Deep learning models to predict mammographic density jointly on standard dose and low dose images

Objectives Mammographic density is associated with increased risk of developing breast cancer. Automated estimation of density in women below normal screening age would enable earlier risk stratification. We are piloting the use of low dose mammograms combined with models that can make accurate mammographic density estimates. Methods Three models were trained on a joint set (107,619) of standard dose mammograms with associated density scores and their simulated low dose counterparts such that the models made predictions on standard and low dose mammograms. A second set of models was trained separately on the standard and simulated low dose mammograms. All models were tested on a held-out set from the training data and an independent dataset with 294 pairs of standard and real low dose mammograms. Results The root mean squared errors (RMSE) between the model predictions and density scores on standard and simulated low dose images were 8.26 (8.16-8.36) and 8.27 (8.17-8.38) respectively. The RMSE between predictions on standard and simulated low dose images for the jointly trained models was 1.91 (1.88-1.96). The RMSE of the predictions on the real low dose images compared to the standard dose images is 3.79 (2.75-4.99). Conclusions Deep learning models make density predictions on low dose images with similar quality as on standard dose images. Such automated analysis of low dose mammograms could contribute to accurate breast cancer risk estimation in younger women enabling stratification for further monitoring and preventative therapy. Advances in knowledge Mammographic density can be estimated in low dose mammograms with similar quality to standard dose mammograms.