Foldyna Borek, Hadzic Ibrahim, Zeleznik Roman, Langenbach Marcel C, Raghu Vineet K, Mayrhofer Thomas, Lu Michael T, Aerts Hugo J W L
Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
Commun Med (Lond). 2024 Mar 13;4(1):44. doi: 10.1038/s43856-024-00475-1.
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.
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).
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.
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.
重度吸烟者患心血管疾病的风险增加，使用常规肺癌筛查胸部计算机断层扫描进行个体化风险量化可能对其有益。我们研究了基于深度学习的自动心外膜脂肪组织量化的预后价值，并将其与既定的心血管危险因素和冠状动脉钙化进行比较。
我们调查了参加国家肺癌筛查试验并随访12.3（11.9 - 12.8）年的重度吸烟者中自动心外膜脂肪组织量化的预后价值。使用经过验证的深度学习算法，在非心电图同步、非增强低剂量胸部计算机断层扫描上对心外膜脂肪组织进行分割和量化。然后利用多变量生存回归分析来确定心外膜脂肪组织体积和密度与全因死亡率和心血管死亡率（心肌梗死和中风）之间的关联。
我们在24,090名成年重度吸烟者（59%为男性；61±5岁）中发现，除了人口统计学、临床危险因素、身体形态、教育水平和冠状动脉钙化评分外，心外膜脂肪组织体积和密度与全因死亡率（调整后的风险比：1.10和1.38；P<0.001）和心血管死亡率（调整后的风险比：1.14和1.78；P<0.001）独立相关。
我们的研究结果表明，从低剂量肺癌筛查图像中自动评估心外膜脂肪组织对重度吸烟者具有预后价值，这对该高危人群的心血管风险分层可能具有潜在意义。
