Discovery Radiomics via StochasticNet Sequencers for Cancer Detection
This work addresses the problem of limited feature characterization in radiomics for cancer detection, potentially improving screening and diagnosis, though it appears incremental as it builds on existing radiomics approaches.
The study tackled the limitation of pre-defined radiomic features in cancer detection by introducing a discovery radiomics framework that directly extracts custom features from medical imaging data, achieving significant improvement over previous state-of-the-art methods in binary classification of 42,340 lung lesions.
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.