Publication Name	World Journal of Gastroenterology
Manuscript ID	104697
Country	China

Received	2024-12-31 03:40
Peer-Review Started	2024-12-31 03:41
To Make the First Decision
Return for Revision	2025-02-13 23:02
Revised	2025-02-20 09:11
Second Decision	2025-03-11 02:45
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-03-11 06:55
Articles in Press	2025-03-11 06:55
Publication Fee Transferred	2025-02-21 03:04
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-03-24 06:56
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: http://creativecommons.org/Licenses/by-nc/4.0/
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Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology
Manuscript Source	Unsolicited Manuscript
All Author List	Si-Yi Feng, Zong-Ren Ding, Jin Cheng and Hai-Bin Tu
ORCID	Author(s) ORCID Number Hai-Bin Tu http://orcid.org/0000-0003-4540-9937
Funding Agency and Grant Number	Funding Agency Grant Number the Agency Natural Science Foundation of Fujian Province, China No. 2022J011285 and No. 2023J011480
Corresponding Author	Hai-Bin Tu, Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, No. 66 Jintang Road, Jianxin Town, Cangshan District, Fuzhou 350025, Fujian Province, China. thb861126@163.com
Key Words	Esophagogastric varices; Machine learning; Extreme gradient boosting; Ultrasound; Serological markers
Core Tip	We constructed a noninvasive predictive model using machine learning for esophagogastric varices in hepatitis B patients. An Extreme gradient boosting model, based on ultrasound and serological markers, achieved high accuracy (area under the curve = 0.96) in predicting high-risk esophagogastric varices. Key predictive variables included albumin, prothrombin time, portal vein flow velocity and spleen stiffness. A web-based application was developed to facilitate clinical use, offering real-time risk assessment. This model provides a promising tool for targeted screening, potentially reducing the need for costly and risky endoscopic procedures in low-risk individuals.
Publish Date	2025-04-02 10:54
Citation	<p>Feng SY, Ding ZR, Cheng J, Tu HB. Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology. <i>World J Gastroenterol</i> 2025; 31(13): 104697</p>
URL	https://www.wjgnet.com/1007-9327/full/v31/i13/104697.htm
DOI	https://dx.doi.org/10.3748/wjg.v31.i13.104697

Full Article (PDF)	WJG-31-104697-with-cover.pdf
Manuscript File	104697_Auto_Edited_010154.docx
Answering Reviewers	104697-answering-reviewers.pdf
Audio Core Tip	104697-audio.mp3
Biostatistics Review Certificate	104697-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	104697-conflict-of-interest-statement.pdf
Copyright License Agreement	104697-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	104697-foundation-statement.pdf
Signed Informed Consent Form(s) or Document(s)	104697-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	104697-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	104697-non-native-speakers.pdf
Supplementary Material	104697-supplementary-material.pdf
Peer-review Report	104697-peer-reviews.pdf
Scientific Misconduct Check	104697-scientific-misconduct-check.png
Scientific Editor Work List	104697-scientific-editor-work-list.pdf
CrossCheck Report	104697-crosscheck-report.pdf