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.

翻译：近期，深度学习模型已展现出预测乳腺癌风险并实现靶向筛查策略的潜力，但现有模型未考虑乳腺随时间的变化。本文提出一种名为PRIME+的乳腺癌风险预测新方法，该方法利用transformer解码器处理先前的乳腺X线影像，其性能优于仅基于单一时点乳腺X线影像的现有最优风险预测方法。我们在包含16,113次检查的数据集上验证了该方法，并进一步证明它能有效捕获先前的乳腺X线影像中的变化模式（如乳腺密度变化），从而提升短期与长期乳腺癌风险预测能力。实验结果表明，与基于现有最优模型的方法相比，本模型在留出测试集上实现了性能的统计显著提升：C指数从0.68升至0.73（p < 0.05）。