Objective: To predict mutations of key genes in glioblastoma multiforme (GBM) from MR image features. Methods: We obtained mutational and MR image data from 35 patients in the Cancer Genome Atlas (TCGA) GBM database. T1-weighted axial images pre and post gadolinium contrast MRI were processed as follows: a board certified neuro-radiologist traced a region of interest (ROI) around the enhanced part of the largest lesion in the T1 post-contrast MRI and confirmed by comparing it with the T1 pre-contrast image. This ROI was then used to compute features that characterized the intensity of the enhanced lesion, the sharpness of lesion boundaries and the boundary shape. We used the resulting data set to build a linear regression model with regularization to predict the presence of a mutation in terms of image features. We focused on predicting mutations in EGFR because several drugs target it and because EGFR mutations are prevalent in the TCGA GBM data. We evaluated the performance of predicting EGFR mutations from MR imaging data using 5-fold cross validation (5F-CV) and the area under the ROC Curve (AUC). The optimal ROC operating point was defined as the point with the maximal sum of sensitivity and specificity when the cost of false positives and false negatives are considered equivalent. Results: We found that computationally-derived MR image features can predict the presence of EGFR mutations with an AUC of 0.80. The optimal operating point had a sensitivity and specificity of 83% and 93% respectively. The top ranked features in the image-based EGFR-predictor model suggest that EGFR-mutated tumors have blurrier edges than EGFR-wild-type tumors. Conclusion: Our preliminary radiogenomic analysis of GBM suggests MR images may be used to non-invasively determine the mutation status of EGFR, an important drug target in glioblastoma. The ability to determine important genomic aberrations based on image data suggests a increasingly important role for imaging in personalized medicine and warrants further investigation.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5561. doi:1538-7445.AM2012-5561