title: Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas.
abstract: BACKGROUND
  The ability of preoperative MRI-sequences to predict the consistency of intracranial meningiomas has not yet been clearly defined. We aim to demonstrate that diffusion tensor imaging (DTI) improves the prediction of intracranial meningiomas consistency.
  
  
  METHODS
  We prospectively studied 110 meningioma patients operated on in a single center from March 1st to the 25th of May 2012. Demographic data, location and size of the tumor, peritumoral edema, T1WI, T2WI, proton density weighted (PDWI), fluid-attenuated inversion recover (FLAIR) sequences, and arterial spin labeling (ASL) perfusion were studied and compared with the gray matter signal to predict meningioma consistency. Diffusion tensor imaging (DTI) with fractional anisotropy (FA) and mean diffusivity (MD) maps were included in the preoperative MRI. Meningioma consistency was evaluated by the operating surgeon who was unaware of the neuroradiological findings.
  
  
  RESULTS
  In univariate analysis, meningioma size (diameter > 2 cm) and supratentorial or sphenoidal wing location were more frequently associated with hard-consistency meningiomas (p < 0.05). In addition, isointense signal on MD maps (p = 0.009), hyperintense signal on FA maps, and FA value > 0.3 (p = 0.00001) were associated with hard-consistency tumors. Age and sex, T1WI, T2WI, PDWI, FLAIR, or ASL perfusion sequences and peritumoral edema were not significantly associated with meningioma consistency. In logistic regression analysis, the most accurate model (AUC: 0.9459) for predicting a hard-consistency meningioma shows that an isointense signal in MD-maps, a hyperintense signal in FA-maps, and an FA value of more than 0.3 have a significant predictive value.
  
  
  CONCLUSIONS
  FA value and MD and FA maps are useful for prediction of meningioma consistency and, therefore, may be considered in the preoperative routine MRI examination of all patients with intracranial meningiomas.
authors.affiliation: Department of Neurosurgery, Huashan Hospital, Fudan University, 12#, Wulumuqi Zhong Road, 200040, Shanghai, People's Republic of China, rossana.romani@helsinki.fi.
authors.forename: Rossana
authors.initials: R
authors.lastname: Romani
authors.forename: Wei-Jun
authors.initials: WJ
authors.lastname: Tang
authors.forename: Ying
authors.initials: Y
authors.lastname: Mao
authors.forename: Dai-Jun
authors.initials: DJ
authors.lastname: Wang
authors.forename: Hai-Liang
authors.initials: HL
authors.lastname: Tang
authors.forename: Feng-Ping
authors.initials: FP
authors.lastname: Zhu
authors.forename: Xiao-Ming
authors.initials: XM
authors.lastname: Che
authors.forename: Ye
authors.initials: Y
authors.lastname: Gong
authors.forename: Kang
authors.initials: K
authors.lastname: Zheng
authors.forename: Ping
authors.initials: P
authors.lastname: Zhong
authors.forename: Shi-Qi
authors.initials: SQ
authors.lastname: Li
authors.forename: Wei-Min
authors.initials: WM
authors.lastname: Bao
authors.forename: Christian
authors.initials: C
authors.lastname: Benner
authors.forename: Jing-Song
authors.initials: JS
authors.lastname: Wu
authors.forename: Liang-Fu
authors.initials: LF
authors.lastname: Zhou
mesh_terms: D000328:Adult; D000368:Aged; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008577:Meningeal Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D008579:Meningioma / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis