Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound
<b>Background:</b> 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). <b>Methods:<...
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2025-01-01
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| author | Zsély Boglárka 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 Pál Novák Kaposi |
| author_facet | Zsély Boglárka 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 Pál Novák Kaposi |
| author_sort | Zsély Boglárka |
| collection | DOAJ |
| description | <b>Background:</b> 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). <b>Methods:</b> 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. <b>Results:</b> 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). <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-baf3178be33340659308fb54fc4b925b |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
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| series | Diagnostics |
| spelling | doaj-art-baf3178be33340659308fb54fc4b925b2025-01-24T13:29:06ZengMDPI AGDiagnostics2075-44182025-01-0115220310.3390/diagnostics15020203Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative UltrasoundZsély Boglárka0Zita Zsombor1Aladár D. Rónaszéki2Anna Egresi3Róbert Stollmayer4Marco Himsel5Viktor Bérczi6Ildikó Kalina7Klára Werling8Gabriella Győri9Pál Maurovich-Horvat10Anikó Folhoffer11Krisztina Hagymási12Pál Novák Kaposi13Department of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Surgery, Transplantation, and Gastroenterology, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Surgery, Transplantation, and Gastroenterology, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, HungaryDepartment of Internal Medicine and Oncology, Semmelweis University, 1082 Budapest, HungaryDepartment of Surgery, Transplantation, and Gastroenterology, Semmelweis University, 1082 Budapest, HungaryDepartment of Radiology, Medical Imaging Centre, Semmelweis University, 1082 Budapest, Hungary<b>Background:</b> 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). <b>Methods:</b> 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. <b>Results:</b> 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). <b>Conclusions:</b> 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.https://www.mdpi.com/2075-4418/15/2/203fatty livermetabolic dysfunction-associated steatotic liver diseaseultrasound fat fraction (USFF)magnetic resonance imaging proton-density fat fractionregression analysis |
| spellingShingle | Zsély Boglárka 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 Pál Novák Kaposi Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound Diagnostics fatty liver metabolic dysfunction-associated steatotic liver disease ultrasound fat fraction (USFF) magnetic resonance imaging proton-density fat fraction regression analysis |
| title | Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound |
| title_full | Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound |
| title_fullStr | Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound |
| title_full_unstemmed | Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound |
| title_short | Construction of a Compound Model to Enhance the Accuracy of Hepatic Fat Fraction Estimation with Quantitative Ultrasound |
| title_sort | construction of a compound model to enhance the accuracy of hepatic fat fraction estimation with quantitative ultrasound |
| topic | fatty liver metabolic dysfunction-associated steatotic liver disease ultrasound fat fraction (USFF) magnetic resonance imaging proton-density fat fraction regression analysis |
| url | https://www.mdpi.com/2075-4418/15/2/203 |
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