Correlation between ultrasound attenuation coefficient measurement (ACM) and MRI PDFF assessment for evaluating liver steatosis in MASLD

Metabolically associated steatotic liver disease (MASLD) is a prevalent and serious global health concern, affecting 20–40% of the population. Early detection and accurate quantification of liver steatosis (LS) are crucial for preventing disease progression[1]. Current imaging methods, including ultrasound (US), and magnetic resonance imaging (MRI), play key roles in noninvasive diagnosis. Among these, MRI-proton density fat fraction (MRI-PDFF) serves as the reference standard for quantitative assessment; however, its cost and limited availability restrict widespread use. This study evaluated the diagnostic performance of attenuation coefficient measurements (ACM) for detecting and quantifying LS in MASLD using MRI-PDFF as the reference standard. This prospective study included 168 patients aged 18–64 years. Demographic, anthropometric, and laboratory data were collected. US evaluation included right and left liver lobe thickness, hepatorenal index (HRI), shear wave elastography (SWE), and ACM quantification. MRI-PDFF was performed on a 1.5-T system. Statistical analysis included the Kruskal–Wallis test, correlation analysis, and receiver operating characteristic (ROC) curve assessment. A strong correlation was found between ACM and MRI-PDFF (r = 0.83, p < 0.0001), confirming high diagnostic reliability. ACM demonstrated high diagnostic accuracy for identifying hepatic steatosis (AUC = 0.94, 95% CI: 0.88–0.96), with specificity and sensitivity of 89.8% and 82.2%, respectively. Compared with other ultrasound techniques, ACM outperformed HRI (AUC = 0.89) and SWE (AUC = 0.72). The established ACM cut-off values for LS stages were 2.29 dB/cm (S0–S3), 2.24 dB/cm (S1), 2.51 dB/cm (S2), and 2.88 dB/cm (S3). ACM demonstrates high diagnostic accuracy for detecting and quantifying LS and shows strong agreement with MRI-PDFF. Given its noninvasive nature, accessibility, and reliability, ACM has strong potential as a primary screening and monitoring tool for MASLD. Further validation in larger cohorts is recommended to support widespread clinical implementation.