  Radiomics has proven to be a powerful prognostic tool for cancer detection,
and has previously been applied in lung, breast, prostate, and head-and-neck
cancer studies with great success. However, these radiomics-driven methods rely
on pre-defined, hand-crafted radiomic feature sets that can limit their ability
to characterize unique cancer traits. In this study, we introduce a novel
discovery radiomics framework where we directly discover custom radiomic
features from the wealth of available medical imaging data. In particular, we
leverage novel StochasticNet radiomic sequencers for extracting custom radiomic
features tailored for characterizing unique cancer tissue phenotype. Using
StochasticNet radiomic sequencers discovered using a wealth of lung CT data, we
perform binary classification on 42,340 lung lesions obtained from the CT scans
of 93 patients in the LIDC-IDRI dataset. Preliminary results show significant
improvement over previous state-of-the-art methods, indicating the potential of
the proposed discovery radiomics framework for improving cancer screening and
diagnosis.
