Thierry Lefebvre1,2,3, Mélanie Hébert1,2, Laurent Bilodeau1,2, Giada Sebastiani4, Milena Cerny1,2, Damien Olivié1, Zu-Hua Gao5, Marie-Pierre Sylvestre2,6, Guy Cloutier1,7,8, Bich N Nguyen9, Guillaume Gilbert1,10, An Tang11,12,13. 1. Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada. 2. Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada. 3. Medical Physics Unit, McGill University, Montréal, Canada. 4. Division of Gastroenterology and Hepatology, Department of Medicine, McGill University Health Centre (MUHC), Montréal, Canada. 5. Department of Pathology, McGill University, Montréal, Canada. 6. Department of Social and Preventive Medicine, École de santé publique de l'Université de Montréal (ESPUM), Montréal, Canada. 7. Institute of Biomedical Engineering, Université de Montréal, Montréal, Canada. 8. Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada. 9. Service of Pathology, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Canada. 10. MR Clinical Science, Philips Healthcare Canada, Markham, Canada. 11. Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada. an.tang@umontreal.ca. 12. Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada. an.tang@umontreal.ca. 13. Institute of Biomedical Engineering, Université de Montréal, Montréal, Canada. an.tang@umontreal.ca.
Abstract
OBJECTIVE: To evaluate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for grading hepatic inflammation. METHODS: In this retrospective cross-sectional dual-center study, 91 patients with chronic liver disease were recruited between September 2014 and September 2018. Patients underwent 3.0-T MRI examinations within 6 weeks from a liver biopsy. IVIM parameters, perfusion fraction (f), diffusion coefficient (D), and pseudo-diffusion coefficient (D*), were estimated using a voxel-wise nonlinear regression on DWI series (10 b-values from 0 to 800 s/mm2). The reference standard was histopathological analysis of hepatic inflammation grade, steatosis grade, and fibrosis stage. Intraclass correlation coefficients (ICC), univariate and multivariate correlation analyses, and areas under receiver operating characteristic curves (AUC) were assessed. RESULTS: Parameters f, D, and D* had ICCs of 0.860, 0.839, and 0.916, respectively. Correlations of f, D, and D* with inflammation grade were ρ = - 0.70, p < 0.0001; ρ = 0.10, p = 0.35; and ρ = - 0.27, p = 0.010, respectively. When adjusting for fibrosis and steatosis, the correlation between f and inflammation (p < 0.0001) remained, and that between f and fibrosis was also significant to a lesser extent (p = 0.002). AUCs of f, D, and D* for distinguishing inflammation grades 0 vs. ≥ 1 were 0.84, 0.53, and 0.70; ≤ 1 vs. ≥ 2 were 0.88, 0.57, and 0.60; and ≤ 2 vs. 3 were 0.86, 0.54, and 0.65, respectively. CONCLUSION: Perfusion fraction f strongly correlated, D very weakly correlated, and D* weakly correlated with inflammation. Among all IVIM parameters, f accurately graded inflammation and showed promise as a biomarker of hepatic inflammation. KEY POINTS: • IVIM parameters derived from DWI series with 10 b-values are reproducible for liver tissue characterization. • This retrospective two-center study showed that perfusion fraction provided good diagnostic performance for distinguishing dichotomized grades of inflammation. • Fibrosis is a significant confounder on the association between inflammation and perfusion fraction.
OBJECTIVE: To evaluate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for grading hepatic inflammation. METHODS: In this retrospective cross-sectional dual-center study, 91 patients with chronic liver disease were recruited between September 2014 and September 2018. Patients underwent 3.0-T MRI examinations within 6 weeks from a liver biopsy. IVIM parameters, perfusion fraction (f), diffusion coefficient (D), and pseudo-diffusion coefficient (D*), were estimated using a voxel-wise nonlinear regression on DWI series (10 b-values from 0 to 800 s/mm2). The reference standard was histopathological analysis of hepatic inflammation grade, steatosis grade, and fibrosis stage. Intraclass correlation coefficients (ICC), univariate and multivariate correlation analyses, and areas under receiver operating characteristic curves (AUC) were assessed. RESULTS: Parameters f, D, and D* had ICCs of 0.860, 0.839, and 0.916, respectively. Correlations of f, D, and D* with inflammation grade were ρ = - 0.70, p < 0.0001; ρ = 0.10, p = 0.35; and ρ = - 0.27, p = 0.010, respectively. When adjusting for fibrosis and steatosis, the correlation between f and inflammation (p < 0.0001) remained, and that between f and fibrosis was also significant to a lesser extent (p = 0.002). AUCs of f, D, and D* for distinguishing inflammation grades 0 vs. ≥ 1 were 0.84, 0.53, and 0.70; ≤ 1 vs. ≥ 2 were 0.88, 0.57, and 0.60; and ≤ 2 vs. 3 were 0.86, 0.54, and 0.65, respectively. CONCLUSION: Perfusion fraction f strongly correlated, D very weakly correlated, and D* weakly correlated with inflammation. Among all IVIM parameters, f accurately graded inflammation and showed promise as a biomarker of hepatic inflammation. KEY POINTS: • IVIM parameters derived from DWI series with 10 b-values are reproducible for liver tissue characterization. • This retrospective two-center study showed that perfusion fraction provided good diagnostic performance for distinguishing dichotomized grades of inflammation. • Fibrosis is a significant confounder on the association between inflammation and perfusion fraction.
Authors: Giada Sebastiani; Keyur Patel; Vlad Ratziu; Jordan J Feld; Brent A Neuschwander-Tetri; Massimo Pinzani; Salvatore Petta; Annalisa Berzigotti; Peter Metrakos; Naglaa Shoukry; Elizabeth M Brunt; An Tang; Jeremy F Cobbold; Jean-Marie Ekoe; Karen Seto; Peter Ghali; Stéphanie Chevalier; Quentin M Anstee; Heather Watson; Harpreet Bajaj; James Stone; Mark G Swain; Alnoor Ramji Journal: Can Liver J Date: 2022-02-04
Authors: Ricardo Donners; Carmen Zaugg; Julian E Gehweiler; Tuyana Boldanova; Markus H Heim; Luigi M Terracciano; Daniel T Boll Journal: Quant Imaging Med Surg Date: 2022-02
Authors: Guilherme Moura Cunha; Patrick J Navin; Kathryn J Fowler; Sudhakar K Venkatesh; Richard L Ehman; Claude B Sirlin Journal: Br J Radiol Date: 2021-02-26 Impact factor: 3.629