  Recently, deep learning models have shown the potential to predict breast
cancer risk and enable targeted screening strategies, but current models do not
consider the change in the breast over time. In this paper, we present a new
method, PRIME+, for breast cancer risk prediction that leverages prior
mammograms using a transformer decoder, outperforming a state-of-the-art risk
prediction method that only uses mammograms from a single time point. We
validate our approach on a dataset with 16,113 exams and further demonstrate
that it effectively captures patterns of changes from prior mammograms, such as
changes in breast density, resulting in improved short-term and long-term
breast cancer risk prediction. Experimental results show that our model
achieves a statistically significant improvement in performance over the
state-of-the-art based model, with a C-index increase from 0.68 to 0.73 (p <
0.05) on held-out test sets.
