  Objective: Lung cancer is the leading cause of cancer-related death
worldwide. Computer-aided diagnosis (CAD) systems have shown significant
promise in recent years for facilitating the effective detection and
classification of abnormal lung nodules in computed tomography (CT) scans.
While hand-engineered radiomic features have been traditionally used for lung
cancer prediction, there have been significant recent successes achieving
state-of-the-art results in the area of discovery radiomics. Here, radiomic
sequencers comprising of highly discriminative radiomic features are discovered
directly from archival medical data. However, the interpretation of predictions
made using such radiomic sequencers remains a challenge. Method: A novel
end-to-end interpretable discovery radiomics-driven lung cancer prediction
pipeline has been designed, build, and tested. The radiomic sequencer being
discovered possesses a deep architecture comprised of stacked interpretable
sequencing cells (SISC). Results: The SISC architecture is shown to outperform
previous approaches while providing more insight in to its decision making
process. Conclusion: The SISC radiomic sequencer is able to achieve
state-of-the-art results in lung cancer prediction, and also offers prediction
interpretability in the form of critical response maps. Significance: The
critical response maps are useful for not only validating the predictions of
the proposed SISC radiomic sequencer, but also provide improved
radiologist-machine collaboration for effective diagnosis.
