Bayesian Integrative Analysis of Radiogenomics

• Activity Number: 518 - Special Session: Student Paper Competition
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Youyi Zhang* and Jeffrey S Morris and Shivali Narang Aerry and Arvind U.K. Rao and Veera Baladandayuthapani
• Companies: The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical S and The University of Texas M.D. Anderson Cancer Center and Engineering for Professionals, Whiting School of Engineering, Johns Hopkins University and The University of Texas MD Anderson Cancer Center and UT MD Anderson Cancer Center
• Keywords: Radiomics ; Genomics ; Bayesian ; Integrative Analysis

Abstract

We present a multi-stage integrative Bayesian hierarchical model for the analysis of Radiogenomics (imaging genetics) driven by the motivation of linking non-invasive imaging features, multiplatform genomics information and clinical outcomes. Our goals are to identify significant genes and imaging markers as well as the hidden associations between these two platforms, and to further detect the overall clinical relevance. For this task, we established a multi-stage Bayesian hierarchical model which acquires several annovative characteristics: it incorporates integrative analysis of multi-platform genomics data sets to capture fundamental biological mechanism in Radiogenomics framework; explores the associations between imaging markers carrying genetic information with clinical outcomes; detects important genetic markers and imaging markers via establishing hierarchical model with Bayesian continuous shrinkage priors. Applied to the Glioblastoma (GBM) dataset, the model hierarchically identifies important magnetic resonance imaging (MRI) imaging features and the associated genomic platforms that significantly affect patients' survival.