title: Differentiation of fibroblastic meningiomas from other benign subtypes using diffusion tensor imaging.
abstract: OBJECTIVE
  To differentiate fibroblastic meningiomas, usually considered to be of a hard consistency, from other benign subtypes using diffusion tensor imaging (DTI).
  
  
  METHODS
  From DTI data sets of 30 patients with benign meningiomas, we calculated diffusion tensors and mean diffusivity (MD) and fractional anisotropy (FA) maps as well as barycentric maps representing the geometrical shape of the tensors. The findings were compared to postoperative histology. The study was approved by the local ethics committee, and informed consent was given by the patients.
  
  
  RESULTS
  According to one-way analysis of variance (ANOVA), FA was the best parameter to differentiate between the subtypes (F=32.2; p<0.0001). Regarding tensor shape, endothelial meningiomas were represented by spherical tensors (80%) corresponding to isotropic diffusion, whereas the fibroblastic meningiomas showed a high percentage (43%) of nonspherical tensors, indicating planar or longitudinal diffusion. The difference was highly significant (F=28.4; p<0.0001) and may be due to the fascicular arrangement of long spindle-shaped tumor cells and the high content of intra- and interfascicular fibers as shown in the histology. In addition, a capsule-like rim of the in-plane diffusion surrounded most meningiomas irrespective of their histological type.
  
  
  CONCLUSIONS
  If these results correlate to the intraoperative findings of meningioma consistency, DTI-based measurement of FA and analysis of the shape of the diffusion tensor is a promising method to differentiate between fibroblastic and other subtypes of benign meningiomas in order to get information about their "hard" or "soft" consistency prior to removal.
authors.affiliation: Institute of Neuroradiology, University Clinic, Mainz, Germany. tropine@neuroradio.klinik.uni-mainz.de
authors.forename: Andrei
authors.initials: A
authors.lastname: Tropine
authors.forename: Paulo D
authors.initials: PD
authors.lastname: Dellani
authors.forename: Martin
authors.initials: M
authors.lastname: Glaser
authors.forename: Juergen
authors.initials: J
authors.lastname: Bohl
authors.forename: Till
authors.initials: T
authors.lastname: Plöner
authors.forename: Goran
authors.initials: G
authors.lastname: Vucurevic
authors.forename: Axel
authors.initials: A
authors.lastname: Perneczky
authors.forename: Peter
authors.initials: P
authors.lastname: Stoeter
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D016880:Anisotropy; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging; D005260:Female; D005347:Fibroblasts; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008577:Meningeal Neoplasms / Q000175:diagnosis* / Q000473:pathology; D008579:Meningioma / Q000175:diagnosis* / Q000473:pathology; D008875:Middle Aged; D012984:Software