Local curvature analysis for differentiating Glioblastoma multiforme from solitary metastasis

Ambiguous imaging appearance of Glioblastoma multiforme (GBM) and solitary metastasis (MET) is a challenge to conventional Magnetic Resonance Imaging (MRI) based diagnosis. In this study, a local curvature analysis scheme is implemented to enable morphological differentiation between GBMs and METs. The first stage of the scheme takes advantage of a Diffusion Tensor Imaging (DTI) clustering segmentation technique, complemented by post-contrast T1 imaging for final tumor boundary definition. 3D tumor models are generated by morphological morphing interpolation to compensate for low z-axis resolution of a widely utilized MRI acquisition protocol, followed by triangulated surface mesh generation. Five 3D morphology descriptors, based on local curvature analysis, are tested in a pilot case of 12 lesions (8 GBMs and 4 METs) in terms of morphology differentiation capability, utilizing four first order statistics. Statistically significant differences are identified for all five descriptors tested, however for a varying first order statistics. Results demonstrate the potential of morphology analysis in pre-treatment brain MRI tumor differentiation. © 2016 IEEE

Exploiting morphology and texture of 3D tumor models in DTI for differentiating glioblastoma multiforme from solitary metastasis

Ambiguous imaging appearance of Glioblastoma Multiforme (GBM) and solitary Metastasis (MET) is a challenge to conventional Magnetic Resonance Imaging (MRI) based diagnosis, leading to exploitation of advanced MRI techniques, such as Diffusion Tensor Imaging (DTI). In this study, 3D tumor models are generated by a DTI clustering segmentation technique, providing up to 16 brain tissue diffusivities, complemented by T1 post-contrast imaging, resulting in the identification of tumor core, whose surface is refined by a Morphological Morphing interpolation technique. The 3D models are analyzed in terms of their surface and internal signal variations characteristics towards identification of discriminant features for differentiation between GBMs and METs, utilizing a case sample composed of 10 GBMs and 10 METs. Morphology analysis of tumor core surface is assessed by 5 local curvature features. Texture analysis considers 11 first and 16 second order 3D textural features. From the 16 second order features, 11 are based on Gray Level Co-Occurrence Matrices (GLCM) and 5 on Gray Level Run Length Matrices (GLRLM), calculated from DTI isotropic and anisotropic parametric maps, corresponding to 3D tumor core segmented from the clustering technique. Also, 3 different image quantization levels (QL) were tested for both GLCM and GLRLM analysis, while 1–4 pixel displacements (D) in case of GLCM analysis. Case sample distributions of morphology and texture features were analyzed using the Mann-Whitney U test, with a cut-off value of 0.05 to identify discriminant features. The discriminatory performance of the derived features was analyzed with Receiver Operating Characteristic (ROC) curve analysis. Results highlight the value of all 5 local curvature descriptors to capture differences between the boundary of GBMs and METs. Histogram analysis of isotropy maps revealed statistical significant differences for median value and kurtosis, while 7 out of the 11 GLCM features were capable of discriminating heterogeneity of anisotropic diffusion properties of GBMs and METs, at QL = 6 and D = 2. Finally, all 5 GLRLM features extracted from diffusion isotropy maps seem to discriminate structural properties of GBMs and METs, at QL = 5. Results demonstrate the potential of surface morphology and texture analysis of 3D tumor imaging appearance in pre-treatment brain MRI tumor differentiation. © 2018 Elsevier Lt