Determination of Grade and Subtype of Meningiomas by Using Histogram Analysis of Diffusion-Tensor Imaging Metrics

Abstract

Purpose:To determine whether histogram analysis of diffusiontensor (DT) magnetic resonance (MR) imaging metrics, including tensor shape measurements, can help determine the grades and subtypes of meningiomas. Materials and Methods:The institutional review board approved this HIPAAcompliant study. Nine atypical, three anaplastic, and 39 typical meningiomas were retrospectively studied. The 39 typical meningiomas included one secretory meningioma and 11 fi broblastic, 11 transitional, 14 meningothelial, and two angiomatous meningiomas. DT imaging metrics, including fractional anisotropy, mean diffusivity, linear anisotropy coeffi cient, planar anisotropy coeffi cient (CP), spherical anisotropy coeffi cient (CS), and eigenvalue skewness (SK), as well as normalized signal intensity from contrastenhanced T1-and T2-weighted images, were measured from the enhancing region of the tumor. Mean, variance, skewness, and kurtosis were extracted from the histograms. A two-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classifi cation. Results:Histogram skewness of SK and kurtosis of SK were significantly higher in atypical and anaplastic meningiomas than in typical meningiomas ( P , .01). Among typical meningiomas, signifi cant differences in histogram measures of CP and CS between fi broblastic meningiomas and other subtypes were observed ( P , .01). The best model for differentiating atypical and anaplastic meningiomas from typical meningiomas consisted of mean and skewness of SK and kurtosis of T1 signal intensity , with an area under the receiver operating characteristic curve (AUC) of 0.946. The best model for differentiating fi broblastic meningiomas from other subtypes consisted of skewness of T2 signal intensity and kurtosis of CP (AUC, 0.970). Conclusion:Histogram analysis of DT imaging metrics can help determine the grades and subtypes of meningiomas, which can better assist in surgical planning.q RSNA, 2011Supplemental material: http://radiology.rsna.org/lookup /suppl

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

“…26 These findings can be explained by the presence of a large amount of collagen in fibroblastic subtypes. 25,33 It has been reported that the structure and orientation of extracellular matrix affect the anisotropy, but not the mean diffusivity 34 ; these results are consistent with ours. On the other hand, in brain metastases, neoplasm cells grow into the brain parenchyma in an expansive, noninfiltrating pattern.…”

“…The remaining region of FLAIR abnormality, outside of the IPR, was the distant peritumoral region. 12,23,25 The median FA and MD values were measured from the 3 regions. Data analysis tools, including image coregistration and segmentation, were implemented by using IDL (Exelis Visual Information Solutions, Boulder, Colorado).…”

Assessment of Angiographic Vascularity of Meningiomas with Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging and Diffusion Tensor Imaging

“…Previous studies investigated the roles of ADC and FA in subtyping meningiomas, but the results were controversial. 7,8,27 In the present study, we found that angiographic vascularity of meningiomas correlated with FA but not with ADC. Whereas ADC measures the average changes of water diffusivity, FA quantifies the diffusion anisotropy.…”

“…5 DSC-PWI, on the other hand, measures T2*-weighted signal intensity loss that occurs dynamically over bolus injection of contrast medium, from which relative CBV, a quantitative marker of tumor angiogenesis, can be computed. 6 Both DTI 7,8 and DSC-PWI [9][10][11] had been reported to be useful in subtyping meningiomas. The microvessel area of meningiomas determined by histopathology was found to correlate with relative CBV derived from DSC-PWI.…”

Impaired integrity of the brain parenchyma in non-geriatric patients with major depressive disorder revealed by diffusion tensor imaging

“…Additional indices that can be evaluated include skewness (the magnitude of symmetry of the histograms) and kurtosis (peakness of histograms) (Mori et al, 2008). Histogram analysis of DTI has been successfully used in brain tumor classification (Wang et al, 2012), investigation of clinical correlates in leukoariosis (Della Nave et al, 2007) and multiple sclerosis (Cercignani et al, 2001), differentiation between ischemic and non-ischemic patients with moyamoya disease (Mori et al, 2008), evaluation of traumatic brain inury (Marquez de la Plata et al, 2011), and so on. In this study, histogram analysis enabled a discrimination between the patients and normal subjects by its peak position, which is suggestive of its applicability in the evaluation of integrity of brain parenchyma in MDD.…”