Advances in quantitative ultrasound for metabolic dysfunction-associated steatotic liver disease diagnosis

Metabolic dysfunction–associated steatotic liver disease (MASLD) is increasing worldwide, underscoring the need for noninvasive, repeatable biomarkers for accurate clinical stratification. This review synthesizes recent advances in quantitative ultrasound (QUS) for MASLD phenotyping, spanning attenuation-based metrics (e.g., controlled attenuation parameter, tissue attenuation imaging, ultrasound-guided attenuation parameter), shear-wave elastography, backscatter analysis, sound-speed imaging, and artificial-intelligence–enabled multiparametric models. Unlike conventional B-mode ultrasound, which relies on subjective echogenicity, QUS derives biophysical parameters from radiofrequency signals that correlate with hepatic fat content, stiffness and viscoelasticity, microstructure, and perfusion. Across recent evidence, attenuation techniques show strong performance for detecting early steatosis, whereas shear-wave elastography better stages clinically significant fibrosis; shear-wave dispersion and microvascular imaging provide emerging surrogates of necroinflammatory activity. Integrating multiple QUS features with clinical covariates improves robustness and reduces diagnostic error relative to single-parameter tools. We summarize technical principles, acquisition considerations, and sources of variability, and discuss harmonization, cross-platform comparability, and the role of open protocols. Remaining challenges include vendor heterogeneity, the absence of unified thresholds, limited multicenter outcome data, and the need for explainable and generalizable AI. Overall, QUS is evolving from a screening adjunct to a physiologically grounded, multiparametric platform. However, current challenges such as cross-platform harmonization, vendor variability, and the need for unified diagnostic thresholds remain significant barriers. The future of QUS lies in the integration of artificial intelligence (AI) to enhance diagnostic accuracy, improve reproducibility, and address these limitations. Further research should focus on large-scale validation studies and the development of multi-parametric approaches that combine QUS with other non-invasive diagnostic tools for a more comprehensive assessment of MASLD.