Tissue signature characterisation of diffusion tensor abnormalities in cerebral gliomas

Abstract

The inherent invasiveness of malignant cells is a major determinant of the poor prognosis of cerebral gliomas. Diffusion tensor MRI (DTI) can identify white matter abnormalities in gliomas that are not seen on conventional imaging. By breaking down DTI into its isotropic (p) and anisotropic (q) components, we can determine tissue diffusion "signatures". In this study we have characterised these abnormalities in peritumoural white matter tracts. Thirty-five patients with cerebral gliomas and seven normal volunteers were imaged with DTI and T2-weighted sequences at 3 T. Displaced, infiltrated and disrupted white matter tracts were identified using fractional anisotropy (FA) maps and directionally encoded colour maps and characterised using tissue signatures. The diffusion tissue signatures were normal in ROIs where the white matter was displaced. Infiltrated white matter was characterised by an increase in the isotropic component of the tensor (p) and a less marked reduction of the anisotropic component (q). In disrupted white matter tracts, there was a marked reduction in q and increase in p. The direction of water diffusion was grossly abnormal in these cases. Diffusion tissue signatures may be a useful method of assessing occult white matter infiltration.

Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation

“…• When diffusing into the brain parenchyma, the tumor also affects the fibers of the white matter [40], [22]. This modification of the fiber structure in the invaded area could be taken into account by updating the diffusion tensor .…”

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra

“…Also, it will facilitate the use of third-party software for exploration and processing of the clinical, imaging and spectroscopic data available, particularly freely available DICOM viewers (55). Incorporating quantitative data from other MR-based methodologies (DWI, DTI, PWI) to classifier systems based on MRS for primary diagnosis of tumour type, or for determining the extent of glioma infiltration with a high degree of spatial resolution (39,(56)(57)(58)(59)(60).…”

Imaging biomarkers of brain tumour margin and tumour invasion

“…This study pooled the ADC values from tumour and peritumoural brain from a variety of tumour grades (and only included two glioblastomas) and is likely to be confounded by the variation of ADC values with tumour grade. Another study comparing diffusion tissue signatures [58,59] with tissue from image-guided biopsies avoided this problem by comparing the values with histology in individual patients [60]. Using this method it was possible to differentiate normal tissue from tumour or tumourinvaded brain with a sensitivity of 98% and specificity of 81%.…”