通过时间有序建模提升乳腺癌风险预测精度
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
- 基于纵向影像构建时序有序注意力模型，捕捉乳腺组织变化
- 在公开与自建数据集上均优于现有方法，时间预测准确率更高
- 可视化热图揭示模型关注点，适合临床医生理解决策过程
精准的乳腺癌（BC）风险评估对个性化筛查和预防至关重要。尽管近期基于乳腺钼靶（MG）的深度学习模型在预测风险方面展现出潜力，但多数忽略了患者间未来事件发生时间的时序顺序，且对乳腺组织变化的历史追踪能力有限，限制了其临床应用。本文提出一种新方法OA-BreaCR，能够精确建模未来事件发生的时间顺序及其间隔关系，并以更具可解释性的方式融合纵向乳腺组织变化。我们在公开的EMBED数据集和内部数据集上验证该方法，与现有乳腺癌风险预测及时间预测方法进行对比。结果表明，OA-BreaCR在乳腺癌风险预测和未来事件时间预测任务中均表现更优。此外，时序热图可视化展示了模型随时间的关注区域。研究强调了可解释且精确的风险评估对提升乳腺癌筛查与预防的重要性。代码将对外公开。
原文摘要 · Abstract (English)
Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention. Despite the promising potential of recent mammogram (MG) based deep learning models in predicting BC risk, they mostly overlook the 'time-to-future-event' ordering among patients and exhibit limited explorations into how they track history changes in breast tissue, thereby limiting their clinical application. In this work, we propose a novel method, named OA-BreaCR, to precisely model the ordinal relationship of the time to and between BC events while incorporating longitudinal breast tissue changes in a more explainable manner. We validate our method on public EMBED and inhouse datasets, comparing with existing BC risk prediction and time prediction methods. Our ordinal learning method OA-BreaCR outperforms existing methods in both BC risk and time-to-future-event prediction tasks. Additionally, ordinal heatmap visualizations show the model's attention over time. Our findings underscore the importance of interpretable and precise risk assessment for enhancing BC screening and prevention efforts. The code will be accessible to the public.
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