Olivier Gevaert PhD, Presenter: Nothing to Disclose

Lex Allen Mitchell MD, Abstract Co-Author: Nothing to Disclose

Achal Achrol, Abstract Co-Author: Nothing to Disclose

Jiajing Xu MS, Abstract Co-Author: Nothing to Disclose

Gary K Steinberg MD, PhD, Abstract Co-Author: Nothing to Disclose

Samuel H Cheshier, Abstract Co-Author: Nothing to Disclose

Sandy Napel PhD, Abstract Co-Author: Medical Advisory Board, Fovia, Inc Consultant, Carestream Health, Inc Scientific Advisor, Echopixel, Inc

Greg Zaharchuk MD, PhD, Abstract Co-Author: Research Grant, General Electric Company

Sylvia Katina Plevritis PhD, Abstract Co-Author: Nothing to Disclose

To create mappings between quantitative image and genomic features for glioblastoma multiforme (GBM) and to assess the prognostic association of significant correlations.

We obtained multi-omics data from 251 patients and MR image data from a subset of 55 patients in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) GBM databases. A board certified neuroradiologist traced 2D regions of interest (ROI) around necrotic and enhanced parts of the largest lesion in a selected slice from a T1 post-contrast MR, and around the region of hyperintensity obtained from the enhancement on the matched T2 FLAIR slice. These ROIs were used to compute quantitative image features from their shapes and pixel values. We used a module network algorithm that integrates copy number, DNA methylation and gene expression data into 100 co-expressed gene modules, modeled by sparse linear regression of driver genes, which were selected based on a significant correlation of copy number or DNA methylation with their respective gene expression. We established a radiogenomics map by correlating the modules with the quantitative image features, and correlated the image features from this map with significant correlations with survival using Cox proportional hazards modeling.

A total of 28 quantitative image features were extracted for each of the necrosis, enhancement and edema ROIs in each patient. The radiogenomics map between modules and quantitative image features revealed 14, 10 and 16 significant gene-module associations with necrosis, enhancement and edema ROIs respectively. For example we found a significant correlation between Module 64, enriched with genes in neuronal differentiation, and the compactness of the necrosis (p=0.0145). Also, we found that the amount of necrosis vs. enhancement or edema is correlated with Module 74, enriched in metabolism related genes (p<0.01). Finally, we found e.g. that the compactness of the necrosis ROI is correlated with poor survival (p=0.037).

Creating radiogenomics maps provides multi-scale insight by associating image features with molecular function. Moreover, these maps may provide additional insight for image features with prognostic correlations.

Associating activation of molecular pathways with image features has the potential of allowing non-invasive assessment of the molecular properties of a tumor at the time of diagnosis.

Gevaert, O, Mitchell, L, Achrol, A, Xu, J, Steinberg, G, Cheshier, S, Napel, S, Zaharchuk, G, Plevritis, S, Creating a Radiogenomics Map of Multi-omics and Quantitative Image Features in Glioblastoma Multiforme.  Radiological Society of North America 2013 Scientific Assembly and Annual Meeting, December 1 - December 6, 2013 ,Chicago IL. http://archive.rsna.org/2013/13014806.html