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Kaposi PN, Zsombor Z, Rónaszéki AD, Budai BK, Csongrády B, Stollmayer R, Kalina I, Győri G, Bérczi V, Werling K, Maurovich-Horvat P, Folhoffer A, Hagymási K. The Calculation and Evaluation of an Ultrasound-Estimated Fat Fraction in Non-Alcoholic Fatty Liver Disease and Metabolic-Associated Fatty Liver Disease. Diagnostics (Basel) 2023; 13:3353. [PMID: 37958249 PMCID: PMC10648816 DOI: 10.3390/diagnostics13213353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
We aimed to develop a non-linear regression model that could predict the fat fraction of the liver (UEFF), similar to magnetic resonance imaging proton density fat fraction (MRI-PDFF), based on quantitative ultrasound (QUS) parameters. We measured and retrospectively collected the ultrasound attenuation coefficient (AC), backscatter-distribution coefficient (BSC-D), and liver stiffness (LS) using shear wave elastography (SWE) in 90 patients with clinically suspected non-alcoholic fatty liver disease (NAFLD), and 51 patients with clinically suspected metabolic-associated fatty liver disease (MAFLD). The MRI-PDFF was also measured in all patients within a month of the ultrasound scan. In the linear regression analysis, only AC and BSC-D showed a significant association with MRI-PDFF. Therefore, we developed prediction models using non-linear least squares analysis to estimate MRI-PDFF based on the AC and BSC-D parameters. We fitted the models on the NAFLD dataset and evaluated their performance in three-fold cross-validation repeated five times. We decided to use the model based on both parameters to calculate UEFF. The correlation between UEFF and MRI-PDFF was strong in NAFLD and very strong in MAFLD. According to a receiver operating characteristics (ROC) analysis, UEFF could differentiate between <5% vs. ≥5% and <10% vs. ≥10% MRI-PDFF steatosis with excellent, 0.97 and 0.91 area under the curve (AUC), accuracy in the NAFLD and with AUCs of 0.99 and 0.96 in the MAFLD groups. In conclusion, UEFF calculated from QUS parameters is an accurate method to quantify liver fat fraction and to diagnose ≥5% and ≥10% steatosis in both NAFLD and MAFLD. Therefore, UEFF can be an ideal non-invasive screening tool for patients with NAFLD and MAFLD risk factors.
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Affiliation(s)
- Pál Novák Kaposi
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Zita Zsombor
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Aladár D. Rónaszéki
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Bettina K. Budai
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Barbara Csongrády
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Róbert Stollmayer
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Ildikó Kalina
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Gabriella Győri
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Viktor Bérczi
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Klára Werling
- Department of Surgery, Transplantation and Gastroenterology, Faculty of Medicine, Semmelweis University, Üllői út 78., 1082 Budapest, Hungary; (K.W.); (K.H.)
| | - Pál Maurovich-Horvat
- Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2., 1083 Budapest, Hungary; (Z.Z.); (A.D.R.); (B.K.B.); (B.C.); (R.S.); (I.K.); (G.G.); (V.B.); (P.M.-H.)
| | - Anikó Folhoffer
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Korányi S. u. 2/A., 1083 Budapest, Hungary;
| | - Krisztina Hagymási
- Department of Surgery, Transplantation and Gastroenterology, Faculty of Medicine, Semmelweis University, Üllői út 78., 1082 Budapest, Hungary; (K.W.); (K.H.)
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