Clinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis

Nonalcoholic fatty liver disease (NAFLD) is increasing in prevalence worldwide. NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a...

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Veröffentlicht in:Clinical gastroenterology and hepatology 2022-11, Vol.20 (11), p.2451-2461.e3
Hauptverfasser: Andersson, Anneli, Kelly, Matt, Imajo, Kento, Nakajima, Atsushi, Fallowfield, Jonathan A., Hirschfield, Gideon, Pavlides, Michael, Sanyal, Arun J., Noureddin, Mazen, Banerjee, Rajarshi, Dennis, Andrea, Harrison, Stephen
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container_end_page 2461.e3
container_issue 11
container_start_page 2451
container_title Clinical gastroenterology and hepatology
container_volume 20
creator Andersson, Anneli
Kelly, Matt
Imajo, Kento
Nakajima, Atsushi
Fallowfield, Jonathan A.
Hirschfield, Gideon
Pavlides, Michael
Sanyal, Arun J.
Noureddin, Mazen
Banerjee, Rajarshi
Dennis, Andrea
Harrison, Stephen
description Nonalcoholic fatty liver disease (NAFLD) is increasing in prevalence worldwide. NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH). Data from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC). A stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74–0.82), for liver fat was 0.78 (95% CI, 0.73–0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78–0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74–0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64–0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75–0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers. This study shows that quantitative MRI-derived biomarkers cT1 and liver fat are suitable for identifying patients with NASH, and cT1 is a better noninvasive technology than liver fat to identify NASH patients at greatest risk of disease progression. Therefore, MRI cT1 and liver fat have important clinical utility to help guide the appropriate use of interventions in NAFLD and NASH clinical care pathways. [Display omitted]
doi_str_mv 10.1016/j.cgh.2021.09.041
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NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH). Data from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC). A stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74–0.82), for liver fat was 0.78 (95% CI, 0.73–0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78–0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74–0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64–0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75–0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers. This study shows that quantitative MRI-derived biomarkers cT1 and liver fat are suitable for identifying patients with NASH, and cT1 is a better noninvasive technology than liver fat to identify NASH patients at greatest risk of disease progression. Therefore, MRI cT1 and liver fat have important clinical utility to help guide the appropriate use of interventions in NAFLD and NASH clinical care pathways. [Display omitted]</description><identifier>ISSN: 1542-3565</identifier><identifier>EISSN: 1542-7714</identifier><identifier>DOI: 10.1016/j.cgh.2021.09.041</identifier><identifier>PMID: 34626833</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>cT1 ; LiverMultiScan ; NAFLD ; Noninvasive ; PDFF ; Quantitative MRI</subject><ispartof>Clinical gastroenterology and hepatology, 2022-11, Vol.20 (11), p.2451-2461.e3</ispartof><rights>2021 by the AGA Institute</rights><rights>Copyright © 2021 by the AGA Institute. Published by Elsevier Inc. 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NAFLD is associated with excess risk of all-cause mortality, and its progression to nonalcoholic steatohepatitis (NASH) and fibrosis accounts for a growing proportion of cirrhosis and hepatocellular cancer and thus is a leading cause of liver transplant worldwide. Noninvasive precise methods to identify patients with NASH and NASH with significant disease activity and fibrosis are crucial when the disease is still modifiable. The aim of this study was to examine the clinical utility of corrected T1 (cT1) vs magnetic resonance imaging (MRI) liver fat for identification of NASH participants with nonalcoholic fatty liver disease activity score ≥4 and fibrosis stage (F) ≥2 (high-risk NASH). Data from five clinical studies (n = 543) with participants suspected of NAFLD were pooled or used for individual participant data meta-analysis. The diagnostic accuracy of the MRI biomarkers to stratify NASH patients was determined using the area under the receiver operating characteristic curve (AUROC). A stepwise increase in cT1 and MRI liver fat with increased NAFLD severity was shown, and cT1 was significantly higher in participants with high-risk NASH. The diagnostic accuracy (AUROC) of cT1 to identify patients with NASH was 0.78 (95% CI, 0.74–0.82), for liver fat was 0.78 (95% CI, 0.73–0.82), and when combined with MRI liver fat was 0.82 (95% CI, 0.78–0.85). The diagnostic accuracy of cT1 to identify patients with high-risk NASH was good (AUROC = 0.78; 95% CI, 0.74–0.82), was superior to MRI liver fat (AUROC = 0.69; 95% CI, 0.64–0.74), and was not substantially improved by combining it with MRI liver fat (AUROC = 0.79; 95% CI, 0.75–0.83). The meta-analysis showed similar performance to the pooled analysis for these biomarkers. 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subjects cT1
LiverMultiScan
NAFLD
Noninvasive
PDFF
Quantitative MRI
title Clinical Utility of Magnetic Resonance Imaging Biomarkers for Identifying Nonalcoholic Steatohepatitis Patients at High Risk of Progression: A Multicenter Pooled Data and Meta-Analysis
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