Liu SC, Wang ZW, Zhang H. Realizing personalized prediction for the reversal of chronic hepatitis B-associated liver fibrosis through machine learning. World J Gastroenterol 2026; 32(42): 120151 [DOI: 10.3748/wjg.120151]
Corresponding Author of This Article
Shi-Cai Liu, PhD, School of Medical Information, Wannan Medical University, No. 22 Wenchang West Road, Wuhu 241002, Anhui Province, China. liushicainj@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
Open-Access Policy of This Article
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/
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Liu SC, Wang ZW, Zhang H. Realizing personalized prediction for the reversal of chronic hepatitis B-associated liver fibrosis through machine learning. World J Gastroenterol 2026; 32(42): 120151 [DOI: 10.3748/wjg.120151]
World J Gastroenterol. Nov 14, 2026; 32(42): 120151 Published online Nov 14, 2026. doi: 10.3748/wjg.120151
Realizing personalized prediction for the reversal of chronic hepatitis B-associated liver fibrosis through machine learning
Shi-Cai Liu, Zi-Wen Wang, Han Zhang
Shi-Cai Liu, Zi-Wen Wang, School of Medical Information, Wannan Medical University, Wuhu 241002, Anhui Province, China
Shi-Cai Liu, Anhui Province High-Quality Dataset Construction Base for Smart Healthcare, Wannan Medical University, Wuhu 241002, Anhui Province, China
Han Zhang, School of Basic Medical Sciences, Wannan Medical University, Wuhu 241002, Anhui Province, China
Author contributions: Liu SC conceived and outlined the manuscript, contributed to the discussion and the manuscript preparation; Liu SC, Wang ZW and Zhang H contributed to the writing and editing of the manuscript, and review of the literature; all authors have read and approved the final version of the manuscript.
Supported by the Scientific Research Project of Anhui Provincial Department of Education, No. 2025AHGXZK30665; and Talent Scientific Research Start-up Foundation of Wannan Medical College, No. WYRCQD2023045.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Shi-Cai Liu, PhD, School of Medical Information, Wannan Medical University, No. 22 Wenchang West Road, Wuhu 241002, Anhui Province, China. liushicainj@163.com
Received: February 24, 2026 Revised: March 30, 2026 Accepted: April 14, 2026 Published online: November 14, 2026 Processing time: 217 Days and 22.8 Hours
Abstract
Although long-term antiviral therapy can reverse liver fibrosis in a considerable proportion of patients with chronic hepatitis B, treatment response differs markedly among individuals, and reliable methods for predicting who will benefit remain lacking. Conventional clinical tools such as liver stiffness measurement and the aspartate aminotransferase-to-platelet ratio index were originally dewsigned for cross-sectional staging rather than longitudinal prediction, and their ability to capture the dynamic process of fibrosis reversal is limited. In this opinion review, we discuss how machine learning (ML) may help address this unmet need. We focus on three areas where ML has shown early but encouraging results: Quantitative analysis of images using ML, integration of pathological and clinical data through multimodal models, and dynamic prediction of treatment response based on serial measurements during antiviral therapy. In our view, the greatest value of ML lies not in replacing existing assessment tools but in complementing them, particularly through the early identification of patients who are unlikely to achieve fibrosis reversal with antiviral therapy alone. However, we also caution that most current models have been developed from small, single-center retrospective cohorts, and their clinical adoption will require prospective multi-center validation, improved model interpretability, and adherence to standardized reporting frameworks.
Core Tip: Predicting liver fibrosis reversal in chronic hepatitis B remains a major clinical challenge due to the limitations of conventional assessment tools. Leveraging machine learning-driven histopathological image analysis and multimodal data fusion, enhances the accuracy and individualization of predicting treatment response during antiviral therapy. Integrating quantitative pathological features, clinical parameters, and proteomic biomarkers into unified machine learning frameworks optimizes early identification of non-responders, supports personalized therapeutic decision-making for chronic hepatitis B patients.
