Intravoxel incoherent motion diffusion-weighted MRI for the characterization of inflammation in chronic liver disease.


Journal

European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774

Informations de publication

Date de publication:
Mar 2021
Historique:
received: 28 03 2020
accepted: 18 08 2020
revised: 10 06 2020
pubmed: 3 9 2020
medline: 15 4 2021
entrez: 3 9 2020
Statut: ppublish

Résumé

To evaluate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for grading hepatic inflammation. 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/mm 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. 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. • 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.

Identifiants

pubmed: 32876833
doi: 10.1007/s00330-020-07203-y
pii: 10.1007/s00330-020-07203-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1347-1358

Subventions

Organisme : CIHR
ID : 273738
Pays : Canada
Organisme : CIHR
ID : 301520
Pays : Canada
Organisme : Fonds de Recherche du Québec - Santé
ID : 26993
Organisme : Fonds de Recherche du Québec - Santé
ID : 34939
Organisme : Fonds de Recherche du Québec - Santé
ID : 27127
Organisme : Fonds de Recherche du Québec - Santé
ID : 267806
Organisme : Fonds de Recherche du Québec - Santé
ID : 34875
Organisme : CIHR
ID : 273738
Pays : Canada
Organisme : CIHR
ID : 301520
Pays : Canada

Références

Younossi ZM, Blissett D, Blissett R et al (2016) The economic and clinical burden of nonalcoholic fatty liver disease in the United States and Europe. Hepatology 64:1577–1586
doi: 10.1002/hep.28785
Bedossa P (2017) Pathology of non-alcoholic fatty liver disease. Liver Int 37(Suppl 1):85–89
doi: 10.1111/liv.13301
Yin M, Glaser KJ, Manduca A et al (2017) Distinguishing between hepatic inflammation and fibrosis with MR elastography. Radiology 284:694–705
doi: 10.1148/radiol.2017160622
Argo CK, Northup PG, Al-Osaimi AM, Caldwell SH (2009) Systematic review of risk factors for fibrosis progression in non-alcoholic steatohepatitis. J Hepatol 51:371–379
doi: 10.1016/j.jhep.2009.03.019
Poynard T, Mathurin P, Lai CL et al (2003) A comparison of fibrosis progression in chronic liver diseases. J Hepatol 38:257–265
doi: 10.1016/S0168-8278(02)00413-0
Zhang B, Liang L, Dong Y et al (2016) Intravoxel incoherent motion MR imaging for staging of hepatic fibrosis. PLoS One 11:e0147789
doi: 10.1371/journal.pone.0147789
Jiang H, Chen J, Gao R, Huang Z, Wu M, Song B (2017) Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdom Radiol (NY) 42:490–501
doi: 10.1007/s00261-016-0913-6
Lewin M, Poujol-Robert A, Boelle PY et al (2007) Diffusion-weighted magnetic resonance imaging for the assessment of fibrosis in chronic hepatitis C. Hepatology 46:658–665
doi: 10.1002/hep.21747
Taouli B, Chouli M, Martin AJ, Qayyum A, Coakley FV, Vilgrain V (2008) Chronic hepatitis: role of diffusion-weighted imaging and diffusion tensor imaging for the diagnosis of liver fibrosis and inflammation. J Magn Reson Imaging 28:89–95
doi: 10.1002/jmri.21227
Taouli B, Tolia AJ, Losada M et al (2007) Diffusion-weighted MRI for quantification of liver fibrosis: preliminary experience. AJR Am J Roentgenol 189:799–806
doi: 10.2214/AJR.07.2086
Sandrasegaran K, Territo P, Elkady RM et al (2018) Does intravoxel incoherent motion reliably stage hepatic fibrosis, steatosis, and inflammation? Abdom Radiol (NY) 43:600–606
doi: 10.1007/s00261-017-1263-8
Leitao HS, Doblas S, Garteiser P et al (2017) Hepatic fibrosis, inflammation, and steatosis: influence on the MR viscoelastic and diffusion parameters in patients with chronic liver disease. Radiology 283:98–107
doi: 10.1148/radiol.2016151570
Franca M, Marti-Bonmati L, Alberich-Bayarri A et al (2017) Evaluation of fibrosis and inflammation in diffuse liver diseases using intravoxel incoherent motion diffusion-weighted MR imaging. Abdom Radiol (NY) 42:468–477
doi: 10.1007/s00261-016-0899-0
Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168:497–505
doi: 10.1148/radiology.168.2.3393671
Murphy P, Hooker J, Ang B et al (2015) Associations between histologic features of nonalcoholic fatty liver disease (NAFLD) and quantitative diffusion-weighted MRI measurements in adults. J Magn Reson Imaging 41:1629–1638
doi: 10.1002/jmri.24755
Manning P, Murphy P, Wang K et al (2017) Liver histology and diffusion-weighted MRI in children with nonalcoholic fatty liver disease: a MAGNET study. J Magn Reson Imaging 46:1149–1158
doi: 10.1002/jmri.25663
Huang H, Che-Nordin N, Wang LF et al (2019) High performance of intravoxel incoherent motion diffusion MRI in detecting viral hepatitis-b induced liver fibrosis. Ann Transl Med 7:39
doi: 10.21037/atm.2018.12.33
Lu PX, Huang H, Yuan J et al (2014) Decreases in molecular diffusion, perfusion fraction and perfusion-related diffusion in fibrotic livers: a prospective clinical intravoxel incoherent motion MR imaging study. PLoS One 9:e113846
doi: 10.1371/journal.pone.0113846
Fujimoto K, Tonan T, Azuma S et al (2011) Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. Radiology 258:739–748
doi: 10.1148/radiol.10100853
Xie Y, Zhang H, Jin C et al (2018) Gd-EOB-DTPA-enhanced T1rho imaging vs diffusion metrics for assessment liver inflammation and early stage fibrosis of nonalcoholic steatohepatitis in rabbits. Magn Reson Imaging 48:34–41
doi: 10.1016/j.mri.2017.12.017
