Special Section on Radiomics and Imaging Genomics

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

[+] Author Affiliations
Wentian Guo

University of Chicago, Department of Public Health Sciences, 5841 South Maryland Avenue MC2000, Chicago, Illinois 60637, United States

Fudan University, School of Public Health, 130 Dongan Road, Shanghai 200032, China

Hui Li, Li Lan, Karen Drukker, Maryellen L. Giger

University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States

Yitan Zhu, Shengjie Yang

NorthShore University Health System, Program of Computational Genomics & Medicine, 1001 University Place, Evanston, Illinois 60201, United States

Elizabeth Morris

Memorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, New York 10065, United States

Elizabeth Burnside

University of Wisconsin, School of Medicine and Public Health, Department of Radiology, E3/366 Clinical Science Center, 600 Highland Avenue, Madison, Wisconsin 53792-3252, United States

Gary Whitman

MD Anderson, 1515 Holcombe Boulevard, Houston, Texas 77030, United States

Yuan Ji

University of Chicago, Department of Public Health Sciences, 5841 South Maryland Avenue MC2000, Chicago, Illinois 60637, United States

NorthShore University Health System, Program of Computational Genomics & Medicine, 1001 University Place, Evanston, Illinois 60201, United States

TCGA Breast Phenotype Research Group

https://wiki.cancerimagingarchive.net/display/Public/TCGA+Breast+Phenotype+Research+Group

J. Med. Imag. 2(4), 041007 (Sep 23, 2015). doi:10.1117/1.JMI.2.4.041007
History: Received April 13, 2015; Accepted July 22, 2015
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Abstract.  Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.

Figures in this Article
© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Wentian Guo ; Hui Li ; Yitan Zhu ; Li Lan ; Shengjie Yang, et al.
"Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data", J. Med. Imag. 2(4), 041007 (Sep 23, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.4.041007


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