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Proceedings Paper

Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer
Author(s): Hong-Jun Yoon; Arvind Ramanathan; Folami Alamudun; Georgia Tourassi
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Paper Abstract

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.

Paper Details

Date Published: 6 July 2018
PDF: 6 pages
Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107181H (6 July 2018); doi: 10.1117/12.2318508
Show Author Affiliations
Hong-Jun Yoon, Oak Ridge National Lab. (United States)
Arvind Ramanathan, Oak Ridge National Lab. (United States)
Folami Alamudun, Oak Ridge National Lab. (United States)
Georgia Tourassi, Oak Ridge National Lab. (United States)

Published in SPIE Proceedings Vol. 10718:
14th International Workshop on Breast Imaging (IWBI 2018)
Elizabeth A. Krupinski, Editor(s)

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