965The estimated impact of improved breast screening tests targeted at women with dense breasts. (2nd September 2021) - Procopio, Pietro; Velentzis, Louiza; Dennis Petrie, A; Mann, G Bruce; Kavanagh, Anne M; Ray Watson, A; Saxby, Karinna; Korevaar, Elizabeth; Carr, Sarah; Bromley, Hannah; Canfell, Karen; Nickson, Carolyn
Abstract: Background: There is increasing interest in risk-based breast cancer screening, including interventions to improve outcomes for women with mammographically dense breasts. Methods: Policy1-Breast is a continuous-time, multiple-cohort micro-simulation whole-population model which incorporates breast cancer risk, life-course breast density, menopause, hormone therapy use and screening participation. Outcomes include cancer diagnoses and characteristics (invasive/DCIS, tumour size, grade), mode of detection (screen-detected/interval/other) and mortality (breast cancer and other cause). Policy1-Breast validates well against key observed clinical outcomes in Australia. We estimate changes in outcomes within and outside the BreastScreen program upon the introduction of a hypothetical screening test with improved sensitivity for women with dense breasts. Results: We estimate that introducing in year 2020 a screening test for women in the highest quintile of breast density at age 50 that halves the masking effect of their breast density would, by 2026-2030, increase diagnosis rates of population-level invasive cancers ( 10%) and screen-detected cancers (20%) and decrease rates of interval cancers (17%) and community-detected cancers (6%). Conclusions: Targeted screening tests with improved sensitivity for women with dense breasts are expected to markedly reduce interval cancers and other cancers diagnosed outside the BreastScreen program, while increasing all cancer diagnoses due to increased rates of screen-detected cancers. Key messages: Specialised breast cancer screening tests directed at women with very high breast density are expected to reduce interval cancers and increase overall cancer diagnoses. Population simulation models such as Policy1-Breast can complement trial evidence by evaluating a range of scenarios and estimating short and long-term implications.