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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Aug 27, 2025; 17(8): 109530
Published online Aug 27, 2025. doi: 10.4254/wjh.v17.i8.109530
Published online Aug 27, 2025. doi: 10.4254/wjh.v17.i8.109530
Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma
Xue-Yong Zuo, Department of Gastroenterology, Third Affiliated Hospital of Soochow Uni versity, Changzhou 213003, Jiangsu Province, China
Hai-Feng Liu, Department of Radiology, Third Affiliated Hospital of Soochow University: Changzhou First People’s Hospital, Changzhou 213003, Jiangsu Province, China
Author contributions: Zuo XY, Liu HF conceived and designed research, performed experiments and analyzed data, prepared figures, edited and revised manuscript; Zuo XY interpreted results of experiments, drafted manuscript; all authors have read and approve the final manuscript.
Supported by Clinical Trials from the Third Affiliated Hospital of Soochow University, No. 2024-156; and Changzhou Science and Technology Program, No. CJ20244017.
Institutional review board statement: The study was approved by Ethics Committee of Third Affiliated Hospital of Soochow University (2022-CL027-01).
Informed consent statement: As no direct participant contact or additional data collection occurred, no statement of informed consent is required.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: All data used or analyzed during this study are included in this article and its supplementary information files.
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/
Corresponding author: Xue-Yong Zuo, MM, Department of Gastroenterology, Third Affiliated Hospital of Soochow University, No. 185 Juqian Street, Tianning District, Changzhou 213003, Jiangsu Province, China. zuoxueyong@outlook.com
Received: May 15, 2025
Revised: June 16, 2025
Accepted: July 10, 2025
Published online: August 27, 2025
Processing time: 105 Days and 17.5 Hours
Revised: June 16, 2025
Accepted: July 10, 2025
Published online: August 27, 2025
Processing time: 105 Days and 17.5 Hours
Core Tip
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.