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

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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.