Cardiovascular disease (CVD) is a leading cause of death in the lung cancer screening population. Chest CT scans
made in lung cancer screening are suitable for identification of participants at risk of CVD. Existing methods
analyzing CT images from lung cancer screening for prediction of CVD events or mortality use engineered
features extracted from the images combined with patient information. In this work we propose a method
that automatically predicts 5-year cardiovascular mortality directly from chest CT scans without the need for
hand-crafting image features.
A set of 1,583 participants of the National Lung Screening Trial was included (1,188 survivors, 395 nonsurvivors). Low-dose chest CT images acquired at baseline were analyzed and the follow-up time was 5 years.
To limit the analysis to the heart region, the heart was first localized by our previously developed algorithm
for organ localization exploiting convolutional neural networks. Thereafter, a convolutional autoencoder was
used to encode the identified heart region. Finally, based on the extracted encodings subjects were classified
into survivors or non-survivors using a support vector machine classifier. The performance of the method was
assessed in eight cross-validation experiments with 1,433 images used for training, 50 for validation and 100 for
testing. The method achieved a performance with an area under the ROC curve of 0.72.
The results demonstrate that prediction of cardiovascular mortality directly from low-dose screening chest CT
scans, without hand-crafted features, is feasible, allowing identification of subjects at risk of fatal CVD events.
Keywords: Cardiovascular disease, mortality prediction, convolutional autoencoder, lung screening, low-dose
CT, deep learnin