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Imaging signatures of meningioma and low-grade glioma: a diffusion tensor, magnetization transfer and quantitative longitudinal relaxation time MRI study

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http://www.sciencedirect.com/science/article/pii/S0730725X1500301X
Original languageEnglish
Pages (from-to)596-602
JournalMagnetic Resonance Imaging
Volume34
Early online date17 Dec 2015
DOIs
Publication statusPublished - May 2016

Abstract

Differentiation of cerebral tumor pathology currently relies on interpretation of conventional structural MRI and in some cases histology. However, more advanced MRI methods may provide further insight into the organization of cerebral tumors and have the potential to aid diagnosis. The objective of this study was to use multimodal quantitative MRI to measure the imaging signatures of meningioma and low-grade glioma (LGG). Nine adults with meningioma and 11 with LGG were identified, and underwent standard structural, quantitative longitudinal relaxation time (T1) mapping, magnetization transfer and diffusion tensor MRI. Maps of mean (〈D〉), axial (λAX) and radial (λRAD) diffusivity, fractional anisotropy (FA), magnetization transfer ratio (MTR) and T1 were generated on a voxel-by-voxel basis. Using structural and echo-planar T2-weighted MRI, manual region-of-interest segmentation of brain tumor, edema, ipsilateral and contralateral normal-appearing white matter (NAWM) was performed. Differences in imaging signatures between the different tissue types, both absolute mean values and ratios relative to contralateral NAWM, were assessed using t-tests with statistical significance set at p < 0.05. For both absolute mean values and ratios relative to contralateral NAWM, there were significant differences in 〈D〉, λAX, λRAD, FA, MTR and T1 between meningioma and LGG tumor tissue, respectively. Only T1 and FA differed significantly between edematous tissue associated with the two tumor types. These results suggest that multimodal MRI biomarkers are significantly different, particularly in tumor tissue, between meningioma and LGG. By using quantitative multimodal MRI it may be possible to identify tumor pathology non-invasively.

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