P044] Imaging biomarker analysis of structural MR images for glioma grading

Abstract

Purpose Preoperative differentiation between low and high grade gliomas is critical for adapting appropriate treatment strategies. However biopsy based diagnosis presents limitations in assessing tumor diffusive and solid growth patterns, respectively, due to the potential subsampling of tumor region as well as the associated patient risk. The purpose of this study is to investigate the value of imaging biomarker analysis in terms of a 3D texture analysis of T1 pre/post-contrast, T2, and DTI maps in the evaluation of tumor heterogeneity towards glioma grading. Methods In this study a semi-automated segmentation technique, based on T1 pre/post-contrast, T2, and DTI parametric maps, is used for the identification of the tumor core and peritumoral edema regions from 3 Tesla MR Data. 3D Texture analysis was performed on 10 low grade and 10 high grade glioma patients, considering several 3D Histogram, Gray Level Co-Occurrence Matrices (GLCM) and Gray Level Run Length Matrices (GLRLM) derived features. Results Initial evaluation of the proposed method is performed in terms of textural features statistical significant differences between the two tumor groups, with the prospect of implementing an advance classification scheme for identifying more complex correlations. The preliminary results suggest that 3D Texture Analysis findings might be utilized as a surrogate biomarker for the global non-invasive assessment of glioma grading. Conclusions Results demonstrate the potential role of Texture Analysis of 3D tumor imaging appearance in structural MRI, for pre-treatment gliomas grade differentiation.