The Predicting Risk of Cancer at Screening (PROCAS) study provided women who were eligible for breast cancer screening in Greater Manchester (United Kingdom) with their 10-year risk of breast cancer, i.e., low (≤1.5%), average (1.5–4.99%), moderate (5.-7.99%) or high (≥8%). The aim of this study is to explore which factors were associated with women’s uptake of screening and prevention recommendations. Additionally, we evaluated women’s organisational preferences regarding tailored screening.
A total of 325 women with a self-reported low (n = 60), average (n = 125), moderate (n = 80), or high (n = 60) risk completed a two-part web-based survey. The first part contained questions about personal characteristics. For the second part women were asked about uptake of early detection and preventive behaviours after breast cancer risk communication. Additional questions were posed to explore preferences regarding the organisation of risk-stratified screening and prevention. We performed exploratory univariable and multivariable regression analyses to assess which factors were associated with uptake of primary and secondary breast cancer preventive behaviours, stratified by breast cancer risk. Organisational preferences are presented using descriptive statistics.
Self-reported breast cancer risk predicted uptake of (a) supplemental screening and breast self-examination, (b) risk-reducing medication and (c) preventive lifestyle behaviours. Further predictors were (a) having a first degree relative with breast cancer, (b) higher age, and (c) higher body mass index (BMI). Women’s organisational preferences for tailored screening emphasised a desire for more intensive screening for women at increased risk by further shortening the screening interval and moving the starting age forward.
Breast cancer risk communication predicts the uptake of key tailored primary and secondary preventive behaviours. Effective communication of breast cancer risk information is essential to optimise the population-wide impact of tailored screening.
High participation in mammographic screening is essential for its effectiveness to detect breast cancers early and thereby, improve breast cancer outcomes. Breast density is a strong predictor of breast cancer risk and significantly reduces the sensitivity of mammography to detect the disease. There are increasing mandates for routine breast density notification within mammographic screening programs. It is unknown if breast density notification impacts the likelihood of women returning to screening when next due (i.e. rescreening rates). This study investigates the association between breast density notification and rescreening rates using individual-level data from BreastScreen Western Australia (WA), a population-based mammographic screening program.
We examined 981,705 screening events from 311,656 women aged 40+ who attended BreastScreen WA between 2008 and 2017. Mixed effect logistic regression was used to investigate the association between rescreening and breast density notification status.
Results were stratified by age (younger, targeted, older) and screening round (first, second, third+). Targeted women screening for the first time were more likely to return to screening if notified as having dense breasts (Percentunadjusted notified vs. not-notified: 57.8% vs. 56.1%; Padjusted = 0.016). Younger women were less likely to rescreen if notified, regardless of screening round (all P < 0.001). There was no association between notification and rescreening in older women (all P > 0.72).
Breast density notification does not deter women in the targeted age range from rescreening but could potentially deter younger women from rescreening. These results suggest that all breast density notification messaging should include information regarding the importance of regular mammographic screening to manage breast cancer risk, particularly for younger women. These results will directly inform BreastScreen programs in Australia as well as other population-based screening providers outside Australia who notify women about breast density or are considering implementing breast density notification.
AbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.