Technical Development of Automated Low Dose Risk Assessment Mammography (ALDRAM) in Women Attending for Annual Mammography Through a Family History Clinic

Breast cancer (BC) is the commonest cause of death in young women. Breast screening in women aged 35-45, at increased risk due to their family history, has been shown to improve survival. However, 80% of women who develop BC do not have a family history. Numerous studies have shown that high mammographic density (MD) is one of the strongest risk factors for BC development. Full field digital mammography (FFDM) can be used to assess MD, however it is not recommended for population BC screening in those <40 years of age due to the concerns about the use of ionising radiation. Safe and accurate high throughput methods to quantify MD in young women are thus required to improve risk prediction and reduce BC mortality. This study aims to develop a low dose mammogram, with quantification of density using artificial intelligence, to facilitate high throughput risk assessment in young women. 600 women aged 30-45, previously identified as being at increased risk of BC and attending for annual mammography at The Nightingale Centre will be recruited. Participants will undergo FFDM of the right breast as usual, however, following acquisition of the craniocaudal (CC) view, the breast will remain compressed and the mammogram dose reduced by 90% to deliver a LD mammogram. This process will be repeated for the right medio-lateral oblique (MLO) view. The left breast FFDM will proceed as normal. It is estimated that each extra exposure will take 1-2 minutes only. Deep machine learning methods will be used to define the relationship between standard FFDM views and their low dose counterparts and determine which view (CC vs MLO) provides the best correlation to be taken forward to the next stage of the research.