Relationships between computer-extracted mammographic texture pattern features and BRCA1-2mutation status: a cross-sectional studyReport as inadecuate




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Breast Cancer Research

, 16:424

First Online: 23 August 2014Received: 13 February 2014Accepted: 31 July 2014DOI: 10.1186-s13058-014-0424-8

Cite this article as: Gierach, G.L., Li, H., Loud, J.T. et al. Breast Cancer Res 2014 16: 424. doi:10.1186-s13058-014-0424-8

Abstract

IntroductionMammographic density is similar among women at risk of either sporadic or BRCA1-2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1-2 mutation carriers and non-carriers.

MethodsWe compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1-2 mutations n = 137 versus non-carriers n = 100. Subjects were stratified into training 107 carriers, 70 non-carriers and testing 30 carriers, 30 non-carriers datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject’s digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis RTA classifier model aimed at distinguishing BRCA1-2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network BANN algorithm, which produced a probability score rating the likelihood of each subject’s belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1-2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model’s discriminatory capacity.

ResultsIn the testing dataset, a one standard deviation SD increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1-2 mutation status: unadjusted odds ratio OR = 2.00, 95% confidence interval CI: 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1-2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density.

ConclusionsOur findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1-2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography.

AbbreviationsAUCarea under curve

BANNBayesian artificial neural network

CDFcumulative distribution function

COOCco-occurrence

FFDMfull-field digital mammogram

ICCintraclass correlation coefficient

IRBInstitutional Review Board

NCINational Cancer Institute

NIHNational Institutes of Health

NNMCNational Naval Medical Center

ORodds ratio

PATPedigree Assessment Tool

PMDpercent mammographic density

ROCreceiver operating characteristic

ROIregion-of-interest

RTAradiographic texture analysis

Electronic supplementary materialThe online version of this article doi:10.1186-s13058-014-0424-8 contains supplementary material, which is available to authorized users.

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Author: Gretchen L Gierach - Hui Li - Jennifer T Loud - Mark H Greene - Catherine K Chow - Li Lan - Sheila A Prindiville - Jen

Source: https://link.springer.com/







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