To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features. Materials and Methods Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial). Results There is an association (P = .0015) between the  luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype. Conclusion The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma. (c) RSNA, 2014 Online supplemental material is available for this article.

Data Access

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Citations & Data Usage Policy 

Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, and Silber JI. (2014). Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. The Cancer Imaging Archive.DOI: 10.7937/K9/TCIA.2014.IIRMBUNX

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. (2013) The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6 pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

Mazurowski, M. A., Zhang, J., Grimm, L. J., Yoon, S. C., & Silber, J. I. (2014, November). Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. Radiology. Radiological Society of North America (RSNA). DOI: 10.1148/radiol.14132641

Other Publications Using This Data

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Version 1 (Current): 2014/11/12

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Image Data (DICOM)