Publication Name	World Journal of Gastroenterology
Manuscript ID	105283
Country	China

Received	2025-01-17 15:47
Peer-Review Started	2025-01-17 15:47
To Make the First Decision
Return for Revision	2025-03-19 23:14
Revised	2025-03-27 10:21
Second Decision	2025-04-27 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-27 05:58
Articles in Press	2025-04-27 05:58
Publication Fee Transferred	2025-03-28 06:42
Edit the Manuscript by Language Editor	2025-04-28 03:31
Typeset the Manuscript	2025-05-14 06:14
Publish the Manuscript Online	2025-05-21 05:53

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	Retrospective Study
Article Title	Interpretable machine learning model for anastomotic leakage after rectal cancer resection based on serum calcium: Multi-center study
Manuscript Source	Unsolicited Manuscript
All Author List	Bo-Yu Kang, Yi-Huan Qiao, Jun Zhu, Bao-Liang Hu, Ze-Cheng Zhang, Ji-Peng Li and Yan-Jiang Pei
Funding Agency and Grant Number	Funding Agency Grant Number National Natural Science Foundation of China 82172781 Shaanxi Health Scientific Research Innovation Team Project 2024TD-06
Corresponding Author	Yan-Jiang Pei, MD, PhD, Professor, Department of Digestive Surgery, Honghui Hospital, Xi'an Jiaotong University., No. 555, Youyi East Road, Beilin District, Xi’an City, Shaanxi Province, China,, Xi’an 710032, Shanxi Province, China. 15829329200@126.com
Key Words	Machine learning; Rectal cancer; Anastomotic leakage; Shapley Additive exPlanations algorithms
Core Tip	Ten machine learning models were established using 10 factors and interpreted using the Shapley Additive exPlanations model. Through model evaluation and comparison, we selected the best prediction model and performed external validation in multiple centers; We found for the first time that perioperative serum calcium ion level plays an important role in the occurrence of anastomotic leakage (AL) after anterior resection of rectal cancer, and proposed that preoperative serum calcium ion lower than 2.1 and postoperative calcium ion lower than 2.2 are clinical warning values for the occurrence of AL.
Publish Date	2025-05-21 05:53
Citation	<p>Kang BY, Qiao YH, Zhu J, Hu BL, Zhang ZC, Li JP, Pei YJ. Interpretable machine learning model for anastomotic leakage after rectal cancer resection based on serum calcium: Multi-center study. <i>World J Gastroenterol</i> 2025; 31(19): 105283</p>
URL	https://www.wjgnet.com/1007-9327/full/v31/i19/105283.htm
DOI	https://dx.doi.org/10.3748/wjg.v31.i19.105283

Full Article (PDF)	WJG-31-105283-with-cover.pdf
Manuscript File	105283_Auto_Edited_074351.docx
Answering Reviewers	105283-answering-reviewers.pdf
Audio Core Tip	105283-audio.mp3
Biostatistics Review Certificate	105283-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	105283-conflict-of-interest-statement.pdf
Copyright License Agreement	105283-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	105283-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	105283-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	105283-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	105283-non-native-speakers.pdf
Supplementary Material	105283-supplementary-material.pdf
Peer-review Report	105283-peer-reviews.pdf
Scientific Misconduct Check	105283-scientific-misconduct-check.png
Scientific Editor Work List	105283-scientific-editor-work-list.pdf
CrossCheck Report	105283-crosscheck-report.pdf