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Association between Participant Characteristics and Breast Parenchymal Texture Variation among Postmenopausal Women

Year: 2025

Type: article

Abstract: 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. A total of 344 texture features were extracted from 2D digital screening mammograms. Data dimensionality was reduced using principal component analysis followed by clustering. Associations between texture feature clusters and participant characteristics were evaluated using χ2 or Wilcoxon tests and multinomial logistic regression. P values < 0.05 were considered statistically significant. RESULTS: The 305 participants clustered into three texture feature groups. Group differences were defined by age (P < 0.01), body mass index (BMI, P < 0.01), race (P = 0.02), time since menopause (P < 0.01), breast imaging reporting and data system 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 underinvestigated breast cancer risk mechanisms.

Source: Cancer Epidemiology Biomarkers & Prevention

DOI: 10.1158/1055-9965.epi-25-0674