Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study

• Published in: Breast cancer research : BCR Vol. 16; no. 4; p. 424
• Main Authors: Gierach, Gretchen L, Li, Hui, Loud, Jennifer T, Greene, Mark H, Chow, Catherine K, Lan, Li, Prindiville, Sheila A, Eng-Wong, Jennifer, Soballe, Peter W, Giambartolomei, Claudia, Mai, Phuong L, Galbo, Claudia E, Nichols, Kathryn, Calzone, Kathleen A, Olopade, Olufunmilayo I, Gail, Mitchell H, Giger, Maryellen L
• Format: Journal Article
• Language: English
• Published: England 2014
• Subjects: Adult; Aged; Breast Density; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Datasets as Topic; Female; Genes, BRCA1; Genes, BRCA2; Heterozygote; Humans; Mammary Glands, Human - abnormalities; Mammography; Middle Aged; Mutation; Risk Factors; Sensitivity and Specificity
• ISSN: 1465-542X; 1465-542X
• DOI: 10.1186/PREACCEPT-1744229618121391

Mammographic 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. We 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. In 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. Our 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.