Publication Name	World Journal of Gastroenterology
Manuscript ID	98888
Country	China

Received	2024-08-19 07:34
Peer-Review Started	2024-07-08 13:37
To Make the First Decision
Return for Revision	2024-09-07 18:05
Revised	2024-09-19 07:45
Second Decision	2024-09-27 02:36
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-09-27 06:23
Articles in Press	2024-09-27 06:23
Publication Fee Transferred	2024-09-29 08:17
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-10-13 16:03
Publish the Manuscript Online	2024-10-16 06:29

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: http://creativecommons.org/Licenses/by-nc/4.0/
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Category	Oncology
Manuscript Type	Retrospective Study
Article Title	Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors
Manuscript Source	Unsolicited Manuscript
All Author List	Hong-Wei Li, Zi-Yu Zhu, Yu-Fei Sun, Chao-Yu Yuan, Mo-Han Wang, Nan Wang and Ying-Wei Xue
ORCID	Author(s) ORCID Number Hong-Wei Li http://orcid.org/0009-0000-6905-0813 Zi-Yu Zhu http://orcid.org/0000-0002-5160-4483 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, China Nn10 PY 2017-03
Corresponding Author	Ying-Wei Xue, PhD, Professor, Surgical Oncologist, Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Harbin 150081, Heilongjiang Province, China. xueyingwei@hrbmu.edu.cn
Key Words	Gastric cancer; Machine learning; Immune checkpoint inhibitors; Web-based calculator; Progression-free survival; Overall survival
Core Tip	This study identified predictive markers and developed machine learning models to assess the prognosis of patients with gastric cancer and treated with immune checkpoint inhibitors. Key findings highlighted the significance of peripheral blood markers such as platelet count/(lymphocyte count × serum prealbumin), prognostic nutrition index, and body mass index in predicting overall survival and progression-free survival. eXtreme Gradient Boosting was the most effective model for prediction and outperformed traditional methods. These insights underscore the potential of machine-learning algorithms in personalized medicine and emphasize the role of nutritional status in treatment outcomes of patients with gastric cancer.
Publish Date	2024-10-16 06:29
Citation	<p>Li HW, Zhu ZY, Sun YF, Yuan CY, Wang MH, Wang N, Xue YW. Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors. <i>World J Gastroenterol</i> 2024; 39(40): 4354-4366</p>
URL	https://www.wjgnet.com/1007-9327/full/v30/i40/4354.htm
DOI	https://dx.doi.org/10.3748/wjg.v30.i40.4354

Full Article (PDF)	WJG-30-4354-with-cover.pdf
Manuscript File	98888_Auto_Edited_094742.docx
Answering Reviewers	98888-answering-reviewers.pdf
Audio Core Tip	98888-audio.mp3
Biostatistics Review Certificate	98888-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	98888-conflict-of-interest-statement.pdf
Copyright License Agreement	98888-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	98888-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	98888-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	98888-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	98888-non-native-speakers.pdf
Supplementary Material	98888-supplementary-material.pdf
Peer-review Report	98888-peer-reviews.pdf
Scientific Misconduct Check	98888-scientific-misconduct-check.png
Scientific Editor Work List	98888-scientific-editor-work-list.pdf
CrossCheck Report	98888-crosscheck-report.pdf