Publication Name	World Journal of Gastroenterology
Manuscript ID	99940
Country	China

Received	2024-08-03 10:07
Peer-Review Started	2024-08-03 10:07
To Make the First Decision
Return for Revision	2024-09-11 04:00
Revised	2024-09-25 01:57
Second Decision	2024-10-18 01:19
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-10-18 05:45
Articles in Press	2024-10-18 05:45
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-10-22 03:44
Publish the Manuscript Online	2024-10-31 10:42

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	Gastroenterology & Hepatology
Manuscript Type	Letter to the Editor
Article Title	Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward
Manuscript Source	Invited Manuscript
All Author List	Hong-Niu Wang, Jia-Hao An 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 Liang Zong http://orcid.org/0000-0003-4139-4571
Funding Agency and Grant Number
Corresponding Author	Liang Zong, PhD, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, No. 502 Changxing Middle Road, Changzhi 046000, Shanxi Province, China. 250537471@qq.com
Key Words	Artificial intelligence; Gastric cancer; Gastrectomy; Random forest model; Complication
Core Tip	Hong et al developed a predictive scoring system that uses machine learning techniques including LASSO regression, random forests, and artificial neural networks to assess complications following laparoscopic radical gastrectomy for gastric cancer. Their model, which was validated using data from multiple centers, showed high diagnostic accuracy and sensitivity, particularly with the random forest method. This innovative artificial intelligence-driven approach enhances surgical safety, reduces complication risks, and offers a valuable tool for both preoperative and postoperative decision-making, particularly for less-experienced gastroenterologists managing gastric cancer cases.
Publish Date	2024-10-31 10:42
Citation	<p>Wang HN, An JH, Zong L. Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward. <i>World J Gastroenterol</i> 2024; 30(43): 4669-4671</p>
URL	https://www.wjgnet.com/1007-9327/full/v30/i43/4669.htm
DOI	https://dx.doi.org/10.3748/wjg.v30.i43.4669

Full Article (PDF)	WJG-30-4669-with-cover.pdf
Manuscript File	99940_Auto_Edited_150159.docx
Answering Reviewers	99940-answering-reviewers.pdf
Audio Core Tip	99940-audio.m4a
Conflict-of-Interest Disclosure Form	99940-conflict-of-interest-statement.pdf
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Non-Native Speakers of English Editing Certificate	99940-non-native-speakers.pdf
Peer-review Report	99940-peer-reviews.pdf
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Scientific Editor Work List	99940-scientific-editor-work-list.pdf
CrossCheck Report	99940-crosscheck-report.png
CrossCheck Report	99940-crosscheck-report.pdf