Literature DB >> 34408822

Steatosis grading consistency between controlled attenuation parameter and MRI-PDFF in monitoring metabolic associated fatty liver disease.

Cong Xiang Shao1, Junzhao Ye1, Zhi Dong2, Fuxi Li1, Yansong Lin1, Bing Liao3, Shiting Feng2, Bihui Zhong4.   

Abstract

BACKGROUND: The consistency in steatosis grading between magnetic resonance imaging-based proton density fat fraction (MRI-PDFF) and controlled attenuation parameter (CAP) before and after treatment remains unclear. This study aimed to compare the diagnostic accuracy of steatosis grading between MRI-PDFF and CAP using liver biopsy as standard and to evaluate the value of monitoring changes in steatosis grading with CAP during follow-up utilizing MRI-PDFF as a reference.
METHODS: Consecutive patients from a biopsy cohort and a randomized controlled trial were included in this study and classified into 3 groups (the biopsy, orlistat treatment, and routine treatment subgroups). Hepatic steatosis was measured via MRI-PDFF and CAP at baseline and at the 6th month; the accuracy and cutoffs were assessed in the liver biopsy cohort at baseline.
RESULTS: A total of 209 consecutive patients were enrolled. MRI-PDFF and CAP showed comparable diagnostic accuracy for detecting pathological steatosis [⩾S1, area under the receiver operating characteristic curve (AUC) = 0.984 and 0.972, respectively]; in contrast, CAP presented significantly lower AUCs in grades S2-3 and S3 (0.820 and 0.815, respectively). The CAP values correlated well with the MRI-PDFF values at baseline and at the 6th month (r = 0.809 and 0.762, respectively, both p < 0.001), whereas a moderate correlation in their changes (r = 0.612 and 0.524 for moderate-severe and mild steatosis, respectively; both p < 0.001) was observed. The AUC of CAP change was obtained to predict MRI-PDFF changes of ⩾5% and ⩾10% (0.685 and 0.704, p < 0.001 and p = 0.001, respectively). The diagnostic agreement of steatosis grade changes between MRI-PDFF and CAP was weak (κ = 0.181, p = 0.001).
CONCLUSIONS: CAP has decreased value for the initial screening of moderate-severe steatosis and is limited in monitoring changes in steatosis during treatment. The confirmation of steatosis grading with MRI-PDFF remains necessary.
© The Author(s), 2021.

Entities:  

Keywords:  controlled attenuation parameter; hepatic steatosis; liver biopsy; magnetic resonance imaging-based proton density fat fraction; metabolic associated fatty liver disease

Year:  2021        PMID: 34408822      PMCID: PMC8366131          DOI: 10.1177/20406223211033119

Source DB:  PubMed          Journal:  Ther Adv Chronic Dis        ISSN: 2040-6223            Impact factor:   5.091


  43 in total

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2.  Remission of Human Type 2 Diabetes Requires Decrease in Liver and Pancreas Fat Content but Is Dependent upon Capacity for β Cell Recovery.

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Journal:  Cell Metab       Date:  2018-10-02       Impact factor: 27.287

3.  MR Spectroscopy-derived Proton Density Fat Fraction Is Superior to Controlled Attenuation Parameter for Detecting and Grading Hepatic Steatosis.

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Journal:  Radiology       Date:  2017-09-15       Impact factor: 11.105

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Journal:  Hepatol Int       Date:  2020-10-01       Impact factor: 6.047

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7.  Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis.

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Journal:  Hepatology       Date:  2018-02-19       Impact factor: 17.425

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Journal:  Medicine (Baltimore)       Date:  2018-12       Impact factor: 1.817

10.  Ectopic and Visceral Fat Deposition in Lean and Obese Patients With Type 2 Diabetes.

Authors:  Eylem Levelt; Michael Pavlides; Rajarshi Banerjee; Masliza Mahmod; Catherine Kelly; Joanna Sellwood; Rina Ariga; Sheena Thomas; Jane Francis; Christopher Rodgers; William Clarke; Nikant Sabharwal; Charalambos Antoniades; Jurgen Schneider; Matthew Robson; Kieran Clarke; Theodoros Karamitsos; Oliver Rider; Stefan Neubauer
Journal:  J Am Coll Cardiol       Date:  2016-07-05       Impact factor: 24.094

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  3 in total

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3.  Improving the accuracy of fatty liver index to reflect liver fat content with predictive regression modelling.

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