  Known for its high morbidity and mortality rates, lung cancer poses a
significant threat to human health and well-being. However, the same population
is also at high risk for other deadly diseases, such as cardiovascular disease.
Since Low-Dose CT (LDCT) has been shown to significantly improve the lung
cancer diagnosis accuracy, it will be very useful for clinical practice to
predict the all-cause mortality for lung cancer patients to take corresponding
actions. In this paper, we propose a deep learning based method, which takes
both chest LDCT image patches and coronary artery calcification risk scores as
input, for direct prediction of mortality risk of lung cancer subjects. The
proposed method is called Hybrid Risk Network (HyRiskNet) for mortality risk
prediction, which is an end-to-end framework utilizing hybrid imaging features,
instead of completely relying on automatic feature extraction. Our work
demonstrates the feasibility of using deep learning techniques for all-cause
lung cancer mortality prediction from chest LDCT images. The experimental
results show that the proposed HyRiskNet can achieve superior performance
compared with the neural networks with only image input and with other
traditional semi-automatic scoring methods. The study also indicates that
radiologist defined features can well complement convolutional neural networks
for more comprehensive feature extraction.
