Exploiting an advanced DTI segmentation technique towards differentiation of GBM and MET

Introduction It is commonly accepted that differentiation between glioblastoma multiforme (GBM) and solitary metastases (MET), relying on conventional Magnetic Resonance Imaging (MRI), in daily clinical practice, remains controversial. Purpose In the frame of such differentiation by means of quantitative image analysis of advanced MR techniques, such as Diffusion Tensor Imaging (DTI), the initial step of tumor segmentation is essential. Materials and methods In this study a proposed state-of-the-art segmentation technique was implemented, based on isotropic (p) and anisotropic (q) maps, derived from diffusion tensor decomposition. The unsupervised k-medians clustering of the 2D (p,q) histogram ( k  = 16, account for 16 different types of brain tissues) results in whole brain segmented maps, where brain tumor lesions present distinctive boundaries. The technique has been tested on a case sample of 10 GBM and 10 MET patients, who underwent preoperative DTI scans at 3Tesla. Results Initial pilot evaluation of the produced brain color maps, by expert observers, demonstrated a potential role of specific tissue segments in precise determination of tumor’s margins, including intratumoral/peritumoral regions. In addition, due to its automated character, the technique is expected to deal with observer variabilities, introduced by manual ROI sampling of the above mentioned tumor regions, representing the current clinical standard. Conclusion The technique implemented lends itself to 3D tumor modeling and is expected to contribute in GMB and metastases differentiation, by means of 3D surface quantitative descriptors, complemented by 3D whole tumor texture analysis.