Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

  • Mohammad Javad Shafiee
  • Alexander Wong


While skin cancer is the most diagnosed form of cancer in men
and women, with more cases diagnosed each year than all other
cancers combined, sufficiently early diagnosis results in very good
prognosis and as such makes early detection crucial. While radiomics
have shown considerable promise as a powerful diagnostic
tool for significantly improving oncological diagnostic accuracy and
efficiency, current radiomics-driven methods have largely rely on
pre-defined, hand-crafted quantitative features, which can greatly
limit the ability to fully characterize unique cancer phenotype that
distinguish it from healthy tissue. Recently, the notion of discovery
radiomics was introduced, where a large amount of custom, quantitative
radiomic features are directly discovered from the wealth of
readily available medical imaging data. In this study, we present
a novel discovery radiomics framework for skin cancer detection,
where we leverage novel deep multi-column radiomic sequencers
for high-throughput discovery and extraction of a large amount of
custom radiomic features tailored for characterizing unique skin
cancer tissue phenotype. The discovered radiomic sequencer was
tested against 9,152 biopsy-proven clinical images comprising of
different skin cancers such as melanoma and basal cell carcinoma,
and demonstrated sensitivity and specificity of 91% and 75%, respectively,
thus achieving dermatologist-level performance and
hence can be a powerful tool for assisting general practitioners
and dermatologists alike in improving the efficiency, consistency,
and accuracy of skin cancer diagnosis.

How to Cite
Shafiee, M., & Wong, A. (2017). Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection. Journal of Computational Vision and Imaging Systems, 3(1).