Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Shuang Zheng; Kan He; Lei Zhang; Mingyang Li; Huimao Zhang; Pujun Gao

doi:10.1016/j.ejrad.2023.110912

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging

Eur J Radiol. 2023 Aug:165:110912. doi: 10.1016/j.ejrad.2023.110912. Epub 2023 Jun 2.

Authors

Shuang Zheng¹, Kan He², Lei Zhang³, Mingyang Li⁴, Huimao Zhang⁵, Pujun Gao⁶

Affiliations

¹ Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China. Electronic address: [email protected].
² Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China. Electronic address: [email protected].
³ Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China. Electronic address: [email protected].
⁴ Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China. Electronic address: [email protected].
⁵ Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China. Electronic address: [email protected].
⁶ Department of Hepatology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China. Electronic address: [email protected].

PMID: 37290363
DOI: 10.1016/j.ejrad.2023.110912

Abstract

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

Keywords: Artificial intelligence; CT; Chronic liver disease; Liver fibrosis staging; MRI.

Publication types

Review

MeSH terms

Artificial Intelligence
Elasticity Imaging Techniques* / methods
Humans
Liver / diagnostic imaging
Liver / pathology
Liver Cirrhosis / diagnostic imaging
Liver Cirrhosis / pathology
Liver Diseases* / pathology
Magnetic Resonance Imaging / methods
Tomography, X-Ray Computed