning-based gastric cancer (GC) staging systems, which incorporate the positive lymph node ratio and pT stages, tailored for well/moderately differentiated GC and poorly differentiated GC patients, respectively. These novel systems demonstrate superior prognostic accuracy compared to the traditional American Joint Committee on Cancer tumor node metastasis staging system, providing a more precise tool for predicting overall survival in resectable GC patients and guiding personalized treatment strategies.
Publish Date	2025-04-02 10:54
Citation	<p>Zhang YL, Song HB, Xue YW. Machine learning-based reconstruction of prognostic staging for gastric cancer patients with different differentiation grades: A multicenter retrospective study. <i>World J Gastroenterol</i> 2025; 31(13): 104466</p>
URL	https://www.wjgnet.com/1007-9327/full/v31/i13/104466.htm
DOI	https://dx.doi.org/10.3748/wjg.v31.i13.104466

Full Article (PDF)	WJG-31-104466-with-cover.pdf
Manuscript File	104466_Auto_Edited_074435.docx
Answering Reviewers	104466-answering-reviewers.pdf
Audio Core Tip	104466-audio.mp3
Biostatistics Review Certificate	104466-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	104466-conflict-of-interest-statement.pdf
Copyright License Agreement	104466-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	104466-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	104466-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	104466-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	104466-non-native-speakers.pdf
Supplementary Material	104466-supplementary-material.pdf
Peer-review Report	104466-peer-reviews.pdf
Scientific Misconduct Check	104466-scientific-misconduct-check.png
Scientific Editor Work List	104466-scientific-editor-work-list.pdf
CrossCheck Report	104466-crosscheck-report.pdf