  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 non-survivors).
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.
