Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.

Published

Journal Article

PURPOSE: 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.

Full Text

Duke Authors

Cited Authors

  • Mazurowski, MA; Zhang, J; Grimm, LJ; Yoon, SC; Silber, JI

Published Date

  • November 2014

Published In

Volume / Issue

  • 273 / 2

Start / End Page

  • 365 - 372

PubMed ID

  • 25028781

Pubmed Central ID

  • 25028781

Electronic International Standard Serial Number (EISSN)

  • 1527-1315

International Standard Serial Number (ISSN)

  • 0033-8419

Digital Object Identifier (DOI)

  • 10.1148/radiol.14132641

Language

  • eng