  Lung cancer is the leading cause for cancer related deaths. As such, there is
an urgent need for a streamlined process that can allow radiologists to provide
diagnosis with greater efficiency and accuracy. A powerful tool to do this is
radiomics: a high-dimension imaging feature set. In this study, we take the
idea of radiomics one step further by introducing the concept of discovery
radiomics for lung cancer prediction using CT imaging data. In this study, we
realize these custom radiomic sequencers as deep convolutional sequencers using
a deep convolutional neural network learning architecture. To illustrate the
prognostic power and effectiveness of the radiomic sequences produced by the
discovered sequencer, we perform cancer prediction between malignant and benign
lesions from 97 patients using the pathologically-proven diagnostic data from
the LIDC-IDRI dataset. Using the clinically provided pathologically-proven data
as ground truth, the proposed framework provided an average accuracy of 77.52%
via 10-fold cross-validation with a sensitivity of 79.06% and specificity of
76.11%, surpassing the state-of-the art method.
