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Routine laboratory model for identifying significant fibrosis in chronic hepatitis B
Ting-Ting Wang, Yi-Qiang Lou, Mao-Mao Pu, Lu Huang, Hai-Jun Huang, Center for General Practice Medicine, Department of Infectious Disease, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
Yi-Li Chu, Rou-Yi Yang, The Second School of Clinical Medicine, Hangzhou Normal University, Hangzhou 311121, Zhejiang Province, China
Lian-Jiang Shan, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang Province, China
Shan-Shan Chen, Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou 310014, Zhejiang Province, China
Author contributions: Wang TT and Huang HJ designed the research study; Wang TT, Chu YL, Lou YQ, Yang RY, and Pu MM performed data acquisition, quality control, and cross-center harmonization; Wang TT performed the statistical analysis, developed the machine-learning model, and drafted the manuscript; Chu YL and Huang L provided support for statistical analysis and model development; Shan LJ provided clinical oversight, interpreted the histological findings, and coordinated the external validation cohort; Chen SS contributed to interpretation of the results and critically revised the manuscript; and all authors reviewed, revised, and approved the final manuscript.
AI contribution statement: AI tools were used only for language polishing and writing assistance to improve clarity and readability. No part of the scientific content of the manuscript (including Abstract, Introduction, Materials and Methods, Results, Discussion, and Conclusion) was generated by AI. All content was written, reviewed, and approved by the authors.
Supported by National Nature Science Foundation of China, No. 82272425.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of Zhejiang Provincial People’s Hospital, No. KY2024182.
Informed consent statement: Informed consent was waived by the Institutional Review Board because this is a retrospective study using anonymized clinical data.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: Technical appendix, statistical code, and dataset are available from the corresponding author at upon reasonable request.
Corresponding author: Hai-Jun Huang, Professor, Researcher, Center for General Practice Medicine, Department of Infectious Disease, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou 310014, Zhejiang Province, China. huanghaijun@hmc.edu.cn
Received: January 22, 2026
Revised: March 11, 2026
Accepted: April 20, 2026
Published online: June 27, 2026
Processing time: 155 Days and 22.4 Hours
Revised: March 11, 2026
Accepted: April 20, 2026
Published online: June 27, 2026
Processing time: 155 Days and 22.4 Hours
Core Tip
Core Tip: In this study, a laboratory-based machine-learning model was developed and externally validated for noninvasive identification of significant fibrosis in patients with chronic hepatitis B using biopsy-confirmed multicenter cohorts. The model showed stable discrimination and acceptable calibration across independent hospitals, and retained risk-ranking ability in an exploratory population-based cohort with surrogate fibrosis labels. Because it relies only on routinely available laboratory tests, this model may serve as a practical complementary tool for fibrosis risk stratification, particularly in settings where elastography is unavailable or inconsistently applied.