Non-invasive imaging biomarkers in chronic liver disease
- PMID: 39317002
- DOI: 10.1016/j.ejrad.2024.111749
Non-invasive imaging biomarkers in chronic liver disease
Abstract
Chronic liver disease (CLD) is a global and worldwide clinical challenge, considering that different underlying liver entities can lead to hepatic dysfunction. In the past, blood tests and clinical evaluation were the main noninvasive tools used to detect, diagnose and follow-up patients with CLD; in case of clinical suspicion of CLD or unclear diagnosis, liver biopsy has been considered as the reference standard to rule out different chronic liver conditions. Nowadays, noninvasive tests have gained a central role in the clinical pathway. Particularly, liver stiffness measurement (LSM) and cross-sectional imaging techniques can provide transversal information to clinicians, helping them to correctly manage, treat and follow patients during time. Cross-sectional imaging techniques, namely computed tomography (CT) and magnetic resonance imaging (MRI), have plenty of potential. Both techniques allow to compute the liver surface nodularity (LSN), associated with CLDs and risk of decompensation. MRI can also help quantify fatty liver infiltration, mainly with the proton density fat fraction (PDFF) sequences, and detect and quantify fibrosis, especially thanks to elastography (MRE). Advanced techniques, such as intravoxel incoherent motion (IVIM), T1- and T2- mapping are promising tools for detecting fibrosis deposition. Furthermore, the injection of hepatobiliary contrast agents has gained an important role not only in liver lesion characterization but also in assessing liver function, especially in CLDs. Finally, the broad development of radiomics signatures, applied to CT and MR, can be considered the next future approach to CLDs. The aim of this review is to provide a comprehensive overview of the current advancements and applications of both invasive and noninvasive imaging techniques in the evaluation and management of CLD.
Keywords: Computed tomography; Diffusion magnetic resonance imaging; Gadolinium EOB-DTPA; Magnetic resonance spectroscopy; Multiparametric magnetic resonance imaging.
Copyright © 2024 Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Roberto Cannella’s disclosures: support for attending meetings from Bracco and Bayer; research collaboration with Siemens Healthineers. Roberto Cannella’s funding: co-funding by the European Union - FESR or FSE, PON Research and Innovation 2014-2020 - DM 1062/2021. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article..
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