title: Determination of grade and subtype of meningiomas by using histogram analysis of diffusion-tensor imaging metrics.
abstract: OBJECTIVE
  To determine whether histogram analysis of diffusion-tensor (DT) magnetic resonance (MR) imaging metrics, including tensor shape measurements, can help determine the grades and subtypes of meningiomas.
  
  
  METHODS
  The institutional review board approved this HIPAA-compliant study. Nine atypical, three anaplastic, and 39 typical meningiomas were retrospectively studied. The 39 typical meningiomas included one secretory meningioma and 11 fibroblastic, 11 transitional, 14 meningothelial, and two angiomatous meningiomas. DT imaging metrics, including fractional anisotropy, mean diffusivity, linear anisotropy coefficient, planar anisotropy coefficient (CP), spherical anisotropy coefficient (CS), and eigenvalue skewness (SK), as well as normalized signal intensity from contrast-enhanced T1- and T2-weighted images, were measured from the enhancing region of the tumor. Mean, variance, skewness, and kurtosis were extracted from the histograms. A two-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification.
  
  
  RESULTS
  Histogram skewness of SK and kurtosis of SK were significantly higher in atypical and anaplastic meningiomas than in typical meningiomas (P<.01). Among typical meningiomas, significant differences in histogram measures of CP and CS between fibroblastic meningiomas and other subtypes were observed (P<.01). The best model for differentiating atypical and anaplastic meningiomas from typical meningiomas consisted of mean and skewness of SK and kurtosis of T1 signal intensity, with an area under the receiver operating characteristic curve (AUC) of 0.946. The best model for differentiating fibroblastic meningiomas from other subtypes consisted of skewness of T2 signal intensity and kurtosis of CP (AUC, 0.970).
  
  
  CONCLUSIONS
  Histogram analysis of DT imaging metrics can help determine the grades and subtypes of meningiomas, which can better assist in surgical planning.
authors.affiliation: Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, 219 Dulles Bldg, Philadelphia, PA 19104, USA. Sumei.Wang@uphs.upenn.edu
authors.forename: Sumei
authors.initials: S
authors.lastname: Wang
authors.forename: Sungheon
authors.initials: S
authors.lastname: Kim
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Lu
authors.initials: L
authors.lastname: Wang
authors.forename: Edward B
authors.initials: EB
authors.lastname: Lee
authors.forename: Peter
authors.initials: P
authors.lastname: Syre
authors.forename: Harish
authors.initials: H
authors.lastname: Poptani
authors.forename: Elias R
authors.initials: ER
authors.lastname: Melhem
authors.forename: John Y K
authors.initials: JY
authors.lastname: Lee
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008577:Meningeal Neoplasms / Q000473:pathology*; D008579:Meningioma / Q000473:pathology*; D008875:Middle Aged; D060787:Neoplasm Grading; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity