Mustafa R Bashir1,2,3, Tanya Wolfson4, Anthony C Gamst4, Kathryn J Fowler5, Michael Ohliger6, Shetal N Shah7, Adina Alazraki8, Andrew T Trout9, Cynthia Behling10, Daniela S Allende11, Rohit Loomba12, Arun Sanyal13, Jeffrey Schwimmer14, Joel E Lavine15, Wei Shen16, James Tonascia17, Mark L Van Natta17, Adrija Mamidipalli18, Jonathan Hooker18, Kris V Kowdley19, Michael S Middleton18, Claude B Sirlin18. 1. Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA. 2. Center for Advanced Magnetic Resonance Development (CAMRD), Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA. 3. Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina. 4. Computational and Applied Statistics Laboratory (CASL), San Diego Supercomputing Center (SDSC), University of California-San Diego, San Diego, California, USA. 5. Department of Radiology, Washington University, St. Louis, Missouri, USA. 6. Departments of Radiology and Biomedical Engineering, University of California-San Francisco, San Francisco, California, USA. 7. Section of Abdominal Imaging and Nuclear Medicine Department, Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA. 8. Departments of Radiology and Pediatrics, Emory University School of Medicine/Children's Healthcare of Atlanta, Atlanta, Georgia, USA. 9. Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. 10. Department of Pathology, University of California-San Diego, La Jolla, California, USA. 11. Department of Pathology, Cleveland Clinic, Cleveland, Ohio, USA. 12. NAFLD Research Center, Division of Gastroenterology, Department of Medicine, University of California-San Diego, La Jolla, California, USA. 13. Division of Gastroenterology, Hepatology and Nutrition, Department of Internal Medicine, Virginia Commonwealth University, Richmond, Virginia, USA. 14. Department of Pediatrics, University of California-San Diego, San Diego, California, USA. 15. Department of Pediatrics, Columbia College of Physicians and Surgeons, New York, New York, USA. 16. Division of Pediatric Gastroenterology, Hepatology and Nutrition, Department of Pediatrics and the Institute of Human Nutrition, Columbia University Medical Center, New York, New York, USA. 17. Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA. 18. Liver Imaging Group, Department of Radiology, University of California, San Diego, San Diego, California, USA. 19. Liver Care Network and Organ Care Research, Swedish Medical Center, Seattle, Washington, USA.
Abstract
BACKGROUND: The liver R2* value is widely used as a measure of liver iron but may be confounded by the presence of hepatic steatosis and other covariates. PURPOSE: To identify the most influential covariates for liver R2* values in patients with nonalcoholic fatty liver disease (NAFLD). STUDY TYPE: Retrospective analysis of prospectively acquired data. POPULATION: Baseline data from 204 subjects enrolled in NAFLD/NASH (nonalcoholic steatohepatitis) treatment trials. FIELD STRENGTH: 1.5T and 3T; chemical-shift encoded multiecho gradient echo. ASSESSMENT: Correlation between liver proton density fat fraction and R2*; assessment for demographic, metabolic, laboratory, MRI-derived, and histological covariates of liver R2*. STATISTICAL TESTS: Pearson's and Spearman's correlations; univariate analysis; gradient boosting machines (GBM) multivariable machine-learning method. RESULTS: Hepatic proton density fat fraction (PDFF) was the most strongly correlated covariate for R2* at both 1.5T (r = 0.652, P < 0.0001) and at 3T (r = 0.586, P < 0.0001). In the GBM analysis, hepatic PDFF was the most influential covariate for hepatic R2*, with relative influences (RIs) of 61.3% at 1.5T and 47.5% at 3T; less influential covariates had RIs of up to 11.5% at 1.5T and 16.7% at 3T. Nonhepatocellular iron was weakly associated with R2* at 3T only (RI 6.7%), and hepatocellular iron was not associated with R2* at either field strength. DATA CONCLUSION: Hepatic PDFF is the most influential covariate for R2* at both 1.5T and 3T; nonhepatocellular iron deposition is weakly associated with liver R2* at 3T only. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1456-1466.
BACKGROUND: The liver R2* value is widely used as a measure of liver iron but may be confounded by the presence of hepatic steatosis and other covariates. PURPOSE: To identify the most influential covariates for liver R2* values in patients with nonalcoholic fatty liver disease (NAFLD). STUDY TYPE: Retrospective analysis of prospectively acquired data. POPULATION: Baseline data from 204 subjects enrolled in NAFLD/NASH (nonalcoholic steatohepatitis) treatment trials. FIELD STRENGTH: 1.5T and 3T; chemical-shift encoded multiecho gradient echo. ASSESSMENT: Correlation between liver proton density fat fraction and R2*; assessment for demographic, metabolic, laboratory, MRI-derived, and histological covariates of liver R2*. STATISTICAL TESTS: Pearson's and Spearman's correlations; univariate analysis; gradient boosting machines (GBM) multivariable machine-learning method. RESULTS: Hepatic proton density fat fraction (PDFF) was the most strongly correlated covariate for R2* at both 1.5T (r = 0.652, P < 0.0001) and at 3T (r = 0.586, P < 0.0001). In the GBM analysis, hepatic PDFF was the most influential covariate for hepatic R2*, with relative influences (RIs) of 61.3% at 1.5T and 47.5% at 3T; less influential covariates had RIs of up to 11.5% at 1.5T and 16.7% at 3T. Nonhepatocellular iron was weakly associated with R2* at 3T only (RI 6.7%), and hepatocellular iron was not associated with R2* at either field strength. DATA CONCLUSION: Hepatic PDFF is the most influential covariate for R2* at both 1.5T and 3T; nonhepatocellular iron deposition is weakly associated with liver R2* at 3T only. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1456-1466.
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