Mammographic density assessed using deep learning in women at high risk of developing breast cancer: the effect of weight change on density

Abstract

Objectives: High mammographic density (MD) and excess weight are associated with increased risk of breast cancer. Weight loss interventions could reduce risk, but classically defined percentage density measures may not reflect this due to disproportionate loss of breast fat. We investigate an artificial intelligence-based density method, reporting density changes in 46 women enrolled in a weight-loss study in a family history breast cancer clinic, using a volumetric density method as a comparison. Methods: We analysed data from women who had weight recorded and mammograms taken at the start and end of the 12-month weight intervention study. MD was assessed at both time points using a deep learning model, pVAS, trained on expert estimates of percent density, and VolparaTM density software. Results: The Spearman rank correlation between reduction in weight and change in density was 0.17 (-0.13 to 0.43) for pVAS and 0.59 (0.36 to 0.75) for Volpara volumetric percent density. Conclusions: pVAS percent density measurements were not significantly affected by change in weight. Percent density measured with Volpara increased as weight decreased, driven by changes in fat volume. Advances in knowledge: The effect of weight change on pVAS mammographic density predictions has not previously been published.