The Calculation and Evaluation of an Ultrasound-Estimated Fat Fraction in Non-alcoholic Fatty Liver Disease and Metabolic-Associated Fatty Liver Disease.
We aimed to develop a non-linear regression model that could predict the fat fraction of the liver (USFF), 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 3-fold cross-validation repeated five times. We decided to use the model based on both parameters to estimate the ultrasound fat fraction (USFF). The correlation between USFF and MRI-PDFF was strong in NAFLD and very strong in MAFLD. According to a receiver operating characteristics (ROC) analysis, USFF 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, USFF 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, USFF can be an ideal non-invasive screening tool for patients with NAFLD and MAFLD risk factors..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Preprints.org - (2023) vom: 06. Nov. Zur Gesamtaufnahme - year:2023 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Kaposi, Pál Novák [VerfasserIn] |
|---|
| Links: |
|---|
| doi: |
10.20944/preprints202309.1729.v1 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
preprintsorg040997456 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | preprintsorg040997456 | ||
| 003 | DE-627 | ||
| 005 | 20231205144052.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231002s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.20944/preprints202309.1729.v1 |2 doi | |
| 024 | 7 | |a 10.20944/preprints202309.1729.v1 |2 doi | |
| 035 | |a (DE-627)preprintsorg040997456 | ||
| 035 | |a (preprintsorg)10.20944/preprints202309.1729 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Kaposi, Pál Novák |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a The Calculation and Evaluation of an Ultrasound-Estimated Fat Fraction in Non-alcoholic Fatty Liver Disease and Metabolic-Associated Fatty Liver Disease. |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a We aimed to develop a non-linear regression model that could predict the fat fraction of the liver (USFF), 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 3-fold cross-validation repeated five times. We decided to use the model based on both parameters to estimate the ultrasound fat fraction (USFF). The correlation between USFF and MRI-PDFF was strong in NAFLD and very strong in MAFLD. According to a receiver operating characteristics (ROC) analysis, USFF 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, USFF 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, USFF can be an ideal non-invasive screening tool for patients with NAFLD and MAFLD risk factors. | ||
| 700 | 1 | |a Zsombor, Zita |0 (orcid)0000-0002-2369-0329 |4 aut | |
| 700 | 1 | |a Rónaszéki, Aladár Dávid |4 aut | |
| 700 | 1 | |a Budai, Bettina Katalin |0 (orcid)0000-0002-3982-7887 |4 aut | |
| 700 | 1 | |a Csongrády, Barbara |4 aut | |
| 700 | 1 | |a Stollmayer, Róbert |4 aut | |
| 700 | 1 | |a Kalina, Ildikó |4 aut | |
| 700 | 1 | |a Győri, Gabriella |4 aut | |
| 700 | 1 | |a Bérczi, Viktor |4 aut | |
| 700 | 1 | |a Werling, Klára |4 aut | |
| 700 | 1 | |a Maurovich-Horvát, Pál |4 aut | |
| 700 | 1 | |a Folhoffer, Anikó |4 aut | |
| 700 | 1 | |a Hagymási, Krisztina |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Preprints.org |g (2023) vom: 06. Nov. |
| 773 | 1 | 8 | |g year:2023 |g day:06 |g month:11 |
| 856 | 4 | 0 | |u https://doi.org/10.3390/diagnostics13213353 |z lizenzpflichtig |3 Volltext |
| 856 | 4 | 0 | |u http://dx.doi.org/10.20944/preprints202309.1729.v1 |z kostenfrei |3 Volltext |
| 912 | |a preprintsorg | ||
| 951 | |a AR | ||
| 952 | |j 2023 |b 06 |c 11 | ||