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J Hepatol. 2016 Feb;64(2):308-315. doi: 10.1016/j.jhep.2015.10.009. Epub 2015 Nov 10.

Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease.

Author information

1
Translational Gastroenterology Unit, University of Oxford, UK; Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, UK.
2
Perspectum Diagnostics, Oxford, UK.
3
Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, UK.
4
Royal Berkshire Hospital, Reading, UK.
5
Translational Gastroenterology Unit, University of Oxford, UK.
6
Translational Gastroenterology Unit, University of Oxford, UK; Peter Medawar Building, University of Oxford, Oxford, UK. Electronic address: ellie.barnes@ndm.ox.ac.uk.

Abstract

BACKGROUND & AIMS:

Multiparametric magnetic resonance (MR) imaging has been demonstrated to quantify hepatic fibrosis, iron, and steatosis. The aim of this study was to determine if MR can be used to predict negative clinical outcomes in liver disease patients.

METHODS:

Patients with chronic liver disease (n=112) were recruited for MR imaging and data on the development of liver related clinical events were collected by medical records review. The median follow-up was 27months. MR data were analysed blinded for the Liver Inflammation and Fibrosis score (LIF; <1, 1-1.99, 2-2.99, and ⩾3 representing normal, mild, moderate, and severe liver disease, respectively), T2∗ for liver iron content and proportion of liver fat. Baseline liver biopsy was performed in 102 patients.

RESULTS:

Liver disease aetiologies included non-alcoholic fatty liver disease (35%) and chronic viral hepatitis (30%). Histologically, fibrosis was mild in 54 (48%), moderate in 17 (15%), and severe in 31 (28%) patients. Overall mortality was 5%. Ten patients (11%) developed at least one liver related clinical event. The negative predictive value of LIF<2 was 100%. Two patients with LIF 2-2.99 and eight with LIF⩾3 had a clinical event. Patients with LIF⩾3 had a higher cumulative risk for developing clinical events, compared to those with LIF<1 (p=0.02) and LIF 1-1.99 (p=0.03). Cox regression analysis including all 3 variables (fat, iron, LIF) resulted in an enhanced LIF predictive value.

CONCLUSIONS:

Non-invasive standardised multiparametric MR technology may be used to predict clinical outcomes in patients with chronic liver disease.

KEYWORDS:

Iron corrected T(1); LIF score; LiverMultiScan; T(1) mapping

PMID:
26471505
PMCID:
PMC4751288
DOI:
10.1016/j.jhep.2015.10.009
[Indexed for MEDLINE]
Free PMC Article

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