Zhou SQ, Ke QH. Advancing precision in hepatocellular carcinoma prognostication: The promise of biparametric magnetic resonance imaging-based multimodal models. World J Hepatol 2025; 17(10): 112078 [DOI: 10.4254/wjh.v17.i10.112078]
Corresponding Author of This Article
Qing-Hua Ke, Department of Chemoradiotherapy, The First Affiliated Hospital of Yangtze University, No. 10 Tianhu Road, Shashi District, Jingzhou 434000, Hubei Province, China. 3803354759@qq.com
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Oncology
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Letter to the Editor
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Oct 27, 2025 (publication date) through Oct 27, 2025
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Publication Name
World Journal of Hepatology
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1948-5182
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Zhou SQ, Ke QH. Advancing precision in hepatocellular carcinoma prognostication: The promise of biparametric magnetic resonance imaging-based multimodal models. World J Hepatol 2025; 17(10): 112078 [DOI: 10.4254/wjh.v17.i10.112078]
World J Hepatol. Oct 27, 2025; 17(10): 112078 Published online Oct 27, 2025. doi: 10.4254/wjh.v17.i10.112078
Advancing precision in hepatocellular carcinoma prognostication: The promise of biparametric magnetic resonance imaging-based multimodal models
Shi-Qiong Zhou, Qing-Hua Ke
Shi-Qiong Zhou, Qing-Hua Ke, Department of Chemoradiotherapy, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, Hubei Province, China
Author contributions: Zhou SQ drafted the manuscript; Ke QH designed the overall concept and supervised the project. All authors read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Qing-Hua Ke, Department of Chemoradiotherapy, The First Affiliated Hospital of Yangtze University, No. 10 Tianhu Road, Shashi District, Jingzhou 434000, Hubei Province, China. 3803354759@qq.com
Received: July 17, 2025 Revised: July 28, 2025 Accepted: September 17, 2025 Published online: October 27, 2025 Processing time: 102 Days and 22.2 Hours
Core Tip
Core Tip: Zuo and Liu examined the prognostic performance of a biparametric magnetic resonance imaging-based multimodal model in hepatocellular carcinoma. By integrating radiomics, deep transfer learning - a technique that captures subtle imaging patterns imperceptible to the human eye through convolutional neural networks, and clinical factors, this integrated multimodal model effectively predicts Ki-67 risk stratification and recurrence-free survival, offering a noninvasive alternative to invasive histopathological analysis. Given the retrospective nature of the cohort, further validation in multicenter, prospective studies is essential. This work advances precision prognostication in hepatocellular carcinoma, addressing the critical need for preoperative risk stratification tools.
