Association between participant characteristics and breast parenchymal texture variation among postmenopausal women

Background: Mammographic parenchymal texture is a predictor of breast cancer risk, but knowledge of the clinical or epidemiologic factors that contribute to texture variability is limited. We evaluated the relationship between texture and breast cancer risk factors among women participating in mammography. Methods: Postmenopausal women were invited to participate between October 2020 and July 2022. A total of 305 women provided informed consent. Participant characteristics were obtained from medical records and a questionnaire. 344 texture features were extracted from 2-D digital screening mammograms. Data dimensionality was reduced using principal components analysis followed by clustering. Associations between texture feature clusters and participant characteristics were evaluated using chi-square or Wilcoxon tests and multinomial logistic regression. P-values <0.05 were statistically significant. Results: The 305 participants clustered into three texture feature groups. Group differences were defined by age (P<0.01), BMI (P<0.01), race (P=0.02), time since menopause (P<0.01), BI-RADS breast density (P<0.01), percent breast density (P<0.01), dense breast area (P=0.02), and breast thickness (P<0.01). Ethnicity, menopause type, parity, age at first birth, and family history of breast cancer did not differ between groups (all P>0.05). In multivariable analysis, participant characteristics explained 32% of texture feature variability. Conclusions: Our findings indicate that texture features in postmenopausal women reflect, in part, factors that change over time (e.g., BMI, breast area and thickness) and are less influenced by independent reproductive events (e.g., parity, age at first birth). Impact: The low proportion of variability explained suggests that texture patterns are related to unknown or under-investigated breast cancer risk mechanisms.