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Diagnostic Utility of Diffusion Tensor Imaging in Differentiating Glioblastomas from Brain Metastases

Diagnostic Utility of Diffusion Tensor Imaging in Differentiating Glioblastomas from Brain... 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. ABBREVIATIONS: AUC area under the curve ER enhancing region FA fractional anisotropy IPR immediate peritumoral region LRM logistic regression model MD mean diffusivity http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Neuroradiology American Journal of Neuroradiology

Diagnostic Utility of Diffusion Tensor Imaging in Differentiating Glioblastomas from Brain Metastases

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Publisher
American Journal of Neuroradiology
Copyright
Copyright © 2014 by the American Society of Neuroradiology.
ISSN
0195-6108
eISSN
1936-959X
DOI
10.3174/ajnr.A3871
pmid
24503556
Publisher site
See Article on Publisher Site

Abstract

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. ABBREVIATIONS: AUC area under the curve ER enhancing region FA fractional anisotropy IPR immediate peritumoral region LRM logistic regression model MD mean diffusivity

Journal

American Journal of Neuroradiology – American Journal of Neuroradiology

Published: May 1, 2014

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