GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes

Ahmad Chaddad; Christian Desrosiers; Matthew Toews

doi:10.1117/12.2214491

21 March 2016 GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes

Ahmad Chaddad, Christian Desrosiers, Matthew Toews

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978424 (2016) https://doi.org/10.1117/12.2214491
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Glioblastoma multiforme (GBM) is the most common malignant primary tumor of the central nervous system, characterized among other traits by rapid metastatis. Three tissue phenotypes closely associated with GBMs, namely, necrosis (N), contrast enhancement (CE), and edema/invasion (E), exhibit characteristic patterns of texture heterogeneity in magnetic resonance images (MRI). In this study, we propose a novel model to characterize GBM tissue phenotypes using gray level co-occurrence matrices (GLCM) in three anatomical planes. The GLCM encodes local image patches in terms of informative, orientation-invariant texture descriptors, which are used here to sub-classify GBM tissue phenotypes. Experiments demonstrate the model on MRI data of 41 GBM patients, obtained from the cancer genome atlas (TCGA). Intensity-based automatic image registration is applied to align corresponding pairs of fixed T1˗weighted (T1˗WI) post-contrast and fluid attenuated inversion recovery (FLAIR) images. GBM tissue regions are then segmented using the 3D Slicer tool. Texture features are computed from 12 quantifier functions operating on GLCM descriptors, that are generated from MRI intensities within segmented GBM tissue regions. Various classifier models are used to evaluate the effectiveness of texture features for discriminating between GBM phenotypes. Results based on T1-WI scans showed a phenotype classification accuracy of over 88.14%, a sensitivity of 85.37% and a specificity of 96.1%, using the linear discriminant analysis (LDA) classifier. This model has the potential to provide important characteristics of tumors, which can be used for the sub-classification of GBM phenotypes.

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Ahmad Chaddad, Christian Desrosiers, and Matthew Toews "GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978424 (21 March 2016); https://doi.org/10.1117/12.2214491

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