title: Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rm, 7-E108, Bethesda 20892-9774, MD, USA. gierachg@mail.nih.gov.
authors.forename: Gretchen L
authors.initials: GL
authors.lastname: Gierach
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.forename: Jennifer T
authors.initials: JT
authors.lastname: Loud
authors.forename: Mark H
authors.initials: MH
authors.lastname: Greene
authors.forename: Catherine K
authors.initials: CK
authors.lastname: Chow
authors.forename: Li
authors.initials: L
authors.lastname: Lan
authors.forename: Sheila A
authors.initials: SA
authors.lastname: Prindiville
authors.forename: Jennifer
authors.initials: J
authors.lastname: Eng-Wong
authors.forename: Peter W
authors.initials: PW
authors.lastname: Soballe
authors.forename: Claudia
authors.initials: C
authors.lastname: Giambartolomei
authors.forename: Phuong L
authors.initials: PL
authors.lastname: Mai
authors.forename: Claudia E
authors.initials: CE
authors.lastname: Galbo
authors.forename: Kathryn
authors.initials: K
authors.lastname: Nichols
authors.forename: Kathleen A
authors.initials: KA
authors.lastname: Calzone
authors.forename: Olufunmilayo I
authors.initials: OI
authors.lastname: Olopade
authors.forename: Mitchell H
authors.initials: MH
authors.lastname: Gail
authors.forename: Maryellen L
authors.initials: ML
authors.lastname: Giger
mesh_terms: D000328:Adult; D000368:Aged; D000071060:Breast Density; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology*; D066264:Datasets as Topic; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D006579:Heterozygote; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008327:Mammography; D008875:Middle Aged; D009154:Mutation; D012307:Risk Factors; D012680:Sensitivity and Specificity