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Article

AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response

1
Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing 100044, China
2
Liver Research Center, Beijing Friendship Hospital, Capital Medical University, 95 Yong-an Road, Xi-Cheng District, Beijing 100050, China
3
HistoIndex Pte. Ltd., Singapore 117674, Singapore
4
Department of Pathology, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore
5
LiverPat SAS, F-75116 Paris, France
6
Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing 102218, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
These authors also contributed equally to this work.
Diagnostics 2024, 14(16), 1837; https://doi.org/10.3390/diagnostics14161837
Submission received: 22 July 2024 / Revised: 16 August 2024 / Accepted: 20 August 2024 / Published: 22 August 2024

Abstract

This study aimed to understand the dynamic changes in fibrosis and its relationship with the evaluation of post-treatment viral hepatitis using qFibrosis. A total of 158 paired pre- and post-treatment liver samples from patients with chronic hepatitis B (CHB; n = 100) and C (CHC; n = 58) were examined. qFibrosis was employed with artificial intelligence (AI) to analyze the fibrosis dynamics in the portal tract (PT), periportal (PP), midzonal, pericentral, and central vein (CV) regions. All patients with CHB achieved a virological response after 78 weeks of treatment, whereas patients with CHC achieved a sustained viral response after 24 weeks. For patients initially staged as F5/6 (Ishak system) at baseline, the post-treatment cases exhibited a significant reduction in the collagen proportionate area (CPA) (25–69%) and number of collagen strings (#string) (9–72%) across all regions. In contrast, those initially staged as F3/4 at baseline showed a similar CPA and #string trend at 24 weeks. For regression patients, 27 parameters (25 in the CV region) in patients staged as F3/4 and 15 parameters (three in the PT and 12 in the PP regions) in those staged as F5/6 showed significant differences between the CHB and CHC groups at baseline. Following successful antiviral treatment, the pre- and post-treatment liver samples provided quantitative evidence of the heterogeneity of fibrotic features. qFibrosis has the potential to provide new insights into the characteristics of fibrosis regression in both patients with CHB and CHC as early as 24 weeks after antiviral therapy.
Keywords: artificial intelligence; chronic hepatitis B; chronic hepatitis C; liver fibrosis; qFibrosis; viral hepatitis artificial intelligence; chronic hepatitis B; chronic hepatitis C; liver fibrosis; qFibrosis; viral hepatitis

Share and Cite

MDPI and ACS Style

Liu, F.; Sun, Y.; Tai, D.; Ren, Y.; Chng, E.L.K.; Wee, A.; Bedossa, P.; Huang, R.; Wang, J.; Wei, L.; et al. AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response. Diagnostics 2024, 14, 1837. https://doi.org/10.3390/diagnostics14161837

AMA Style

Liu F, Sun Y, Tai D, Ren Y, Chng ELK, Wee A, Bedossa P, Huang R, Wang J, Wei L, et al. AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response. Diagnostics. 2024; 14(16):1837. https://doi.org/10.3390/diagnostics14161837

Chicago/Turabian Style

Liu, Feng, Yameng Sun, Dean Tai, Yayun Ren, Elaine L. K. Chng, Aileen Wee, Pierre Bedossa, Rui Huang, Jian Wang, Lai Wei, and et al. 2024. "AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response" Diagnostics 14, no. 16: 1837. https://doi.org/10.3390/diagnostics14161837

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