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Construction of a compound model to enhance the accuracy of hepatic fat fraction estimation with quantitative ultrasound$ Boglárka Zsély, Zita Zsombor, Aladár D. Rónaszéki, Anna Egresi, Róbert Stollmayer, Marco Himsel, Viktor Bérczi, Ildikó Kalina, Klára Werling, Gabriella Győri, Pál Maurovich-Horvat, Anikó Folhoffer, Krisztina Hagymási, and Pál Novák Kaposi

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 co...

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Main Authors: Zsély, Boglárka (Author) , Zsombor, Zita (Author) , Rónaszéki, Aladár D. (Author) , Egresi, Anna (Author) , Stollmayer, Róbert (Author) , Himsel, Marco (Author) , Bérczi, Viktor (Author) , Kalina, Ildikó (Author) , Werling, Klára (Author) , Győri, Gabriella (Author) , Maurovich-Horvat, Pál (Author) , Folhoffer, Anikó (Author) , Hagymási, Krisztina (Author) , Kaposi, Pál Novák (Author)
Format: Article (Journal)
Language:English
Published: 17 January 2025
In: Diagnostics
Year: 2025, Volume: 15, Issue: 2, Pages: 1-16
ISSN:2075-4418
DOI:10.3390/diagnostics15020203
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.3390/diagnostics15020203
Verlag, kostenfrei, Volltext: https://www.mdpi.com/2075-4418/15/2/203
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Summary: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.
Item Description:Gesehen am 25.08.2025
Physical Description:Online Resource
ISSN:2075-4418
DOI:10.3390/diagnostics15020203

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