title: Noninvasive liver steatosis quantification using MRI techniques combined with blood markers.
abstract: OBJECTIVE
  To evaluate the accuracy of different techniques of MRI steatosis quantification, based on histological grading and quantification of liver steatosis.
  
  
  METHODS
  Twenty-three patients (21 with nonalcoholic fatty liver disease and two controls) were included. Steatosis was evaluated in liver specimens using histological grading (five grades) and steatosis area (% of liver surface) was computed using an inhouse automated image analysis. The following five MRI quantification techniques were performed: two-point Dixon, three-point Dixon, DUAL, spin echo method and a new technique called multi-echo gradient-echo (MFGRE). Interobserver (two observers) and intersite (three different liver sites) agreements were evaluated for the two best-performing methods.
  
  
  RESULTS
  Steatosis area was correlated with steatosis grade: Rs (Spearman coefficient) = 0.82, P value of less than 0.001. The steatosis area was significantly different between S0-S2 and S3-S4 grades: 4.2 + or - 2.4 versus 16.4 + or - 8.9% (P< 0.001). Correlations between the MRI techniques and steatosis area (or grading) were: MFGRE, Rs = 0.72 (0.78); spin echo method, Rs = 0.72 (0.76); DUAL, Rs =0.71 (0.76); two-point Dixon, Rs = 0.71 (0.75); three-point Dixon, Rs = 0.67 (0.77). Interobserver (Ric = 0.99) and intersite (Ric = 0.97) agreements were excellent for the liver steatosis measurement by MFGRE. The noninvasive diagnosis of the steatosis area was improved by adding blood markers like ALT and triglycerides to MFGRE (aR2: 0.805).
  
  
  CONCLUSIONS
  MRI, and in particular the MFGRE method, provides accurate and automatic quantification for the noninvasive evaluation of liver steatosis, either as a single measurement or in combination with blood variables.
authors.affiliation: Service des Maladies du foie et de l'appareil Digestif, Angers, France. elmetiviercesbron@chu-angers.fr
authors.forename: Elodie
authors.initials: E
authors.lastname: Cesbron-Métivier
authors.forename: Vincent
authors.initials: V
authors.lastname: Roullier
authors.forename: Jérôme
authors.initials: J
authors.lastname: Boursier
authors.forename: Christine
authors.initials: C
authors.lastname: Cavaro-Ménard
authors.forename: Jérôme
authors.initials: J
authors.lastname: Lebigot
authors.forename: Sophie
authors.initials: S
authors.lastname: Michalak
authors.forename: Paul
authors.initials: P
authors.lastname: Calès
authors.forename: Christophe
authors.initials: C
authors.lastname: Aubé
mesh_terms: D000328:Adult; D000368:Aged; D000410:Alanine Transaminase / Q000097:blood; D015415:Biomarkers / Q000097:blood; D005234:Fatty Liver / Q000097:blood / Q000175:diagnosis* / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D014280:Triglycerides / Q000097:blood
chemical_list: D015415:Biomarkers; D014280:Triglycerides; D000410:Alanine Transaminase