AI Article Synopsis
- Chronic liver diseases (CLDs) have various causes, and this study aimed to classify them better using multiparametric MRI to analyze liver changes in murine models induced by high-fat diets and carbon tetrachloride.
- Mice were subjected to different dietary and chemical treatments, and the study utilized advanced MRI techniques to track liver morphology, inflammation, and cell activity over time, comparing MRI data with traditional histopathological methods.
- The results indicated that specific MRI parameters correlated with liver damage and injury, revealing distinct patterns for each model, suggesting that MRI can be a valuable tool for early detection and classification of CLDs.
Article Abstract
Objectives: Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl 4 )-induced CLD models.
Materials And Methods: Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl 4 for 8 weeks. Longitudinal multiparametric MRI was performed every 4 weeks using a 7 T MRI scanner, including T1/T2 relaxometry, morphological T1/T2-weighted imaging, and fat-selective imaging. Diffusion-weighted imaging was applied to assess fibrotic remodeling and T1-weighted and T2*-weighted dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI using gadoxetic acid and ferucarbotran to target hepatocytes and the mononuclear phagocyte system, respectively. Imaging data were associated with histopathological and serological analyses. Principal component analysis and clustering were used to reveal underlying disease patterns.
Results: The MRI parameters significantly correlated with histologically confirmed steatosis, fibrosis, and liver damage, with varying importance. No single MRI parameter exclusively correlated with 1 pathophysiological feature, underscoring the necessity for using parameter patterns. Clustering revealed early-stage, model-specific patterns. Although the HFD model exhibited pronounced liver fat content and fibrosis, the CCl 4 model indicated reduced liver fat content and impaired hepatocyte and macrophage function. In both models, MRI biomarkers of inflammation were elevated.
Conclusions: Multiparametric MRI patterns can be assigned to pathophysiological processes and used for murine CLD classification and progression tracking. These MRI biomarker patterns can directly be explored clinically to improve early CLD detection and differentiation and to refine treatments.
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| http://dx.doi.org/10.1097/RLI.0000000000001075 | DOI Listing |
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