Genomic subtype has been shown to be an important predictor of therapy response for patients with glioblastomas. Unfortunately, obtaining the genomic subtype is an expensive process that is not typically included in the standard of care. It is therefore of interest to investigate potential surrogates of molecular subtypes that use standard diagnostic data such as magnetic resonance (MR) imaging. In this study, we analyze the relationship between tumor genomic subtypes, proposed by Verhaak et al, 2010, and novel features that capture the shape of abnormalities as seen in fluid attenuated inversion recovery (FLAIR) MR images. In our study, we used data from 54 patients with glioblastomas from four institutions provided by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). We explore five shape features calculated by computer algorithms implemented in our laboratory that assess shape both in individual slices and in rendered three-dimensional tumor volumes. The association between each feature and molecular subtype was assessed using area under the receiver operating characteristic curve analysis. We show that the two dimensional measures of edge complexity are significant discriminators between mesenchymal and classical tumors. These preliminary findings show promise for an imaging-based surrogate of molecular subtype and contribute to the understanding of the relationship between tumor biology and its radiology phenotype.