Quantitative Ultrasound for Hepatic Steatosis: A Systematic Review Highlighting the Diagnostic Performance of...

Background/Objectives: Metabolic-dysfunction-associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease worldwide, requiring accurate and accessible diagnostic tools. Methods: A systematic review evaluated the diagnostic performance of Ultrasound-Derived Fat Fraction (UDFF), with a primary focus on prospective studies comparing UDFF to MRI Proton Density Fat Fraction (MRI-PDFF) as the reference standard and a secondary appraisal of its performance against other modalities. Additional parameters, such as technical feasibility, inter-observer agreement, and proposed thresholds, were summarized to support clinical applicability. Results: Seven prospective MRI-based studies (n = 862) demonstrated excellent correlation (average r = 0.848) and reproducibility (inter-observer intraclass correlation coefficient ICC = 0.978, intra-observer ICC = 0.980) of UDFF, with high diagnostic accuracy across steatosis grades (AUCs ≥ 0.89). Additional studies comparing UDFF with Controlled Attenuation Parameter (CAP), histology, and other quantitative ultrasound techniques (attenuation- or backscatter-based methods) confirmed high sensitivity and specificity, particularly for advanced steatosis, and emphasized the potential of UDFF as a comprehensive quantitative biomarker. Proposed UDFF cut-offs for mild, moderate, and severe steatosis ranged from 5% to 23%, demonstrating high sensitivity and specificity. Factors like body position, probe pressure, and visceral fat influenced measurements, underscoring the need for standardized protocols. Conclusions: UDFF seems to offer a reliable and cost-effective quantitative ultrasound modality. So far, it correlates strongly with MRI-PDFF and accurately grades steatosis, especially for S2-S3. Given cut-off variability and protocol sensitivity, broad routine adoption may be premature. Therefore, we recommend further studies focusing on standardized acquisition and cut-off calibration to MRI-PDFF.