European Radiology

, Volume 19, Issue 8, pp 2033–2040

Noninvasive quantitation of human liver steatosis using magnetic resonance and bioassay methods

  • Gaspard d’Assignies
  • Martin Ruel
  • Abdesslem Khiat
  • Luigi Lepanto
  • Miguel Chagnon
  • Claude Kauffmann
  • An Tang
  • Louis Gaboury
  • Yvan Boulanger
Magnetic Resonance

DOI: 10.1007/s00330-009-1351-4

Cite this article as:
d’Assignies, G., Ruel, M., Khiat, A. et al. Eur Radiol (2009) 19: 2033. doi:10.1007/s00330-009-1351-4
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Abstract

The purpose was to evaluate the ability of three magnetic resonance (MR) techniques to detect liver steatosis and to determine which noninvasive technique (MR, bioassays) or combination of techniques is optimal for the quantification of hepatic fat using histopathology as a reference. Twenty patients with histopathologically proven steatosis and 24 control subjects underwent single-voxel proton MR spectroscopy (MRS; 3 voxels), dual-echo in phase/out of phase MR imaging (DEI) and diffusion-weighted MR imaging (DWI) examinations of the liver. Blood or urine bioassays were also performed for steatosis patients. Both MRS and DEI data allowed to detect steatosis with a high sensitivity (0.95 for MRS; 1 for DEI) and specificity (1 for MRS; 0.875 for DEI) but not DWI. Strong correlations were found between fat fraction (FF) measured by MRS, DEI and histopathology segmentation as well as with low density lipoprotein (LDL) and cholesterol concentrations. A Bland-Altman analysis showed a good agreement between the FF measured by MRS and DEI. Partial correlation analyses failed to improve the correlation with segmentation FF when MRS or DEI data were combined with bioassay results. Therefore, FF from MRS or DEI appear to be the best parameters to both detect steatosis and accurately quantify fat liver noninvasively.

Keywords

Liver steatosisMRIMagnetic resonance spectroscopyHistopathologyFat quantitation

Copyright information

© European Society of Radiology 2009

Authors and Affiliations

  • Gaspard d’Assignies
    • 1
  • Martin Ruel
    • 1
  • Abdesslem Khiat
    • 1
  • Luigi Lepanto
    • 1
  • Miguel Chagnon
    • 3
  • Claude Kauffmann
    • 1
  • An Tang
    • 1
  • Louis Gaboury
    • 2
  • Yvan Boulanger
    • 1
    • 4
  1. 1.Département de radiologieCentre hospitalier de l’Université de Montréal (CHUM)MontréalCanada
  2. 2.Département d’anatomo-pathologieCentre hospitalier de l’Université de Montréal (CHUM)MontréalCanada
  3. 3.Département de mathématiques et de statistiqueUniversité de Montréal (UDEM)MontréalCanada
  4. 4.Département de radiologieHôpital Saint-Luc du CHUMMontréalCanada