Purpose: To calculate continuous breast density measures from processed images using deep learning. Method: Processed and unprocessed mammograms were collected for 3251 women attending the UK NHS Breast Screening Programme (NHSBSP). The breast density measures investigated included volumetric breast density, fibroglandular volume and breast volume. The ground truth for these measures was calculated using Volpara software on unprocessed mammograms. A deep learning model was trained and validated to predict each breast density measure. The performance of the deep learning model was assessed using a hold-out test set. Results: The breast volume and fibroglandular volume predicted with deep learning were strongly correlated with the ground truth (r=0.96 and r=0.88 respectively). The volumetric breast density had a Pearson correlation coefficient of 0.90. Conclusions: It is possible to predict volumetric breast density from processed images using deep learning.
The purpose of the study is to test the performance of the combination of digital breast tomosynthesis (DBT) and synthetic views on the detection for cancers presenting as calcifications compared to the performance of planar mammography combined with DBT. A pilot study is presented. A set of 22 cases without cancer were collected from a Siemens Inspiration mammography system. Twenty-two simulated calcification clusters were inserted into the planar and DBT projections of 16 cases. For each case one breast and one view were used. The images were processed using Siemens proprietary software. Seven experienced mammography readers viewed the cases in three study arms: planar alone (ArmP), planar with DBT (ArmP&D) and synthetic 2D with DBT (ArmS&D). The observers marked the suspected location of the clusters and classified the likelihood of there being a suspicious calcification clusters for each case. A JAFROC figure of merit (FoM) was calculated for each study arm. The detection fractions of all cases were 46±16% (P and P&D), 34±19% (S&D). For lesion marked for recall then the maximum detection rate was 19%. The FoMs were 0.48±0.15 (P) and 0.42±0.17 (P&D), but significantly lower (p≤0.003) for S&D (0.32±0.16). This pilot study demonstrated the feasibility of undertaking a larger study. The overall detection were lower (<50%) than optimal for a virtual clinical trial. We plan to increase the detection rate by using less subtle clusters in the final study. When using synthetic 2D images instead of planar images alongside DBT, the FoM was lower for subtle calcification clusters.
Purpose: To measure changes in breast density in a screening population. Method: Unprocessed mammograms were collected for 8,268 women (6034 and 2234 women with two and three sequential screening rounds respectively) with normal breasts (routine recall), from the OPTIMAM image database. The volumetric breast density (VBD), fibroglandular volume (FGV) and breast volume (BV) were determined and the changes between screening rounds calculated. Linear regression determined if the rate of change in these breast density measures varied significantly with age at initial screen. The women were split into four quartiles according to VBD in both screening rounds, and any changes in the quartile allocation of each woman determined. The VBD for these women was compared to our previously published data for women with screen detected and interval cancers. Results: Averaged over all women, the percentage change in VBD, FGV and BV, over 6 years was -11.2% (95%CI: - 12.2% to -10.2%), -5.3% (95%CI: -6.1% to -4.5%) and 11.5% (95%CI: 10.4% to 12.6%) respectively. The percentage change per month, of VBD, FGV and BD decreased significantly with age (p<0.0001). The percentage change in FGV was more strongly associated with the FGV at initial screen than age. For the least and most dense quartiles (who would be of interest for risk stratification), the majority of women did not change quartile between screening rounds. VBD was higher for women who developed interval cancers. Conclusions: The average VBD decreased by 11% over six years. The majority (~80%) of women do not change quartile of VBD in six years.