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

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.

Radiographic prediction of meningioma grade by semantic and radiomic features

“…However, no sets of phenotypic features have been consistently demonstrated to significantly associate with meningioma grade across studies. Differences between meningioma and low grade glioma was investigated using imaging features from T1-weighed and DWI[ 53 ], however, the study presented several limitations including a small sample size (n = 15). Some studies investigating imaging features suggest that benign tumors display higher ADC while malignant tumors have lower ADC values[ 54 – 56 ], while others fail to corroborate a similar relationship [ 57 – 59 ].…”

The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma

“…They reported that it provided additional information for a definitive diagnosis. Piper et al 32 reported that multimodel MRI parameters (AD, RD, FA, and quantitative longitudinal relaxation time and magnetization transfer ratio) could be useful in differentiating between low-grade glioma and meningioma. To the best of our knowledge, this is the first study reporting that a combined use of all MR DTI parameters (ADCmin, ADCmean, ADCmax, FA, AD, RD, rADCmin, rADCmax, rADCmean, rFA, rAD, and rRD) provides higher diagnostic accuracy for the differentiation of low-from high-grade meningioma.…”

Statistical analysis of radiomic features in differentiation of glioma grades

“…The inability to analyse high and low grade correctly lies at the basis of misdiagnosis of glioma grades [20]. Using T1-weighted and diffusionweighted imaging features, the meningioma and LGG were examined in a small sample set, but the small dataset limited the results [17]. Various radiomics features can be extracted from 2D or 3D tumour images depending on their size and shape.…”