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Published on: March 24, 2023
Automated Whole-Liver MRI Segmentation to Assess Steatosis and Iron Quantification in Chronic Liver Disease
David Martí-Aguado1, Ana Jiménez-Pastor1, Ángel Alberich-Bayarri1
1From the Departments of Digestive Diseases (D.M.A., M.P.B., D.E.G.), Pathology (C.A.C., C.M.A., A.F.), and Gastroenterology and Hepatology (D.M.A.), Clinic University Hospital, INCLIVA Health Research Institute, Avenida Blasco Ibáñez 17, 46010 Valencia, Spain; Biomedical Imaging Research Group (GIBI2), La Fe Health Research Institute, Valencia, Spain (D.M.A., A.R.O., L.M.B.); Quantitative Imaging Biomarkers in Medicine, Quibim SL, Valencia, Spain (A.J.P., Á.A.B.); University of Valencia, Faculty of Medicine, Valencia, Spain (C.A.C., A.F., D.E.G., M.A.S.); Departments of Pathology (M.B., J.P.R.), Digestive Diseases (E.V.P., M.G.T.), and Radiology (A.P.G., L.M.B.) and the Hepatology and Liver Transplantation Unit (V.A.), La Fe University and Polytechnic Hospital, Valencia, Spain; Departments of Digestive Diseases (A.G.P., S.B.) and Pathology (V.P.), Hospital Arnau de Vilanova, Valencia, Spain; CIBERehd (Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas), Instituto de Salud Carlos III, Madrid, Spain (S.B., V.A.); and Río Hortega, Instituto Salud Carlos III, Madrid, Spain (D.M.A.).
Abstract:
Background Standardized manual region of interest (ROI) sampling strategies for hepatic MRI steatosis and iron quantification are time consuming, with variable results. Purpose To evaluate the performance of automatic MRI whole-liver segmentation (WLS) for proton density fat fraction (PDFF) and iron estimation (transverse relaxometry [R2*]) versus manual ROI, with liver biopsy as the reference standard. Materials and Methods This prospective, cross-sectional, multicenter study recruited participants with chronic liver disease who underwent liver biopsy and chemical shift-encoded 3.0-T MRI between January 2017 and January 2021. Biopsy evaluation included histologic grading and digital pathology. MRI liver sampling strategies included manual ROI (two observers) and automatic whole-liver (deep learning algorithm) segmentation for PDFF- and R2*-derived measurements. Agreements between segmentation methods were measured using intraclass correlation coefficients (ICCs), and biases were evaluated using Bland-Altman analyses. Linear regression analyses were performed to determine the correlation between measurements and digital pathology. Results A total of 165 participants were included (mean age ± standard deviation, 55 years ± 12; 96 women; 101 of 165 participants [61%] with nonalcoholic fatty liver disease). Agreements between mean measurements were excellent, with ICCs of 0.98 for both PDFF and R2*. The median bias was 0.5% (interquartile range, -0.4% to 1.2%) for PDFF and 2.7 sec-1 (interquartile range, 0.2-5.3 sec-1) for R2* (P < .001 for both). Margins of error were lower for WLS than ROI-derived parameters (-0.03% for PDFF and -0.3 sec-1 for R2*). ROI and WLS showed similar performance for steatosis (ROI AUC, 0.96; WLS AUC, 0.97; P = .53) and iron overload (ROI AUC, 0.85; WLS AUC, 0.83; P = .09). Correlations with digital pathology were high (P < .001) between the fat ratio and PDFF (ROI r = 0.89; WLS r = 0.90) and moderate (P < .001) between the iron ratio and R2* (ROI r = 0.65; WLS r = 0.64). Conclusion Proton density fat fraction and transverse relaxometry measurements derived from MRI automatic whole-liver segmentation (WLS) were accurate for steatosis and iron grading in chronic liver disease and correlated with digital pathology. Automated WLS estimations were higher, with a lower margin of error than manual region of interest estimations. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moura Cunha and Fowler in this issue.
