Publication Name	World Journal of Hepatology
Manuscript ID	109530
Country	China

Received	2025-05-15 02:36
Peer-Review Started	2025-05-26 00:11
To Make the First Decision
Return for Revision	2025-06-10 05:15
Revised	2025-06-16 14:57
Second Decision	2025-07-10 02:44
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-07-10 08:08
Articles in Press	2025-07-10 08:08
Publication Fee Transferred	2025-06-19 00:23
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-08-14 03:05
Publish the Manuscript Online	2025-08-27 09:21

ISSN	1948-5182 (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	Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma
Manuscript Source	Unsolicited Manuscript
All Author List	Xue-Yong Zuo and Hai-Feng Liu
ORCID	Author(s) ORCID Number Xue-Yong Zuo http://orcid.org/0000-0003-1564-467X Hai-Feng Liu http://orcid.org/0000-0002-5348-6943
Funding Agency and Grant Number
Corresponding Author	Xue-Yong Zuo, MD, PhD, Department of Gastroenterology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou 213003, Changzhou 213003, Jiangsu, China. zuoxueyong@outlook.com
Key Words	Hepatocellular carcinoma; Ki-67; Radiomics; Deep transfer learning; Recurrence-free survival
Core Tip	In this study, our developed nomogram model can serve as an effective imaging biomarker for predicting Ki-67 risk stratification and predicting survival outcomes in hepatocellular carcinoma patients, with better reliability and clinical utility.
Publish Date	2025-08-27 09:21
Citation	<p>Zuo XY, Liu HF. Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma. <i>World J Hepatol</i> 2025; 17(8): 109530</p>
URL	https://www.wjgnet.com/1948-5182/full/v17/i8/109530.htm
DOI	https://dx.doi.org/10.4254/wjh.v17.i8.109530

Full Article (PDF)	WJH-17-109530-with-cover.pdf
Manuscript File	109530_Auto_Edited_013721.docx
Answering Reviewers	109530-answering-reviewers.pdf
Audio Core Tip	109530-audio.m4a
Biostatistics Review Certificate	109530-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	109530-conflict-of-interest-statement.pdf
Copyright License Agreement	109530-copyright-assignment.pdf
Signed Informed Consent Form(s) or Document(s)	109530-informed-consent-statement.pdf
Institutional Review Board Approval Form or Document	109530-institutional-review-board-statement.pdf
Non-Native Speakers of English Editing Certificate	109530-non-native-speakers.pdf
Supplementary Material	109530-supplementary-material.pdf
Peer-review Report	109530-peer-reviews.pdf
Scientific Misconduct Check	109530-scientific-misconduct-check.png
Scientific Editor Work List	109530-scientific-editor-work-list.pdf
CrossCheck Report	109530-crosscheck-report.pdf