Publication Name	World Journal of Hepatology
Manuscript ID	109530
Country	China

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
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.
Citation	Zuo XY, Liu HF. Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma. World J Hepatol 2025; In press

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

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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