Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer

Conference · Sun Jul 01 00:00:00 EDT 2018

DOI:https://doi.org/10.1117/12.2318508· OSTI ID:1468168

Yoon, Hong-Jun  ^[1]; Ramanathan, Arvind  ^[1]; Alamudun, Folami T.  ^[1]; Tourassi, Georgia  ^[1]

---

1. ORNL

Integration of heterogeneous data from different modalities such as genomics and radiomics is a growing area of research expected to generate better prediction of clinical outcomes in comparison with single modality approaches. To date radiogenomics studies have focused primarily on investigating correlations between genomic and radiomic features, or selection of salient features to determine clinical tumor phenotype. In this study, we designed deep neural networks (DNN), which combine both radiomic and genomic features to predict pathological stage and molecular receptor status of invasive breast cancer patients. Utilizing imaging data from The Cancer Imaging Archive (TCIA) and gene expression data from The Cancer Genome Atlas (TCGA), we evaluated the predictive power of Convolutional Neural Networks (CNN). Overall, results suggest superior performance on CNNs leveraging radiogenomics in comparison with CNNs trained on single modality data sources.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
• Sponsoring Organization: USDOE Office of Science (SC)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1468168
• Resource Relation: Conference: International Workshop on Breast Imaging (IWBI 2018) - Atlanta, Georgia, United States of America - 7/8/2018 8:00:00 AM-7/11/2018 8:00:00 AM
• Country of Publication: United States
• Language: English