Guilherme Moura Cunha1,2, Tydus T Thai3, Gavin Hamilton3, Yesenia Covarrubias3, Alexandra Schlein3, Michael S Middleton3, Curtis N Wiens4, Alan McMillan4, Rashmi Agni5, Luke M Funk6, Guilherme M Campos7, Santiago Horgan8, Garth Jacobson8, Tanya Wolfson9, Anthony Gamst9, Jeffrey B Schwimmer10, Scott B Reeder4,11,12, Claude B Sirlin3. 1. Liver Imaging Group, Radiology, University of California-San Diego, 9500 Gilman Drive, San Diego, CA, 92037, USA. gcunha@ucsd.edu. 2. Liver Imaging Group, Radiology, Altman Clinical Translational Research Institute, 9452 Medical Center Drive, Lower Level 501, La Jolla, CA, 92037, USA. gcunha@ucsd.edu. 3. Liver Imaging Group, Radiology, University of California-San Diego, 9500 Gilman Drive, San Diego, CA, 92037, USA. 4. Department of Radiology, E3/366 Clinical Science Center, University of Wisconsin, School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53792-3252, USA. 5. Department of Pathology and Laboratory Medicine, University of Wisconsin School of Medicine and Public Health, 3170 UW Medical Foundation Centennial Building (MFCB), 1685 Highland Avenue, Madison, WI, 53705-2281, USA. 6. Surgery, University of Wisconsin, Clinical Science Center, 600 Highland Avenue, Madison, WI, 53792-3252, USA. 7. Department of Surgery, West Hospital, Virginia Commonwealth University, 1200 East Broad Street 16th Floor, West Wing Box 980645, Richmond, VA, 23298-0645, USA. 8. Surgery, University of California-San Diego, 9500 Gilman Drive, San Diego, CA, 92037, USA. 9. Computational and Applied Statistics Laboratory (CASL), San Diego Supercomputer Center, University of California-San Diego, 9500 Gilman Drive, San Diego, CA, 92037, USA. 10. Pediatrics, University of California San Diego, 9500 Gilman Drive, San Diego, CA, 92037, USA. 11. Medical Physics, University of Wisconsin Madison, Clinical Science Center, 600 Highland Avenue, Madison, WI, 53792-3252, USA. 12. Biomedical Engineering, Madison, WI, Clinical Science Center, 600 Highland Avenue, Madison, WI, 53792-3252, USA.
Abstract
PURPOSE: MRI proton density fat fraction (PDFF) can be calculated using magnitude (MRI-M) or complex (MRI-C) MRI data. The purpose of this study was to identify, assess, and compare the accuracy of common PDFF thresholds for MRI-M and MRI-C for assessing hepatic steatosis in patients with obesity, using histology as reference. METHODS: This two-center prospective study included patients undergoing MRI-C- and MRI-M-PDFF estimations within 3 days before weight loss surgery. Liver biopsy was performed, and histology-determined steatosis grades were used as reference standard. Using receiver operating characteristics (ROC) analysis on data pooled from both methods, single common thresholds for diagnosing and differentiating none or mild (0-1) from moderate to severe steatosis (2-3) were selected as the ones achieving the highest sensitivity while providing at least 90% specificity. Selection methods were cross-validated. Performances were compared using McNemar's tests. RESULTS: Of 81 included patients, 54 (67%) had steatosis. The common PDFF threshold for diagnosing steatosis was 5.4%, which provided a cross-validated 0.88 (95% CI 0.77-0.95) sensitivity and 0.92 (0.75-0.99) specificity for MRI-M and 0.87 sensitivity (0.75-0.94) with 0.81 (0.61-0.93) specificity for MRI-C. The common PDFF threshold to differentiate steatosis grades 0-1 from 2 to 3 was 14.7%, which provided cross-validated 0.86 (95% CI 0.59-0.98) sensitivity and 0.95 (0.87-0.99) specificity for MRI-M and 0.93 sensitivity (0.68-0.99) with 0.97(0.89-0.99) specificity for MRI-C. CONCLUSION: If independently validated, diagnostic thresholds of 5.4% and 14.7% could be adopted for both techniques for detecting and differentiating none to mild from moderate to severe steatosis, respectively, with high diagnostic accuracy.
PURPOSE: MRI proton density fat fraction (PDFF) can be calculated using magnitude (MRI-M) or complex (MRI-C) MRI data. The purpose of this study was to identify, assess, and compare the accuracy of common PDFF thresholds for MRI-M and MRI-C for assessing hepatic steatosis in patients with obesity, using histology as reference. METHODS: This two-center prospective study included patients undergoing MRI-C- and MRI-M-PDFF estimations within 3 days before weight loss surgery. Liver biopsy was performed, and histology-determined steatosis grades were used as reference standard. Using receiver operating characteristics (ROC) analysis on data pooled from both methods, single common thresholds for diagnosing and differentiating none or mild (0-1) from moderate to severe steatosis (2-3) were selected as the ones achieving the highest sensitivity while providing at least 90% specificity. Selection methods were cross-validated. Performances were compared using McNemar's tests. RESULTS: Of 81 included patients, 54 (67%) had steatosis. The common PDFF threshold for diagnosing steatosis was 5.4%, which provided a cross-validated 0.88 (95% CI 0.77-0.95) sensitivity and 0.92 (0.75-0.99) specificity for MRI-M and 0.87 sensitivity (0.75-0.94) with 0.81 (0.61-0.93) specificity for MRI-C. The common PDFF threshold to differentiate steatosis grades 0-1 from 2 to 3 was 14.7%, which provided cross-validated 0.86 (95% CI 0.59-0.98) sensitivity and 0.95 (0.87-0.99) specificity for MRI-M and 0.93 sensitivity (0.68-0.99) with 0.97(0.89-0.99) specificity for MRI-C. CONCLUSION: If independently validated, diagnostic thresholds of 5.4% and 14.7% could be adopted for both techniques for detecting and differentiating none to mild from moderate to severe steatosis, respectively, with high diagnostic accuracy.
Entities:
Keywords:
Liver; Magnetic resonance imaging; Non-alcoholic fat liver disease; Obesity
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