Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. The incorporation of genetic information and breast density has been shown to improve predictions for existing models, but detailed image-based features are yet to be included despite correlating with risk. Complex information can be extracted from mammograms using deep-learning algorithms, however, this is a challenging area of research, partly due to the lack of data within the field, and partly due to the computational burden. We propose an attention-based Multiple Instance Learning (MIL) model that can make accurate, short-term risk predictions from mammograms taken prior to the detection of cancer at full resolution. Current screen-detected cancers are mixed in with priors during model development to promote the detection of features associated with risk specifically and features associated with cancer formation, in addition to alleviating data scarcity issues. MAI-risk achieves an AUC of 0.747 [0.711, 0.783] in cancer-free screening mammograms of women who went on to develop a screen-detected or interval cancer between 5 and 55 months, outperforming both IBIS (AUC 0.594 [0.557, 0.633]) and VAS (AUC 0.649 [0.614, 0.683]) alone when accounting for established clinical risk factors.

This study compared mammographic density over time between women who developed breast cancer (cases) and women who did not (controls). Cases had an initial negative mammographic screen and another three years later when cancer was diagnosed. Cases were matched to three controls with two successive negative screens by age, year of mammogram, BMI, parity, menopausal status and HRT use. Mammographic density was measured by VolparaTM. There was a significant reduction in percentage density in the affected breast for cases (5.2 to 4.8 %, p < 0.001) and for the same matched breast in controls (4.9 to 4.5, p < 0.001). Similar results were found for the unaffected breast. After adjusting for density measures at the initial screen, case-control status was only significantly associated with fibroglandular volume in the unaffected breast (adjusted mean 45.8 cm3 in cases, 44.0 cm3 in controls, p = 0.008). The results suggest changes in mammographic density may be less important than initial mammographic density.

This study investigates variations in mammographic density by ethnic group in women attending the NHS breast screening programme in Greater Manchester. Density was estimated using VolparaTM and QuantraTM. Data was analysed for 651 Asian/Asian British, 416 Black/Black British, 394 Jewish origin, 181 ‘Mixed’, 700 ‘Other’ and a random sample of 10,000 women who declared their ethnic origin as White (British or Irish). Age ranged from 46–84 years and mean BMI was 27.4 kg/m2. Fibroglandular volume (VolparaTM) was highest in women of Black/Black British origin (59.4 cm3) and lowest in Asian/Asian British women (47.9 cm3). After adjusting for a number of hormonal and other factors the magnitude of the difference between groups decreased, however, there were still a number of statistical differences between groups. Ethnic differences in mammographic density and personal factors may subsequently contribute to differences in breast cancer incidence.

High overall mammographic density is associated with both an increased risk of developing breast cancer and the risk of cancer being masked. We compared local density at cancer sites in diagnostic images with corresponding previous screening mammograms (priors), and matched controls. Volpara (TM) density maps were obtained for 54 mammograms showing unilateral breast cancer and their priors which had been previously read as normal. These were each matched to 3 controls on age, menopausal status, hormone replacement therapy usage, body mass index and year of prior. Local percent density was computed in 15mm square regions at lesion sites and similar locations in the corresponding images. Conditional logistic regression was used to predict case-control status. In diagnostic and prior images, local density was increased at the lesion site compared with the opposite breast (medians 21.58%, 9.18%, p<0.001 diagnostic; 18.82%, 9.45%, p<0.001 prior). Women in the highest tertile of local density in priors were more likely to develop cancer than those in the lowest tertile (OR 42.09, 95% CI 5.37-329.94). Those in the highest tertile of Volpara (TM) gland volume were also more likely to develop cancer (OR 2.89, 95% CI 1.30-6.42). Local density is increased where cancer will develop compared with corresponding regions in the opposite breast and matched controls, and its measurement could enhance computer-aided mammography.

The detection of breast cancer relies on high-quality images from digital mammography. Optimal levels of compression force are unknown, and UK national guidelines recommend forces of less than 200N. However, large variations in compression forces exist and may be influenced by the mammography practitioner and the breast size and pain threshold of the patient. This study examined the relationship between breast density and compression force. Women attending for routine breast screening and who had a mammogram taken by the same practitioner on the same equipment were included in the study (n=211). Volumetric density measurements were obtained using VolparaTM and details on imaging parameters were obtained from the DICOM headers. There was a strong, positive correlation between compression force and fibroglandular tissue. There was also evidence of a significant positive association between compression force and breast volume which was independent of the volume of fibroglandular tissue present.

Breast cancer incidence has previously been shown to be greater in women of higher socio-economic status (SES), although the picture is complex due to variations in breast cancer risk factors. We have investigated the relationship between one of the strongest risk factors, breast density, with SES in a population of 6398 post- and peri-menopausal women. Volumetric breast density was measured using QuantraTM and VolparaTM, and SES was based on the Index of Multiple Deprivation (IMD) associated with each woman’s postcode. The mean IMD score was 26.39 (SD 16.7). Our results show a weak but significant association between SES and volumetric breast density; women from more deprived areas have slightly less dense breasts. After controlling for age, BMI and HRT use the relationship remained significant for density measured by VolparaTM (gradient -0.01, p <0.005) but not QuantraTM (gradient -0.007, p=0.07).

Mammographic density in digital mammograms can be assessed visually or using automated volumetric methods; the aim in both cases is to identify women at greater risk of developing breast cancer, and those for whom mammography is less sensitive. Ideally all methods should identify the same women as having high density, but this is not the case in practice. 6422 women were ranked from the highest to lowest density by three methods: QuantraTM, VolparaTM and visual assessment recorded on Visual Analogue Scales. For each pair of methods the 20 cases with the greatest agreement in rank were compared with the 20 with the least agreement. The presence of microcalcifications, skin folds, suboptimally positioned inframammary folds, and whether or not the nipple was in profile were found to affect agreement between methods (p<0.05). Careful positioning during mammographic imaging should reduce discrepancy, but a greater understanding of the relationship between methods is also required.