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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0424-8) contains supplementary material, which is available to authorized users.
Highlights
Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer
Utilizing a computer-assisted method to characterize percent mammographic density (PMD), we have previously reported that mammographic density is not associated with BRCA1/2 mutation status [14], a finding consistent with those from prior studies [12,15,16,17,18]
The associations we have identified between specific Radiographic texture analysis (RTA) features and mutation status are independent from any possible modifying effect of mammographic density, which in both our prior work and that of others appears no different in mutation carriers than that observed in the general population [12,14,15,16,17,18]
Summary
Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. Epidemiologic studies have consistently demonstrated that elevated mammographic density is a strong and independent risk factor for sporadic breast cancer, conferring relative risks of 4- to 5-fold when comparing women with high versus low mammographic density [1]. Mammographic density has a strong heritable component [2,3,4,5,6,7,8,9,10], it is currently being debated as to whether mammographic density is associated with hereditary breast cancer risk [11,12]. Huo et al and Li et al used computerized radiographic texture analysis of a retro-areolar region-of-interest (ROI) to distinguish between mutation carriers and low-risk women; mutation carriers had a breast parenchymal texture pattern that was characterized as being coarse with low contrast [19,20]
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