Assessing mammographic density change within individuals across screening rounds using deep learning-based software.

Abstract

Purpose: The purposes are to evaluate the change in mammographic density within individuals across screening rounds using automatic density software, to evaluate whether a change in breast density is associated with a future breast cancer diagnosis, and to provide insight into breast density evolution.

Approach: Mammographic breast density was analyzed in women screened in Malmö, Sweden, between 2010 and 2015 who had undergone at least two consecutive screening rounds $<30$ months apart. The volumetric and area-based densities were measured with deep learning-based software and fully automated software, respectively. The change in volumetric breast density percentage (VBD%) between two consecutive screening examinations was determined. Multiple linear regression was used to investigate the association between VBD% change in percentage points and future breast cancer, as well as the initial VBD%, adjusting for age group and the time between examinations. Examinations with potential positioning issues were removed in a sensitivity analysis.

Results: In 26,056 included women, the mean VBD% decreased from 10.7% [95% confidence interval (CI) 10.6 to 10.8] to 10.3% (95% CI: 10.2 to 10.3) ( $p<0.001$ ) between the two examinations. The decline in VBD% was more pronounced in women with initially denser breasts (adjusted $\beta =-0.10$ , $p<0.001$ ) and less pronounced in women with a future breast cancer diagnosis (adjusted $\beta =0.16$ , $p=0.02$ ).

Conclusions: The demonstrated density changes over time support the potential of using breast density change in risk assessment tools and provide insights for future risk-based screening.