Joo I, Lee JM, Yoon JH, Jang JJ, Han JK, Choi BI (2014) Nonalcoholic fatty liver disease: intravoxel incoherent motion diffusion-weighted MR imaging-an experimental study in a rabbit model. Radiology 270:131–140
doi: 10.1148/radiol.13122506
Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M (2016) Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64:73–84
doi: 10.1002/hep.28431
Lefebvre T, Wartelle-Bladou C, Wong P et al (2019) Prospective comparison of transient, point shear wave, and magnetic resonance elastography for staging liver fibrosis. Eur Radiol 29:6477–6488
doi: 10.1007/s00330-019-06331-4
Hansmann J, Hernando D, Reeder SB (2013) Fat confounds the observed apparent diffusion coefficient in patients with hepatic steatosis. Magn Reson Med 69:545–552
doi: 10.1002/mrm.24535
Gomori JM, Holland GA, Grossman RI, Gefter WB, Lenkinski RE (1988) Fat suppression by section-select gradient reversal on spin-echo MR imaging. Work in progress. Radiology 168:493–495
doi: 10.1148/radiology.168.2.3393670
Iima M, Le Bihan D (2016) Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology 278:13–32
doi: 10.1148/radiol.2015150244
Taouli B, Beer AJ, Chenevert T et al (2016) Diffusion-weighted imaging outside the brain: consensus statement from an ISMRM-sponsored workshop. J Magn Reson Imaging 44:521–540
doi: 10.1002/jmri.25196
Benovoy M, Jacobs M, Cheriet F, Dahdah N, Arai AE, Hsu LY (2017) Robust universal nonrigid motion correction framework for first-pass cardiac MR perfusion imaging. J Magn Reson Imaging 46:1060–1072
doi: 10.1002/jmri.25659
Yokoo T, Bydder M, Hamilton G et al (2009) Nonalcoholic fatty liver disease: diagnostic and fat-grading accuracy of low-flip-angle multiecho gradient-recalled-echo MR imaging at 1.5 T. Radiology 251:67–76
doi: 10.1148/radiol.2511080666
Bydder M, Yokoo T, Yu H, Carl M, Reeder SB, Sirlin CB (2011) Constraining the initial phase in water-fat separation. Magn Reson Imaging 29:216–221
doi: 10.1016/j.mri.2010.08.011
Kleiner DE, Brunt EM, Van Natta M et al (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41:1313–1321
doi: 10.1002/hep.20701
Bedossa P, Poynard T (1996) An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology 24:289–293
doi: 10.1002/hep.510240201
Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86:420–428
doi: 10.1037/0033-2909.86.2.420
Schober P, Boer C, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126:1763–1768
doi: 10.1213/ANE.0000000000002864
DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845
doi: 10.2307/2531595
Le Bihan D, Turner R (1992) The capillary network: a link between IVIM and classical perfusion. Magn Reson Med 27:171–178
doi: 10.1002/mrm.1910270116
Granger DN, Senchenkova E (2010) Inflammation and the microcirculation. Morgan & Claypool Life Sciences. San Rafael, CA
Patel J, Sigmund EE, Rusinek H, Oei M, Babb JS, Taouli B (2010) Diagnosis of cirrhosis with intravoxel incoherent motion diffusion MRI and dynamic contrast-enhanced MRI alone and in combination: preliminary experience. J Magn Reson Imaging 31:589–600
doi: 10.1002/jmri.22081
Barbieri S, Gurney-Champion OJ, Klaassen R, Thoeny HC (2020) Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI. Magn Reson Med 83:312–321
doi: 10.1002/mrm.27910
Neubauer K, Saile B, Ramadori G (2001) Liver fibrosis and altered matrix synthesis. Can J Gastroenterol 15:870205
doi: 10.1155/2001/870205
Diehl AM, Day C (2017) Cause, pathogenesis, and treatment of nonalcoholic steatohepatitis. N Engl J Med 377:2063–2072
doi: 10.1056/NEJMra1503519
Leitao HS, Doblas S, d’Assignies G et al (2013) Fat deposition decreases diffusion parameters at MRI: a study in phantoms and patients with liver steatosis. Eur Radiol 23:461–467
doi: 10.1007/s00330-012-2626-8

Auteurs

Thierry Lefebvre (T)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.
Medical Physics Unit, McGill University, Montréal, Canada.

Mélanie Hébert (M)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.

Laurent Bilodeau (L)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.

Giada Sebastiani (G)

Division of Gastroenterology and Hepatology, Department of Medicine, McGill University Health Centre (MUHC), Montréal, Canada.

Milena Cerny (M)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.

Damien Olivié (D)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.

Zu-Hua Gao (ZH)

Department of Pathology, McGill University, Montréal, Canada.

Marie-Pierre Sylvestre (MP)

Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.
Department of Social and Preventive Medicine, École de santé publique de l'Université de Montréal (ESPUM), Montréal, Canada.

Guy Cloutier (G)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
Institute of Biomedical Engineering, Université de Montréal, Montréal, Canada.
Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.

Bich N Nguyen (BN)

Service of Pathology, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Canada.

Guillaume Gilbert (G)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
MR Clinical Science, Philips Healthcare Canada, Markham, Canada.

An Tang (A)

Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada. an.tang@umontreal.ca.
Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada. an.tang@umontreal.ca.
Institute of Biomedical Engineering, Université de Montréal, Montréal, Canada. an.tang@umontreal.ca.

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