Somatic mutations associated with MRI-derived volumetric features in glioblastoma

Neuroradiology
David GutmanHugo J W L Aerts

Abstract

MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM). Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status. Our results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For exa...Continue Reading

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Methods Mentioned

BETA
biopsy
exome sequencing
feature extraction

Software Mentioned

Linux
R
VASARI
cgdsr
fslstats
FSL FAST tool
FMRIB Automated Segmentation Tool
FSL
FMRIB Software Library
FSLView

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