Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated...

To characterize liver fat distribution in metabolic dysfunction-associated steatohepatitis (MASH) and propose a magnetic resonance imaging proton density fat fraction (MRI-PDFF)-based score for MASH identification in obesity.

Individuals with obesity were recruited for liver biopsy and contemporaneous MR scanning. The variability in fat distribution was evaluated by calculating the standard deviation (SD), range, and coefficient of variation of MRI-PDFF across liver lobes. A machine learning-assisted strategy was employed to establish a diagnostic model for MASH.

A total of 107 participants with biopsy-confirmed fatty liver were included, and 44 were diagnosed with MASH. The MASH group exhibited significantly higher fat content in all liver segments and more homogeneous fat distribution in the right liver lobe than those with non-MASH. A raw-MASH score, incorporating the mean value and SD of PDFF in the right lobe, alanine aminotransferase levels, and waist circumference, was then established to identify MASH with an area under the receiver operating characteristic curve (AUROC) of 0.90 (95%CI 0.84-0.95). It performed better than HAIR, ION, and acNASH (all p < 0.05), and exhibited higher AUROC than MR-MASH model (0.90 vs. 0.85, p = 0.066). Besides, the dual threshold strategy of raw-MASH improved the diagnostic performance with high sensitivity and specificity.

In addition to the hepatic fat content, the homogeneity of fat distribution may represent another significant hallmark of MASH. The raw-MASH score, which is available from MR imaging and routine clinical collection, shows great potential for identifying MASH in obesity.