Journal Article

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers

Aerts, Hugo J. W. L., Lu, Michael T., Raghu, Vineet K., Foldyna, Borek, Hadzic, Ibrahim, Mayrhofer, Thomas, Zeleznik, Roman, Langenbach, Marcel C.

2024

Overview

Background Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium. Methods We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9–12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke). Results Here we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P  < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P  < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score. Conclusions Our findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population. Plain Language Summary Heavy smokers are at increased risk of poor health outcomes, particularly outcomes related to cardiovascular disease. We explore how fat surrounding the heart, known as epicardial adipose tissue, may be an indicator of the health of heavy smokers. We use an artificial intelligence system to measure the heart fat on chest scans of heavy smokers taken during a lung cancer screening trial and following their health for 12 years. We find that higher amounts and denser epicardial adipose tissue are linked to an increased risk of death from any cause, specifically from heart-related issues, even when considering other health factors. This suggests that measuring epicardial adipose tissue during lung cancer screenings could be a valuable tool for identifying heavy smokers at greater risk of heart problems and death, possibly helping to guide their medical management and improve their cardiovascular health. Foldyna et al. demonstrate that deep learning-based quantification of epicardial adipose tissue from low-dose chest CT scans independently predicts all-cause and cardiovascular mortality in heavy smokers. This can enhance cardiovascular risk stratification beyond traditional measures.

• Publisher: Nature Publishing Group UK,Springer Nature B.V,Nature Portfolio
• Subject: 692/308/174 / 692/308/53/2422 / 692/53/2422 / Algorithms / Automation / Body fat / Cardiovascular disease / Coronary vessels / Deep learning / Electrocardiography / Habitus / Heart / Localization / Lung cancer / Medical imaging / Medical screening / Medicine / Medicine & Public Health / Mortality / Pericardium / Tomography