title: Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas?
abstract: OBJECTIVE
  The purpose of this study was to examine the relationship between contrast transfer coefficient [K(trans)] and grade in gliomas.
  
  
  METHODS
  Median values of K(trans), CBV(T1), and of the 95th percentile (95%) of the distribution (K(trans) [95%] and CBV(T1) [95%]) were calculated in 39 patients with glioma. Group comparisons and post hoc pairwise comparisons were performed. The relationship between variables and grade used Spearman rho and canonical discriminant analysis. The separation of high- from low-grade tumors was separately assessed by using Mann-Whitney U tests and logistic regression. Receiver operator curve analysis was performed for high- and low-grade tumors and grade III and grade IV tumors.
  
  
  RESULTS
  There were significant differences between grades for all variables (P < .001). Pairwise comparisons demonstrated significant differences between grades II and III and II and IV for all variables except K(trans), which did not show significance in the grade II and III comparison, and between III and IV for CBV(T1) and CBV(T1) (95%; P < .01). All variables correlated with grade (P < .01). Discriminant analysis showed independent relation between both CBV(T1) and K(trans) (95%) and grade, and the canonical function produced a total correct classification of 74.4% of cases. Logistic regression analysis for low- versus high-grade tumors showed K(trans) (95%) and CBV(T1) to be independent factors (P < .01 and P < .05).
  
  
  CONCLUSIONS
  There are strong independent relationships between both CBV and K(trans) and histologic grade in gliomas. Both measurements show good discriminative power in distinguishing between low- and high-grade tumors with diagnostic sensitivity and specificity >90%.
authors.affiliation: Imaging Science and Biomedical Engineering, Faculty of Medicine, University of Manchester, Manchester, United Kingdom.
authors.forename: Tufail F
authors.initials: TF
authors.lastname: Patankar
authors.forename: Hamied A
authors.initials: HA
authors.lastname: Haroon
authors.forename: Samantha J
authors.initials: SJ
authors.lastname: Mills
authors.forename: Danielle
authors.initials: D
authors.lastname: Balériaux
authors.forename: David L
authors.initials: DL
authors.lastname: Buckley
authors.forename: Geoff J M
authors.initials: GJ
authors.lastname: Parker
authors.forename: Alan
authors.initials: A
authors.lastname: Jackson
mesh_terms: D000328:Adult; D000368:Aged; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology* / Q000503:physiopathology; D005260:Female; D005910:Glioma / Q000145:classification / Q000473:pathology* / Q000503:physiopathology; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D012372:ROC Curve; D011859:Radiography; D013687:Telencephalon / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology; D047368:Tumor Burden