MRI images could be indicative of Genetic variations in Glioblastoma

In this exploratory study, we evaluate the feasibility of using multi-parametric MRI and texture analysis to characterize the regional genetic heterogeneity throughout the ENH and BAT tumor segments of GBM. To accomplish this, we have collected multiple image-guided biopsies throughout both segments in a cohort of patients with primary GBM. For each biopsy, we have analyzed genome-wide DNA somatic copy number variants (CNVs) to determine the regional status for highly recurrent and biologically significant GBM driver genes, as previously determined by TCGA. These include known therapeutic targets and core GBM pathways such as receptor tyrosine kinase (RTK), PI3K, MAPK, p53, and Rb1. We have coregistered each biopsy location with preoperative multi-parametric MRI, which includes CE-MRI, DWI, DTI, and pMRI. This allows us to correlate regional genetic status with spatially matched imaging measurements, including raw MRI signal and MRI-based texture features. Finally, we have used both univariate and multivariate analyses to determine which MRI-based features correlate most strongly with regional status for each driver gene. Our overarching goal is to develop image-based biomarkers that can improve diagnostic accuracy and treatment selection under the paradigm of individualized oncology.