Background: we evaluated regression models based on quantitative ultrasound (QUS)
parameters and compared them with a vendor-provided method for calculating the ultrasound
fat fraction (USFF) in metabolic dysfunction-associated steatotic liver disease (MASLD).
Methods: We measured the attenuation coefficient (AC) and the backscatter-distribution
coefficient (BSC-D) and determined the USFF during a liver ultrasound and calculated
the magnetic resonance imaging proton-density fat fraction (MRI-PDFF) and steatosis
grade (S0-S4) in a combined retrospective-prospective cohort. We trained multiple
models using single or various QUS parameters as independent variables to forecast
MRI-PDFF. Linear and nonlinear models were trained during five-time repeated three-fold
cross-validation in a retrospectively collected dataset of 60 MASLD cases. We calculated
the models' Pearson correlation (r) and the intraclass correlation coefficient (ICC)
in a prospectively collected test set of 57 MASLD cases. Results: The linear multivariable
model (r = 0.602, ICC = 0.529) and USFF (r = 0.576, ICC = 0.54) were more reliable
in S0- and S1-grade steatosis than the nonlinear multivariable model (r = 0.492, ICC
= 0.461). In S2 and S3 grades, the nonlinear multivariable (r = 0.377, ICC = 0.32)
and AC-only (r = 0.375, ICC = 0.313) models' approximated correlation and agreement
surpassed that of the multivariable linear model (r = 0.394, ICC = 0.265). We searched
a QUS parameter grid to find the optimal thresholds (AC ≥ 0.84 dB/cm/MHz, BSC-D ≥
105), above which switching from a linear (r = 0.752, ICC = 0.715) to a nonlinear
multivariable (r = 0.719, ICC = 0.641) model could improve the overall fit (r = 0.775,
ICC = 0.718). Conclusions: The USFF and linear multivariable models are robust in
diagnosing low-grade steatosis. Switching to a nonlinear model could enhance the fit
to MRI-PDFF in advanced steatosis.
