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Proceedings Paper

New method for predicting estrogen receptor status utilizing breast MRI texture kinetic analysis
Author(s): Baishali Chaudhury; Lawrence O. Hall; Dmitry B. Goldgof; Robert A. Gatenby; Robert Gillies; Jennifer S. Drukteinis
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Paper Abstract

Magnetic Resonance Imaging (MRI) of breast cancer typically shows that tumors are heterogeneous with spatial variations in blood flow and cell density. Here, we examine the potential link between clinical tumor imaging and the underlying evolutionary dynamics behind heterogeneity in the cellular expression of estrogen receptors (ER) in breast cancer. We assume, in an evolutionary environment, that ER expression will only occur in the presence of significant concentrations of estrogen, which is delivered via the blood stream. Thus, we hypothesize, the expression of ER in breast cancer cells will correlate with blood flow on gadolinium enhanced breast MRI. To test this hypothesis, we performed quantitative analysis of blood flow on dynamic contrast enhanced MRI (DCE-MRI) and correlated it with the ER status of the tumor. Here we present our analytic methods, which utilize a novel algorithm to analyze 20 volumetric DCE-MRI breast cancer tumors. The algorithm generates post initial enhancement (PIE) maps from DCE-MRI and then performs texture features extraction from the PIE map, feature selection, and finally classification of tumors into ER positive and ER negative status. The combined gray level co-occurrence matrices, gray level run length matrices and local binary pattern histogram features allow quantification of breast tumor heterogeneity. The algorithm predicted ER expression with an accuracy of 85% using a Naive Bayes classifier in leave-one-out cross-validation. Hence, we conclude that our data supports the hypothesis that imaging characteristics can, through application of evolutionary principles, provide insights into the cellular and molecular properties of cancer cells.

Paper Details

Date Published: 18 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351V (18 March 2014); doi: 10.1117/12.2043188
Show Author Affiliations
Baishali Chaudhury, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)
Robert A. Gatenby, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Jennifer S. Drukteinis, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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