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ISSN 1001-5256 (Print)
ISSN 2097-3497 (Online)
CN 22-1108/R
Volume 42 Issue 7
Jul.  2026
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Article Navigation >  Journal of Clinical Hepatology  > 2026  >  42(7): 1683-1687

Clinical evaluation of liver fibrosis regression: A multimodal precision strategy based on histology, serology, and imaging

DOI: 10.12449/JCH260727

  • Liver Research Center，Beijing Friendship Hospital，Capital Medical University，Beijing 100050，China

Research funding:

Special Project of Pathogenology and Epidemic Prevention Technology System Research under the National Key R&D Program during the 14th Five-Year Plan Period (2023YFC2306900)

More Information
  • Corresponding author: Wu Xiaoning， wuxiaoningbs@126.com （ORCID： 0000-0001-5416-712X）
  • Received Date: 2025-11-17
  • Accepted Date: 2026-02-11
  • Published Date: 2026-07-25
    Abstract
  • Liver fibrosis is a key pathological stage in the progression of chronic liver diseases to liver cirrhosis， and effective etiological control or therapeutic interventions can help to achieve varying degrees of liver fibrosis regression in some patients. Accordingly， accurate assessment of the degree of liver fibrosis regression has gradually become an important concern in clinical management and the development of novel therapeutic agents. Conventional assessment methods mainly focus on fibrosis staging and have certain limitations in reflecting structural remodeling and dynamic changes of the liver. This article systematically reviews the recent research advances in the assessment of liver fibrosis regression from various aspects such as histological and digital pathology， serological and multi-omics biomarkers， and functional and molecular imaging modalities and discusses their respective advantages and limitations in assessing liver fibrosis regression. On this basis， this article discusses a multimodal integrated evaluation strategy based on artificial intelligence， in order to provide new ideas for accurate assessment of liver fibrosis regression， individualized therapeutic monitoring， and optimization of clinical trial endpoints.

     

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