CCM Seminar: Shariq Mohammed

Date & Time

Title: “Radiohead: Radiogenomic analysis incorporating tumor heterogeneity in imaging through densities”

Abstract: Recent technological advancements have enabled detailed investigation of associations between the molecular architecture and tumor heterogeneity, through multi-source integration of radiological imaging and genomic (radiogenomic) data. We integrate and harness radiogenomic data in patients with lower grade gliomas (LGG), a type of brain cancer, in order to develop a regression framework called RADIOHEAD (RADIOgenomic analysis incorporating tumor HEterogeneity in imAging through Densities) to identify radiogenomic associations. Imaging data is represented through voxel intensity probability density functions of tumor sub-regions obtained from multimodal magnetic resonance imaging, and genomic data through molecular signatures in the form of pathway enrichment scores based on gene expression profiles. Employing a Riemannian-geometric framework for principal component analysis on the set of probability densities functions, we map each probability density to a vector of principal component scores, which are then included as predictors in a Bayesian regression model with the pathway enrichment scores as the response. Variable selection compatible with the grouping structure amongst the predictors induced through the tumor sub-regions is carried out under a group spike-and-slab prior. Our analyses reveal several pathways relevant to LGG etiology to have significant associations with the corresponding imaging-based predictors.

About the Speaker

Dr. Mohammed is a postdoctoral research fellow in the Departments of Biostatistics, and Computational Medicine and Bioinformatics at The University of Michigan-Ann Arbor. He is also a Precision Health Scholar at the University of Michigan Precision Health. His current research is focused on building statistical methods for biomedical imaging data with applications in cancer and other disease areas.

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