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Title: Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1468168
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
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

Citation Formats

Yoon, Hong-Jun, Ramanathan, Arvind, Alamudun, Folami T., and Tourassi, Georgia. Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer. United States: N. p., 2018. Web. doi:10.1117/12.2318508.
Yoon, Hong-Jun, Ramanathan, Arvind, Alamudun, Folami T., & Tourassi, Georgia. Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer. United States. https://doi.org/10.1117/12.2318508
Yoon, Hong-Jun, Ramanathan, Arvind, Alamudun, Folami T., and Tourassi, Georgia. 2018. "Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer". United States. https://doi.org/10.1117/12.2318508. https://www.osti.gov/servlets/purl/1468168.
@article{osti_1468168,
title = {Deep radiogenomics for predicting clinical phenotypes in invasive breast cancer},
author = {Yoon, Hong-Jun and Ramanathan, Arvind and Alamudun, Folami T. and Tourassi, Georgia},
abstractNote = {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.},
doi = {10.1117/12.2318508},
url = {https://www.osti.gov/biblio/1468168}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {7}
}

Conference:
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Works referenced in this record:

Radiogenomics: Creating a link between molecular diagnostics and diagnostic imaging
journal, May 2009


Radiogenomic Analysis of Breast Cancer Using MRI: A Preliminary Study to Define the Landscape
journal, September 2012


The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
journal, July 2013


Representation learning for mammography mass lesion classification with convolutional neural networks
journal, April 2016