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Screening for Heart Disease by Coronary Calcium

Principal Investigator
Name
Ryan Chamberlain
Degrees
PhD
Institution
Imbio, LLC
Position Title
Senior Machine Learning Scientist
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-473
Initial CDAS Request Approval
Jan 29, 2019
Title
Screening for Heart Disease by Coronary Calcium
Summary
With the growing number high-risk patients undergoing CT-based lung screening due to the published results of the National Lung Screening Trial (NLST) there is an opportunity to screen the same patients for atherosclerosis using the images already being acquired. This project aims to implement a fully automated software tool to quantify the extent of coronary artery calcium in lung screening CT scans. By using a fully automated quantification tool, CAC screening could take place with minimal burden to the Radiologist and give reliable and repeatable metrics.

This project is a partnership between the Los Angeles Biomedical Research Institute and Imbio LLC. Imbio’s commercial lung analysis software, which just received FDA clearance, is being expanded to include a tool to quantify coronary artery calcium. It is this component of the software that we will use to analyze the NLST data to determine the potential for CAC screening using automated quantification. We will compare the results of the Imbio results to manual core lab CT results derived from Matthew Budoff’s lab at Los Angeles Biomedical Research Institute.

Once the CAC quantification is completed in the subjects by the core lab, the amount of CAC will be correlated with Imbio results to determine if the Imbio software can be used to predict the CAC status of the patient with a low dose lung scan and modify the risk prediction for future ASCVD development. Other information such as smoking history and demographic information will be included in the statistical model in order to give a more reliable prediction. If successful, this tool could be used by a primary care physician to determine if a high-risk patient should be referred to a cardiologist for further tests.

By screening undiagnosed ASCVD patients, we hope to improve overall health outcomes and lower costs by reducing the number of undiagnosed cardiac patients at risk for ASCVD events.
Aims

Aim 1: Perform CT densitometry on the NLST screening CT exams. Imbio’s automated lung segmentation and densitometry algorithm with be run on all of the screening CT exams, giving detailed information about the distribution of HU values throughout the lung
Aim 2: Create a statistical model that combines CT densitometry and patient demographic and medical history in order to predict GOLD status and cancer development.
Aim 3: Perform lung nodule texture characterization on NLST screening CT exams. NLST identified lung nodules will be segmented and analyzed with Imbio’s nodule texture characterization method.
Aim 4: Create a statistical model that combines the model developed in Aim 2 with the nodule texture analysis information to create a model predicting the stage and outcome of the patient presenting with a lung nodule.

Collaborators

Matthew Budoff, M.D., Los Angeles Biomedical Research Institute