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Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms
Author(s) -
Grimm Lars J.,
Zhang Jing,
Mazurowski Maciej A.
Publication year - 2015
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24879
Subject(s) - radiogenomics , breast cancer , medicine , breast imaging , estrogen receptor , cancer , pathology , oncology , mammography , radiology , radiomics
Purpose To identify associations between semiautomatically extracted MRI features and breast cancer molecular subtypes. Methods We analyzed routine clinical pre‐operative breast MRIs from 275 breast cancer patients at a single institution in this retrospective, Institutional Review Board‐approved study. Six fellowship‐trained breast imagers reviewed the MRIs and annotated the cancers. Computer vision algorithms were then used to extract 56 imaging features from the cancers including morphologic, texture, and dynamic features. Surrogate markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor‐2 [HER2]) were used to categorize tumors by molecular subtype: ER/PR+, HER2− (luminal A); ER/PR+, HER2+ (luminal B); ER/PR−, HER2+ (HER2); ER/PR/HER2− (basal). A multivariate analysis was used to determine associations between the imaging features and molecular subtype. Results The imaging features were associated with both luminal A ( P = 0.0007) and luminal B ( P = 0.0063) molecular subtypes. No association was found for either HER2 ( P = 0.2465) or basal ( P = 0.1014) molecular subtype and the imaging features. A P ‐value of 0.0125 (0.05/4) was considered significant. Conclusion Luminal A and luminal B molecular subtype breast cancer are associated with semiautomatically extracted features from routine contrast enhanced breast MRI. J. Magn. Reson. Imaging 2015;42:902–907.