Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer

Hong-Jun Yoon; Arvind Ramanathan; Folami Alamudun; Georgia Tourassi

doi:10.1117/12.2318508

6 July 2018 Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer

Hong-Jun Yoon, Arvind Ramanathan, Folami Alamudun, Georgia Tourassi

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 107181H (2018) https://doi.org/10.1117/12.2318508
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

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.

Citation Download Citation

Hong-Jun Yoon, Arvind Ramanathan, Folami Alamudun, and Georgia Tourassi "Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107181H (6 July 2018); https://doi.org/10.1117/12.2318508

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