RT Journal Article
SR Electronic
T1 Diagnostic Utility of Diffusion Tensor Imaging in Differentiating Glioblastomas from Brain Metastases
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 928
OP 934
DO 10.3174/ajnr.A3871
VO 35
IS 5
A1 S. Wang
A1 S.J. Kim
A1 H. Poptani
A1 J.H. Woo
A1 S. Mohan
A1 R. Jin
A1 M.R. Voluck
A1 D.M. O'Rourke
A1 R.L. Wolf
A1 E.R. Melhem
A1 S. Kim
YR 2014
UL http://www.ajnr.org/content/35/5/928.abstract
AB BACKGROUND AND PURPOSE: 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. MATERIALS AND 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. AUCarea under the curveERenhancing regionFAfractional anisotropyIPRimmediate peritumoral regionLRMlogistic regression modelMDmean diffusivity