Opportunistic Atherosclerotic Cardiovascular Disease Risk Estimation at Abdominal CTs with Robust and Unbiased Deep Learning

项目摘要 动脉粥样硬化性心血管疾病（ASCVD）是全球发病率和死亡率的主要原因， 影响全国1800多万成年人。然而，80%的ASCVD死亡可以通过及时干预预防。 在早期筛查ASCVD风险后-这是准确的亚临床 ASCVD诊断因此，在这项研究中，我们评估了基于深度学习（DL）的对预先存在的 腹部计算机断层扫描（CT）与电子病历（EMR）相结合可提高预测能力 心血管死亡、心肌梗死和卒中的风险。我们 将在一个大的、多样化的、真实世界的人群中进行这项研究，并进行外部验证，以确定是否 我们可以改进具有许多缺点的临床使用的合并群组方程（PCE）。 美国每年进行2000多万次腹部CT扫描。虽然这些扫描可以回答特定的临床 问题，与心脏代谢风险相关的组织表型相关的定量信息， 未评估。DL算法可用于量化脂肪组织、肌肉、骨骼 肝脏和血管钙化，这些都可以用于改善PCE，以确定心血管 事件在我们的提案的目标1中，我们将构建具有内置质量控制的自动分割算法 在125，000多名不同受试者中提取这些身体成分指标的机制，以确定人群水平 组织大小和放射密度的标准值。在目标2中，我们将用这些身体来增加PCE协变量 组成值和其他EMR特征，用于使用高级DL模型预测ASCVD风险。此外，委员会认为， 我们将设计新的算法方法，通过确保类似的模型性能来改善健康公平性 在PCE资格、种族/民族、保险类型和CT扫描仪品牌/型号的患者亚组中。在aim中 3.我们将建立一个新的ASCVD风险估计器，直接使用3D成像数据。我们将增强这种端到端的 通过整合利用成像数据和EMR数据的多模态模型来预测方法。实现 需要提高深度学习解决方案的可解释性，我们将建立每个主题的数字孪生模型来描述原因 正在进行模型预测，以及患者可以做出哪些改变来降低ASCVD风险。 我们将在来自斯坦福大学（25 k名患者）的数据上训练所有模型，在来自斯坦福大学（8 k名患者）的数据上进行测试， 根据来自3家马约诊所临床试验机构（20 k+患者）的数据对模型进行外部验证，以评估可推广性 我们的工具。我们已经组建了一个由DL专家、心脏病专家和腹部外科医生组成的跨学科MPI团队。 放射科医生建立这样的ASCVD风险模型。我们开发创新的工具，以提高准确性，普遍性， 偏倚和基于DL的ASCVD风险模型的可解释性。我们的长期目标是使沉默的早期检测 动脉粥样硬化和触发干预，可能最终防止超过800，000死亡，心肌梗死， 和中风事件。

PROJECT SUMMARY Atherosclerotic cardiovascular disease (ASCVD) is the main cause of morbidity and mortality worldwide, and affects 18+ million adults nationally. However, 80% of ASCVD deaths may be prevented with prompt intervention following early screening for ASCVD risk – a powerful rationale for the unmet need of accurate subclinical ASCVD diagnoses. Thus, in this study we assess whether a deep learning (DL)-based analysis of pre-existing abdominal computed tomography (CT) scans paired with electronic medical records (EMR) improves prediction of cardiovascular death, myocardial infarction, and stroke in a large multi-site primary prevention population. We will conduct this study in a large, diverse, real-world population with an external validation to ascertain whether we can improve upon the clinically-utilized pooled cohort equations (PCE) that have numerous shortcomings. 20+ million abdominal CT scans performed annually in the US. While these scans answer specific clinical questions, quantitative information related to tissue phenotypes associated with cardiometabolic risk is simply not evaluated. DL algorithms can be used to quantify body composition metrics for adipose tissue, muscle, bone, liver, and vascular calcifications, which can all be used to improve upon the PCE for determining cardiovascular events. In aim 1 of our proposal, we will build automated segmentation algorithms with a built-in quality control mechanism to extract these body composition metrics in 125,000+ diverse subjects to ascertain population-level normative values of tissue size and radiodensity. In aim 2, we will augment the PCE covariates with these body composition values and additional EMR features for predicting ASCVD risk with advanced DL models. Moreover, we will devise new algorithmic approaches for improving health equity by ensuring similar model performance across patient sub-groups of PCE eligibility, race/ethnicity, insurance type, and CT scanner make/model. In aim 3, we will build a new ASCVD risk estimator that directly uses 3D imaging data. We will augment this end-to-end prediction approach by integrating multi-modal models that leverage both imaging data and EMR data. Realizing the need for improved explainability of DL solutions, we will build digital twins of each subject to describe why model predictions are being made and what changes a patient could make to lower ASCVD risk. We will train all models on data from Stanford (25k patients), test on data from Stanford (8k patients), and externally validate the models on data from three Mayo Clinic sites (20k+ patients) to assess the generalizability of our tools. We have assembled an inter-disciplinary MPI team of DL experts, cardiologists, and abdominal radiologists to build such ASCVD risk models. We develop innovative tools to improve accuracy, generalizability, bias, and explainability of DL-based ASCVD risk models. Our long-term goal is to enable early detection of silent atherosclerosis and trigger interventions that may ultimately prevent over 800,000 death, myocardial infarction, and stroke events in diverse Americans annually.