abstract. while lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early
diagnosis can be pivotal in patient survival rates. imaging-based, or radiomics-driven, detection methods have been
developed to aid diagnosticians, but largely rely on hand-crafted features which may not fully encapsulate the differences between cancerous and healthy tissue. recently, the concept of discovery radiomics was introduced, where
custom abstract features are discovered from readily available imaging data. we propose a novel evolutionary deep
radiomic sequencer discovery approach based on evolutionary deep intelligence. motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach
organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet
similarly descriptive radiomic sequences over multiple generations. as a result, this framework improves operational
efficiency and enables diagnosis to be run locally at the radiologist’s computer while maintaining detection accuracy.
we evaluated the evolved deep radiomic sequencer (edrs) discovered via the proposed evolutionary deep radiomic
sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung ct data with pathologically-proven diagnostic data from the lidc-idri dataset. the evolved deep radiomic
sequencer shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to
previous radiomics approaches.
keywords: discovery radiomics, radiomic sequencing, lung cancer, evolutionary deep intelligence, evolved
deep radiomic sequencer.
* mohammad javad shafiee, mjshafiee@uwaterloo.ca