  Lung cancer is the leading cause of cancer death worldwide. The critical
reason for the deaths is delayed diagnosis and poor prognosis. With the
accelerated development of deep learning techniques, it has been successfully
applied extensively in many real-world applications, including health sectors
such as medical image interpretation and disease diagnosis. By combining more
modalities that being engaged in the processing of information, multimodal
learning can extract better features and improve predictive ability. The
conventional methods for lung cancer survival analysis normally utilize
clinical data and only provide a statistical probability. To improve the
survival prediction accuracy and help prognostic decision-making in clinical
practice for medical experts, we for the first time propose a multimodal deep
learning method for non-small cell lung cancer (NSCLC) survival analysis, named
DeepMMSA. This method leverages CT images in combination with clinical data,
enabling the abundant information hold within medical images to be associate
with lung cancer survival information. We validate our method on the data of
422 NSCLC patients from The Cancer Imaging Archive (TCIA). Experimental results
support our hypothesis that there is an underlying relationship between
prognostic information and radiomic images. Besides, quantitative results
showing that the established multimodal model can be applied to traditional
method and has the potential to break bottleneck of existing methods and
increase the the percentage of concordant pairs(right predicted pairs) in
overall population by 4%.
