title: Comparison of Different Post-Processing Algorithms for Dynamic Susceptibility Contrast Perfusion Imaging of Cerebral Gliomas.
abstract: OBJECTIVE
  The purpose of the present study was to compare different software algorithms for processing DSC perfusion images of cerebral tumors with respect to i) the relative CBV (rCBV) calculated, ii) the cutoff value for discriminating low- and high-grade gliomas, and iii) the diagnostic performance for differentiating these tumors.
  
  
  METHODS
  Following approval of institutional review board, informed consent was obtained from all patients. Thirty-five patients with primary glioma (grade II, 9; grade III, 8; and grade IV, 18 patients) were included. DSC perfusion imaging was performed with 3-Tesla MRI scanner. CBV maps were generated by using 11 different algorithms of four commercially available software and one academic program. rCBV of each tumor compared to normal white matter was calculated by ROI measurements. Differences in rCBV value were compared between algorithms for each tumor grade. Receiver operator characteristics analysis was conducted for the evaluation of diagnostic performance of different algorithms for differentiating between different grades.
  
  
  RESULTS
  Several algorithms showed significant differences in rCBV, especially for grade IV tumors. When differentiating between low- (II) and high-grade (III/IV) tumors, the area under the ROC curve (Az) was similar (range 0.85-0.87), and there were no significant differences in Az between any pair of algorithms. In contrast, the optimal cutoff values varied between algorithms (range 4.18-6.53).
  
  
  CONCLUSIONS
  rCBV values of tumor and cutoff values for discriminating low- and high-grade gliomas differed between software packages, suggesting that optimal software-specific cutoff values should be used for diagnosis of high-grade gliomas.
authors.affiliation: Division of Ultra-High Field MRI, Iwate Medical University.
authors.forename: Kohsuke
authors.initials: K
authors.lastname: Kudo
authors.forename: Ikuko
authors.initials: I
authors.lastname: Uwano
authors.forename: Toshinori
authors.initials: T
authors.lastname: Hirai
authors.forename: Ryuji
authors.initials: R
authors.lastname: Murakami
authors.forename: Hideo
authors.initials: H
authors.lastname: Nakamura
authors.forename: Noriyuki
authors.initials: N
authors.lastname: Fujima
authors.forename: Fumio
authors.initials: F
authors.lastname: Yamashita
authors.forename: Jonathan
authors.initials: J
authors.lastname: Goodwin
authors.forename: Satomi
authors.initials: S
authors.lastname: Higuchi
authors.forename: Makoto
authors.initials: M
authors.lastname: Sasaki
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003287:Contrast Media; D005260:Female; D005910:Glioma / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D055420:Perfusion Imaging; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity; D055815:Young Adult
chemical_list: D003287:Contrast Media