SPARTA: An Integrated Stability, Discriminability, and Sparsity Based Radiomic Feature Selection Approach

In order to ensure that a radiomics-based machine learning model will robustly generalize to new, unseen data (which may harbor significant variations compared to the discovery cohort), radiomic features are often screened for stability via test/retest or cross-site evaluation. However, as stability screening is often conducted independent of the feature selection process, the resulting feature set may not be simultaneously optimized for discriminability, stability, as well as sparsity. In this work, we present a novel radiomic feature selection approach termed SPARse sTable lAsso (SPARTA), uniquely developed to identify a highly discriminative and sparse set of features which are also stable to acquisition or institution variations. The primary contribution of this work is the integration of feature stability as a generalizable regularization term into a least absolute shrinkage and selection operator (LASSO)-based optimization function. Secondly, we utilize a unique non-convex sparse relaxation approach inspired by proximal algorithms to provide a computationally efficient convergence guarantee for our novel algorithm. SPARTA was evaluated on three different multi-institutional imaging cohorts to identify the most relevant radiomic features for distinguishing: (a) healthy from diseased lesions in 147 prostate cancer patients via T2-weighted MRI, (b) healthy subjects from Crohn’s disease patients via 170 CT enterography scans, and (c) responders and non-responders to chemoradiation in 82 rectal cancer patients via T2w MRI. When compared to 3 state-of-the-art feature selection schemes, features selected via SPARTA yielded significantly higher classifier performance on unseen data in multi-institutional validation (hold-out AUCs of 0.91, 0.91, and 0.93 in the 3 cohorts).