title: Differentiation between low-grade and high-grade glioma using combined diffusion tensor imaging metrics.
abstract: OBJECTIVE
  To ascertain whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as planar and spherical isotropy coefficients (CP and CS) can be used to distinguish high-grade from low-grade gliomas.
  
  
  METHODS
  Twenty-five patients with histologically proved brain gliomas (10 low-grade and 15 high-grade) were included in this study. Contrast-enhanced T1-weighted images, non-diffusion weighted b=0 (b0) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CS and CP maps were co-registered and each lesion was divided into two regions of interest (ROI): enhancing and immediate peritumoral edema (edema adjacent to tumor). Univariate and multivariate logistic regression analyses were applied to determine the best classification model.
  
  
  RESULTS
  There was a statistically significant difference in the multivariate logistic regression analysis. The best logistic regression model for classification combined three parameters (CS, FA and CP) from the immediate peritumoral part (p=0.02), resulting in 86% sensitivity, 80% specificity and area under the curve of 0.81.
  
  
  CONCLUSIONS
  Our study revealed that combined DTI metrics can function in effect as a non-invasive measure to distinguish between low-grade and high-grade gliomas.
authors.affiliation: Digital Medical Research Center, Fudan University, Shanghai, China; Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai, China. Electronic address: linma1206@gmail.com.
authors.forename: Lin
authors.initials: L
authors.lastname: Ma
authors.forename: Zhi Jian
authors.initials: ZJ
authors.lastname: Song
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D016880:Anisotropy; D001932:Brain Neoplasms / Q000175:diagnosis / Q000473:pathology*; D002648:Child; D003937:Diagnosis, Differential; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D005910:Glioma / Q000175:diagnosis / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading / Q000295:instrumentation* / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D055815:Young Adult
keywords: Diffusion tensor imaging; High-grade glioma; Low-grade glioma; Magnetic resonance imaging