Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
This work addresses breast cancer risk prediction for medical screening by providing an incremental improvement through the use of prior images.
The paper tackles the problem of breast cancer risk prediction by incorporating prior mammograms to capture changes over time, resulting in a model that improves the C-index from 0.68 to 0.73 compared to a state-of-the-art single-time-point method.
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.