Abstract PD12-06: PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer

Abstract

Abstract Mammographic breast density is a well-established and strong risk factor for breast cancer. Widespread use of digital mammography has opened new potential for assessment of density changes over time. The underlying premise is that changes in breast tissue due to evolving structures that support cancer development should translate to quantifiable differences between the two breasts over time. To address this hypothesis, we draw on extensive digital mammography data and bring repeated measures over up to 10 years to evaluate the association between change in mammographic breast density and risk of breast cancer. Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St Louis. Baseline questionnaire risk factors and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry and death register. Routine screening mammograms are collected every 1 to 2 years. Follow-up of cohort participants through December 2020 was: 78% seen in 2019 or 2020; a further 4.4% seen most recently in 2018 and a further 2.4% in 2017 giving over 80% active follow-up for women seen within the last 36 months. The median number of mammograms is 5 (min = 1, max = 10; sd = 2.43). The average person-years of follow-up through most recent contact is 9.2 person-years. We have excluded women who are diagnosed within the first 6 month of baseline mammogram date in all analyses, leaving 259 cases and 695 controls with a total number of 8,966 crandiocaudal (CC) view mammograms for analysis. For these 954 women, the mean number of years between mammograms is 1.3 (10th percentile: 1.0, 90th percentile: 2.0). For the cases, the mean number of years from last mammogram date to diagnosis date was 2.0 years (10th percentile: 1.0, 90th percentile: 3.9) after excluding mammograms that are within 6 months of diagnosis. The percentage volumetric density (MD) within each digital CC-view mammogram is estimated with an automated pixel-thresholding algorithm ADAPT implemented within the Division of Public Health at WashU. The skin around the breast is automatically removed using a boundary detection algorithm prior to estimating the dense areas. The percent density (MD) is then estimated using the dense area divided by the total breast area which normalizes the difference in breast size across women. The correlation between average MD generated from our automated algorithm with Volpara is 0.82. Initial analyses use linear mixed effects with average density between breasts (fitted with R package lmer). We performed test of assumptions for all linear mixed effects models, including the normality of residuals, linearity, and homogeneity of residual variance. Assessing the averaging density between 2 breasts controlling for age, BMI, histology confirmed benign breast disease, family history, parity, and alcohol, MD decreases significantly over follow-up (time in years) (P < 0.01). At baseline, postmenopausal women had lower density than premenopausal women after controlling for the same set of risk factors. The average MD over all time points was significantly different for the case vs. control women (P < 0.01). For overweight women, the trajectory of MD over time was significantly different for the case vs. control women (P < 0.01). Drawing on over 10 years of follow-up we observe, for the first time, a dynamic effect for breast density such that divergence in density over time is related to risk for breast cancer. Citation Format: Graham A. Colditz, Shu Jiang. PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD12-06.