Interpretable Deep Learning Models for Analysis of Longitudinal 3D Mammography Screenings

项目摘要 乳腺癌的乳房X线筛查有明确的，实质性的好处，包括显着减少乳房 癌症死亡率和改善早期发现癌症的治疗选择。然而，常规的乳房X光检查 筛查使妇女面临几种潜在的危害，包括高假阳性率，超过60%的妇女 在10年的年度筛查后出现假阳性结果;假阴性率高， 在致密乳房中，由于肿瘤外观模糊，cers被遗漏;高回忆率，导致过度焦虑 和不必要的，潜在的侵入性检查的妇女与假阳性屏幕。新的3D乳腺X射线摄影 一种名为数字乳腺断层合成摄影（DBT）的技术已经显示出癌症检测的增加和复发的减少。 呼叫率，但放射科医生需要更长的解释时间，可能缺乏经验。临床工作流程可以 可以通过计算机辅助检测系统来增强。然而，当前的方法仅关注单个 乳房X光检查，忽略了放射科医生会考虑的关键决策信息，例如先前的 乳房X光照片、患者人口统计学和个人病史。相反，已建立的乳腺癌风险模型 仅依靠患者的人口统计学和个人/家族史，不包括乳房X线摄影史。朝向 为了实现减少乳房X光检查的危害和增加其益处的总体目标，我们建议： 提高乳腺癌检测准确性并通过连续3D乳房X线照片预测未来癌症发展 使用一种新型的深度学习模型进行筛查，该模型结合了空间、时间和非成像临床 信息.我们的方法采用基于注意力的神经网络，即，变形金刚，学习复杂的依赖- 在一个序列中的不同元素之间的关系，并自动注意到最相关的信息。在 除了提高性能的潜力之外，注意力机制还提供了内置的模型解释 为了更好地理解对模型预测重要的输入， 模型和促进乳腺癌检测和发展的乳房X线摄影生物标志物的提取。我们 具体目标是：1）开发一个强大的深度学习模型，用于同时利用空间，时间， DBT检查的非成像临床信息; 2）创建一种新的工具来检测乳腺癌， gastrointestinal DBT筛查;和3）开发一种新的模型，用于预测乳腺癌的发展， 纵向DBT研究和提取与癌症发展相关的3D乳腺摄影生物标志物。是- 除了改善乳腺癌检测和风险评估的直接好处外，这项工作还可以减少放射科医生的工作量。 阅读时间和工作量，告知新的个性化筛查方案，进一步了解我们的作用 乳腺结构在癌症风险中的作用，并指导预防性治疗的开发和监测。最后， 所开发的深度学习方法将广泛适用于其他医学领域的时空分析。 条件和成像域。

Project Summary Mammography screening for breast cancer has clear, substantial benefits, including significantly reduced breast cancer mortality and improved treatment options for early detected cancers. However, regular mammography screenings subject women to several potential harms, including high false positive rates, with over 60% of women experiencing a false positive finding after 10 years of annual screening; high false negative rates, with more can- cers missed in dense breasts which obscure tumor appearance; and high recall rates, causing undue anxiety and unnecessary, potentially invasive workup for women with a false positive screen. New 3D mammography technology called digital breast tomosynthesis (DBT) has shown increased cancer detection and decreased re- call rates, but radiologists require longer interpretation time and may lack experience. The clinical workflow could potentially be enhanced with computer aided detection systems. However, current methods only focus on a single mammogram exam, ignoring crucial decision-making information that a radiologist would consider, such as prior mammograms, patient demographics, and personal history. Conversely, established breast cancer risk models rely only on patient demographics and personal/family history, excluding mammographic history. Toward the overarching goal of reducing the harms and increasing the benefits of mammography screening, we propose to increase accuracy of breast cancer detection and predict future cancer development from serial 3D mammogram screenings using a novel deep learning model that jointly incorporates spatial, temporal, and non-imaging clinical information. Our method adopts attention-based neural networks, i.e., Transformers, which learn complex depen- dencies between different elements in a sequence and automatically attend to the most relevant information. In addition to the potential for improved performance, the attention mechanism provides built-in model interpretation to better understand the inputs that are important for the model’s predictions, instilling user confidence in the model and facilitating extraction of mammographic biomarkers for breast cancer detection and development. Our specific aims are to: 1) develop a powerful deep learning model for simultaneously leveraging spatial, temporal, and non-imaging clinical information from DBT exams; 2) create a new tool to detect breast cancer from lon- gitudinal DBT screenings; and 3) develop a new model for predicting development of breast cancer based on longitudinal DBT studies and extract 3D mammographic biomarkers associated with cancer development. Be- yond the direct benefit of improved breast cancer detection and risk estimation, this work could reduce radiologist reading time and workload, inform new individualized screening protocols, further our understanding of the role of breast architecture in cancer risk, and guide development and monitoring of preventive treatments. Finally, the developed deep learning methodology will have wide applicability to spatiotemporal analysis in other medical conditions and imaging domains.