The World Health Organization lists cardiovascular disease as the most common cause of death worldwide at 31% of all deaths. Coronary artery calcium (CAC) scoring has become the most reliable predictor of future coronary artery disease (CAD) events in the asymptomatic population. CAC scores are currently only reported on ECG-gated CT examinations that are ordered when the primary indication is cardiac in nature. However, in 2007 there were 7.1 million non-cardiac thoracic CT scans ordered with lungs as the primary indication where CAC scores could have been reported. An additional 7-10 million CT lung screening scans are expected to be ordered per year as lung screening programs grow. Because lung cancer risk factors and CAD risk factors overlap significantly, most these patients could benefit greatly from having their CAC score reported as part of their non-cardiac CT examination. This could dramatically improve decision making, quality of care, and lower expenses due to future CAD interventions and cardiac specific CT scans. The goal of this project is to develop a fully automated software application that can report CAC scores on both gated and ungated thoracic CT scans with seamless integration into the Radiology workflow. A successful completion of this project will deliver a product capable of dramatically increasing the number of people that receive early intervention for CAD, thereby increasing quality of care and lowering costs. This Phase I application proposes first creating an algorithm capable of automatically calculating global CAC scores from both ECG-gated and ungated CT scans using CAC segmentations performed by an expert physician. If successful, the Phase II project will perform robust clinical validation adequate for an FDA 510(k) submission.
Public Health Relevance Statement: NARRATIVE The goal of this project is to develop a fully automated software application that can report CAC scores on both gated and ungated thoracic CT scans with seamless integration into the Radiology workflow. A successful completion of this project will deliver a product capable of dramatically increasing the number of people that receive early intervention for CAD, thereby increasing quality of care and lowering costs.
Project Terms: Agatston Score; Algorithms; base; Bone Density; Calcium; Cardiac; Cardiovascular Diseases; cardiovascular risk factor; Categories; Cause of Death; Cessation of life; Chest; Chronic Obstructive Airway Disease; Clinical; clinical effect; clinically relevant; Compliance behavior; Computer software; Coronary Arteriosclerosis; Coronary artery; coronary artery calcification; coronary artery calcium; coronary calcium scoring; Correlation Studies; cost; Data; Data Set; Decision Making; deep learning; Detection; Development; Diagnostic; Dose; Early Intervention; Electrocardiogram; Event; Future; Goals; Human; Image; imaging biomarker; improved; innovation; Interstitial Lung Diseases; Intervention; Label; learning strategy; Lesion; Literature; Lung; Lung CAT Scan; Lung nodule; Malignant neoplasm of lung; Manuals; Measures; Modeling; Multi-Ethnic Study of Atherosclerosis; multitask; Nature; Patients; Performance; Phase; Physicians; Population; Predictive Value; Preventive measure; Quality of Care; quantitative imaging; Radiology Specialty; Reporting; Risk Factors; Scanning; screening; screening program; smoking cessation; success; Techniques; Training; Validation; Vision; World Health Organization; X-Ray Computed Tomography