Abstract PO5-07-06: Impact of patient-specific factors on quantitative breast parenchymal texture features
Authors: Nyante Sarah , Kajita Yukie , Mankowski Walter , Killeya Ley , Kontos Despina , Tan Xianming , Cohen Eric , Kuzmiak Cherie 
Published: May 2024

Breast parenchymal texture characterizes the spatial and structural patterns of breast tissue and is an independent predictor of breast cancer risk. Although it has conceptual similarities with another breast composition measure, breast density, parenchymal texture is more complex and is represented by multiple quantitative measures. The extent to which the epidemiology and biological basis of parenchymal texture and breast density differ has not been well described.

In this cross-sectional study, we evaluated quantitative measures of parenchymal texture among 308 postmenopausal women aged ≥ 45 years who participated in a routine breast cancer screening at an academic medical center between 2020 and 2022. After providing informed consent, participants completed a short interview-based questionnaire. Additional demographic and breast cancer risk factor information was obtained from the medical record. Parenchymal texture features were measured from craniocaudal views of full-field digital mammograms. Measured texture values were standardized and averaged across left and right breasts. For this analysis, we evaluated associations between features previously validated as being associated with breast cancer risk (fractal dimension, grey-level mean, co-occurrence entropy and inverse difference moment) and patient personal and clinical characteristics reported at the time of screening. Associations between texture features and participant characteristics were evaluated using Pearson correlations (continuous variables) or the Kruskal-Wallis test (categorical variables). Generalized linear models were used to adjust for technical factors associated with image acquisition, including mammography machine, compression force, and software version. P-values less than 0.05 were considered statistically significant.

Among the 308 participants, 70% were White, 24% were Black, 1% were Asian, 1% were American Indian, and 4% were another race that was not specified. 4% of participants reported Hispanic ethnicity. The mean age was 64.7 years (range 45 – 84 years). Fewer than half of the women were reported by a radiologist to have dense breasts (heterogeneously dense – 24%, extremely dense – 5%, vs. almost entirely fatty – 21%, scattered fibroglandular densities – 50%). The mean body mass index (BMI) was 29.4 kg/m2 (range 19.4 to 53.1 kg/m2) and 77% of the women were parous.

BMI was strongly correlated with fractal dimension (r=0.62, P&lt; 0.01) and grey-level mean (r=-0.48, P&lt; 0.01), and had a weaker correlation with co-occurrence entropy (r=-0.19, P&lt; 0.01). Age was weakly correlated with co-occurrence inverse difference moment (r=-0.12, P=0.03). BI-RADS breast density was inversely associated with fractal dimension, grey-level mean, and co-occurrence entropy (all P&lt; 0.01). Adjustment for technical factors attenuated some associations, but BMI associations with fractal dimension (P&lt; 0.01) and gray-level mean (P&lt; 0.01), and breast density associations with fractal dimension, grey-level mean, and co-occurrence entropy (all P&lt; 0.01) all remained statistically significant. Parity was not associated with any of the features evaluated in unadjusted or adjusted analyses (all P &gt;0.05).

These data suggest that, in postmenopausal women, risk-associated parenchymal texture features are associated with measures affected by body size and adiposity, but not with measures related to age or reproductive status. Longitudinal studies are needed to understand the temporality of these associations. Additionally, future studies will address the joint effects of texture features and the impact of endogenous estrogen levels on the features, which will provide insight into the biological mechanisms that influence texture feature associations with personal characteristics and breast cancer risk.

Citation Format: Sarah Nyante, Yukie Kajita, Walter Mankowski, Ley Killeya, Despina Kontos, Xianming Tan, Eric Cohen, Cherie Kuzmiak. Impact of patient-specific factors on quantitative breast parenchymal texture features [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-06.