title: Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases.
abstract: OBJECTIVE
  Differentiation of glioblastomas and solitary brain metastases is an important clinical problem because the treatment strategy can differ significantly. The purpose of this study was to investigate the potential added value of DTI metrics in differentiating glioblastomas from brain metastases.
  
  
  METHODS
  One hundred twenty-eight patients with glioblastomas and 93 with brain metastases were retrospectively identified. Fractional anisotropy and mean diffusivity values were measured from the enhancing and peritumoral regions of the tumor. Two experienced neuroradiologists independently rated all cases by using conventional MR imaging and DTI. The diagnostic performances of the 2 raters and a DTI-based model were assessed individually and combined.
  
  
  RESULTS
  The fractional anisotropy values from the enhancing region of glioblastomas were significantly higher than those of brain metastases (P < .01). There was no difference in mean diffusivity between the 2 tumor types. A classification model based on fractional anisotropy and mean diffusivity from the enhancing regions differentiated glioblastomas from brain metastases with an area under the receiver operating characteristic curve of 0.86, close to those obtained by 2 neuroradiologists using routine clinical images and DTI parameter maps (area under the curve = 0.90 and 0.85). The areas under the curve of the 2 radiologists were further improved to 0.96 and 0.93 by the addition of the DTI classification model.
  
  
  CONCLUSIONS
  Classification models based on fractional anisotropy and mean diffusivity from the enhancing regions of the tumor can improve diagnostic performance in differentiating glioblastomas from brain metastases.
authors: From the Departments of Radiology (S.W., H.P., J.H.W., S.M., M.R.V., R.L.W.) Sumei.Wang@uphs.upenn.edu.
authors: S
authors: S
authors: Wang
authors: Department of Radiology (S.J.K.), University of Ulsan, Asan Medical Center, Seoul, Republic of Korea.
authors: S J
authors: SJ
authors: Kim
authors: From the Departments of Radiology (S.W., H.P., J.H.W., S.M., M.R.V., R.L.W.).
authors: H
authors: H
authors: Poptani
authors: From the Departments of Radiology (S.W., H.P., J.H.W., S.M., M.R.V., R.L.W.).
authors: J H
authors: JH
authors: Woo
authors: From the Departments of Radiology (S.W., H.P., J.H.W., S.M., M.R.V., R.L.W.).
authors: S
authors: S
authors: Mohan
authors: Medical Data Research Center (R.J.), Providence Health and Services, Portland, Oregon.
authors: R
authors: R
authors: Jin
authors: From the Departments of Radiology (S.W., H.P., J.H.W., S.M., M.R.V., R.L.W.).
authors: M R
authors: MR
authors: Voluck
authors: Neurosurgery (D.M.O.), Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
authors: D M
authors: DM
authors: O'Rourke
authors: From the Departments of Radiology (S.W., H.P., J.H.W., S.M., M.R.V., R.L.W.).
authors: R L
authors: RL
authors: Wolf
authors: Department of Diagnostic Radiology and Nuclear Medicine (E.R.M.), University of Maryland Medical Center, Baltimore, Maryland.
authors: E R
authors: ER
authors: Melhem
authors: Department of Radiology (S.K.), New York University School of Medicine, New York, New York.
authors: S
authors: S
authors: Kim
mesh_terms: D000328:Adult; D000368:Aged; D001932:Brain Neoplasms / Q000473:pathology* / Q000556:secondary*; D003937:Diagnosis, Differential; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D005909:Glioblastoma / Q000473:pathology* / Q000556:secondary*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult