  Recent studies have highlighted the high correlation between cardiovascular
diseases (CVD) and lung cancer, and both are associated with significant
morbidity and mortality. Low-Dose CT (LCDT) scans have led to significant
improvements in the accuracy of lung cancer diagnosis and thus the reduction of
cancer deaths. However, the high correlation between lung cancer and CVD has
not been well explored for mortality prediction. This paper introduces a
knowledge-based analytical method using deep convolutional neural network (CNN)
for all-cause mortality prediction. The underlying approach combines structural
image features extracted from CNNs, based on LDCT volume in different scale,
and clinical knowledge obtained from quantitative measurements, to
comprehensively predict the mortality risk of lung cancer screening subjects.
The introduced method is referred to here as the Knowledge-based Analysis of
Mortality Prediction Network, or KAMP-Net. It constitutes a collaborative
framework that utilizes both imaging features and anatomical information,
instead of completely relying on automatic feature extraction. Our work
demonstrates the feasibility of incorporating quantitative clinical
measurements to assist CNNs in all-cause mortality prediction from chest LDCT
images. The results of this study confirm that radiologist defined features are
an important complement to CNNs to achieve a more comprehensive feature
extraction. Thus, the proposed KAMP-Net has shown to achieve a superior
performance when compared to other methods. Our code is available at
https://github.com/DIAL-RPI/KAMP-Net.
