PLoS ONE (Jan 2013)

Diagnostic performance of a rapid magnetic resonance imaging method of measuring hepatic steatosis.

  • Michael J House,
  • Eng K Gan,
  • Leon A Adams,
  • Oyekoya T Ayonrinde,
  • Sander J Bangma,
  • Prithi S Bhathal,
  • John K Olynyk,
  • Tim G St Pierre

DOI
https://doi.org/10.1371/journal.pone.0059287
Journal volume & issue
Vol. 8, no. 3
p. e59287

Abstract

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ObjectivesHepatic steatosis is associated with an increased risk of developing serious liver disease and other clinical sequelae of the metabolic syndrome. However, visual estimates of steatosis from histological sections of biopsy samples are subjective and reliant on an invasive procedure with associated risks. The aim of this study was to test the ability of a rapid, routinely available, magnetic resonance imaging (MRI) method to diagnose clinically relevant grades of hepatic steatosis in a cohort of patients with diverse liver diseases.Materials and methodsFifty-nine patients with a range of liver diseases underwent liver biopsy and MRI. Hepatic steatosis was quantified firstly using an opposed-phase, in-phase gradient echo, single breath-hold MRI methodology and secondly, using liver biopsy with visual estimation by a histopathologist and by computer-assisted morphometric image analysis. The area under the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the MRI method against the biopsy observations.ResultsThe MRI approach had high sensitivity and specificity at all hepatic steatosis thresholds. Areas under ROC curves were 0.962, 0.993, and 0.972 at thresholds of 5%, 33%, and 66% liver fat, respectively. MRI measurements were strongly associated with visual (r(2) = 0.83) and computer-assisted morphometric (r(2) = 0.84) estimates of hepatic steatosis from histological specimens.ConclusionsThis MRI approach, using a conventional, rapid, gradient echo method, has high sensitivity and specificity for diagnosing liver fat at all grades of steatosis in a cohort with a range of liver diseases.