title: Comparison of DSC-MRI post-processing techniques in predicting microvascular histopathology in patients newly diagnosed with GBM.
abstract: OBJECTIVE
  To evaluate which common post-processing method applied to gradient-echo DSC-MRI data, acquired with a single gadolinium injection and low flip-angle, most accurately reflects microvascular histopathology for patients with de novo, treatment-naive glioblastoma multiforme (GBM).
  
  
  METHODS
  Seventy-two tissue samples were collected from 35 patients with treatment-naive GBM. Sample locations were co-registered to preoperative gradient-echo dynamic susceptibility contrast (DSC) MRI acquired with 35° flip-angle and 0.1 mmol/kg gadolinium. Estimates of blood volume and leakiness at each sample location were calculated using four common postprocessing methods (leakage-corrected nonlinear gamma-variate, non-parametric, scaled MR-signal, and unscaled MR-signal). Tissue sample microvascular morphology was characterized using Factor VIII immunohistochemical analysis. A random-effects regression model, adjusted for repeated measures and contrast-enhancement (CE), identified whether MR parameter estimates significantly predicted IHC findings.
  
  
  RESULTS
  Elevated blood volume estimates from nonlinear and non-parametric methods significantly predicted increased microvascular hyperplasia. Abnormal microvasculature existed beyond the CE-lesion and was significantly reflected by increased blood volume from nonlinear, non-parametric, and scaled MR-signal analysis.
  
  
  CONCLUSIONS
  This study provides histopathological support for both non-parametric and nonlinear post-processing of low flip-angle DSC-MRI for characterizing microvascular hyperplasia within GBM. Non-parametric analysis with a single gadolinium injection may be a particularly useful strategy clinically, as it requires less computational expense and limits gadolinium exposure.
authors.affiliation: The UC Berkeley - UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA 94158, USA. emma.essock-burns@ucsf.edu
authors.forename: Emma
authors.initials: E
authors.lastname: Essock-Burns
authors.forename: Joanna J
authors.initials: JJ
authors.lastname: Phillips
authors.forename: Annette M
authors.initials: AM
authors.lastname: Molinaro
authors.forename: Janine M
authors.initials: JM
authors.lastname: Lupo
authors.forename: Soonmee
authors.initials: S
authors.lastname: Cha
authors.forename: Susan M
authors.initials: SM
authors.lastname: Chang
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Nelson
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D002536:Cerebral Arteries / Q000473:pathology*; D005260:Female; D005909:Glioblastoma / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D055806:Microvessels / Q000473:pathology*; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: DSC post-processing; low flip angle; microvascular histopathology; perfusion MRI; tissue sample