Lung cancer overall survival analysis using computed
tomography (CT) images plays an important role in treatment
planning. Most current analysis methods involve handcrafted
image features for survival time prediction. However,
hand-crafted features require domain knowledge and may lack
specificity to lung cancer. Advanced self-learning models such
as deep learning have showed superior performance in many
medical image tasks, but they require large amount of data
which is difficult to collect for survival analysis because of
the long follow-up time. Although data with survival time is
difficult to acquire, it is relatively easy to collect lung cancer
patients without survival time. In this paper, we proposed an
unsupervised deep learning method to take advantage of the
unlabeled data for survival analysis, and demonstrated better
performance than using hand-crafted features. We proposed a
residual convolutional auto encoder and trained the model using
images from 274 patients without survival time. Afterwards, we
extracted deep learning features through the encoder model,
and constructed a Cox proportional hazards model on 129
patients with survival time. The experiment results showed
that our unsupervised deep learning feature gained better
performance (C-Index = 0.70) than using hand-crafted features
(C-Index = 0.62). Furthermore, we divided the patients into
two groups according to their Cox hazard value. Kaplan-Meier
analysis indicated that our model can divide patients into high
and low risk groups and the survival time of these two groups
had significant difference (p < 0.01).