title: Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade.
abstract: OBJECTIVE
  Numerous different parameters measured by perfusion MR imaging can be used for characterizing gliomas. Parameters derived from 3 different analyses were correlated with histopathologically confirmed grade in gliomas to determine which parameters best predict tumor grade.
  
  
  METHODS
  Seventy-four patients with gliomas underwent dynamic susceptibility contrast-enhanced MR imaging (DSC MR imaging). Data were analyzed by 3 different algorithms. Analysis 1 estimated relative cerebral blood volume (rCBV) by using a single compartment model. Analysis 2 estimated fractional plasma volume (V(p)) and vascular transfer constant (K(trans)) by using a 2-compartment pharmacokinetic model. Analysis 3 estimated absolute cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) by using a single compartment model and an automated arterial input function. The Mann-Whitney U test was used make pairwise comparisons. Binary logistic regression was used to assess whether rCBV, V(p), K(trans), CBV, CBF, and MTT can discriminate high- from low-grade tumors.
  
  
  RESULTS
  rCBV was the best discriminator of tumor grade ype, followed by CBF, CBV, and K(trans). Spearman rank correlation factors were the following: rCBV = 0.812 (P < .0001), CBF = 0.677 (P < .0001), CBV = 0.604 (P < .0001), K(trans) = 0.457 (P < .0001), V(p) = 0.301 (P =.009), and MTT = 0.089 (P = .448). rCBV was the best single predictor, and K(trans) with rCBV was the best set of predictors of high-grade glioma.
  
  
  CONCLUSIONS
  rCBV, CBF, CBV K(trans), and V(p) measurements correlated well with histopathologic grade. rCBV was the best predictor of glioma grade, and the combination of rCBV with K(trans) was the best set of metrics to predict glioma grade.
authors.affiliation: Department of Radiology, NYU Medical Center, New York, NY 10016, USA. lawm01@med.nyu.edu
authors.forename: M
authors.initials: M
authors.lastname: Law
authors.forename: R
authors.initials: R
authors.lastname: Young
authors.forename: J
authors.initials: J
authors.lastname: Babb
authors.forename: M
authors.initials: M
authors.lastname: Rad
authors.forename: T
authors.initials: T
authors.lastname: Sasaki
authors.forename: D
authors.initials: D
authors.lastname: Zagzag
authors.forename: G
authors.initials: G
authors.lastname: Johnson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001783:Blood Flow Velocity / Q000502:physiology; D001810:Blood Volume / Q000502:physiology; D001921:Brain / Q000098:blood supply / Q000473:pathology; D001932:Brain Neoplasms / Q000098:blood supply* / Q000473:pathology; D002199:Capillary Permeability / Q000502:physiology; D002648:Child; D002675:Child, Preschool; D003287:Contrast Media / Q000008:administration & dosage / Q000493:pharmacokinetics*; D005260:Female; D019786:Gadolinium DTPA / Q000493:pharmacokinetics*; D005910:Glioma / Q000098:blood supply* / Q000473:pathology; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D018810:Magnetic Resonance Angiography; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009389:Neovascularization, Pathologic / Q000175:diagnosis* / Q000473:pathology; D011379:Prognosis; D012039:Regional Blood Flow / Q000502:physiology; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA