2014
DOI: 10.1148/radiol.14121031
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Computerized Image Analysis for Identifying Triple-Negative Breast Cancers and Differentiating Them from Other Molecular Subtypes of Breast Cancer on Dynamic Contrast-enhanced MR Images: A Feasibility Study

Abstract: Purpose:To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images. Materials andMethods:This is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board-approved, HI… Show more

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Cited by 130 publications
(132 citation statements)
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References 38 publications
(48 reference statements)
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“…Our study builds upon previous radiogenomic work (1421) and adds to the accumulating evidence that imaging may potentially provide useful supplementary information to molecular analysis in breast cancer, especially in situations where the latter is inconclusive because of insufficient tumor sampling in a small needle biopsy.…”
Section: Discussionmentioning
confidence: 56%
See 1 more Smart Citation
“…Our study builds upon previous radiogenomic work (1421) and adds to the accumulating evidence that imaging may potentially provide useful supplementary information to molecular analysis in breast cancer, especially in situations where the latter is inconclusive because of insufficient tumor sampling in a small needle biopsy.…”
Section: Discussionmentioning
confidence: 56%
“…Previous studies have investigated the relationship between MR imaging and genomic features (813) or molecular subtypes (1421) of breast cancer. However, most studies use a single measurement platform (GEP or IHC) for molecular subtypes and lack independent validation.…”
Section: Introductionmentioning
confidence: 99%
“…This increases objectivity, thereby rendering spatially and temporally dynamic noninvasive image phenotyping more akin to molecular profiling tools. Furthermore, we used a relatively focused, yet diverse image feature library that included statistical, morphologic, and textural features as well as spatiotemporally based image features (31)(32)(33). The statistical and textural measures were found to be uninformative, but the spatiotemporaltype trait, ERF, was both strongly associated with MFS and biologically concordant with known lncRNA regulators of aggressive tumors.…”
Section: Discussionmentioning
confidence: 99%
“…Recently, several groups started investigating the potential of computer-extracted features to improve the diagnosis of cancer at MR imaging, an application that was successful for both breast and prostate MR imaging (10,11). The general concept uses image analysis algorithms to extract subvisual image features that are not readily apparent to the human visual system.…”
Section: Patientsmentioning
confidence: 99%