title: Noninvasive classification of hepatic fibrosis based on texture parameters from double contrast-enhanced magnetic resonance images.
abstract: OBJECTIVE
  To demonstrate a proof of concept that quantitative texture feature analysis of double contrast-enhanced magnetic resonance imaging (MRI) can classify fibrosis noninvasively, using histology as a reference standard.
  
  
  METHODS
  A Health Insurance Portability and Accountability Act (HIPAA)-compliant Institutional Review Board (IRB)-approved retrospective study of 68 patients with diffuse liver disease was performed at a tertiary liver center. All patients underwent double contrast-enhanced MRI, with histopathology-based staging of fibrosis obtained within 12 months of imaging. The MaZda software program was used to compute 279 texture parameters for each image. A statistical regularization technique, generalized linear model (GLM)-path, was used to develop a model based on texture features for dichotomous classification of fibrosis category (F ≤2 vs. F ≥3) of the 68 patients, with histology as the reference standard. The model's performance was assessed and cross-validated. There was no additional validation performed on an independent cohort.
  
  
  RESULTS
  Cross-validated sensitivity, specificity, and total accuracy of the texture feature model in classifying fibrosis were 91.9%, 83.9%, and 88.2%, respectively.
  
  
  CONCLUSIONS
  This study shows proof of concept that accurate, noninvasive classification of liver fibrosis is possible by applying quantitative texture analysis to double contrast-enhanced MRI. Further studies are needed in independent cohorts of subjects.
authors.affiliation: University of California, San Diego, Department of Radiology, San Diego, California 92103, USA.
authors.forename: Gautam
authors.initials: G
authors.lastname: Bahl
authors.forename: Irene
authors.initials: I
authors.lastname: Cruite
authors.forename: Tanya
authors.initials: T
authors.lastname: Wolfson
authors.forename: Anthony C
authors.initials: AC
authors.lastname: Gamst
authors.forename: Julie M
authors.initials: JM
authors.lastname: Collins
authors.forename: Alyssa D
authors.initials: AD
authors.lastname: Chavez
authors.forename: Fatma
authors.initials: F
authors.lastname: Barakat
authors.forename: Tarek
authors.initials: T
authors.lastname: Hassanein
authors.forename: Claude B
authors.initials: CB
authors.lastname: Sirlin
mesh_terms: D000328:Adult; D000368:Aged; D003287:Contrast Media / Q000008:administration & dosage; D003911:Dextrans / Q000008:administration & dosage; D005260:Female; D019786:Gadolinium DTPA / Q000008:administration & dosage; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008103:Liver Cirrhosis / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D058185:Magnetite Nanoparticles / Q000008:administration & dosage; D008297:Male; D008875:Middle Aged; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D003911:Dextrans; D058185:Magnetite Nanoparticles; C097921:ferumoxtran-10; C064925:gadodiamide; D019786:Gadolinium DTPA