QRadAR: A Toolbox for Quantitative Magnetic Resonance Radiomics Analysis and Reliability

Motivation: Radiomic feature robustness as an input to a downstream model is an important consideration for model reliability. Generating a subset of robust, reproducible, and repeatable features is an important step in determining predictive or indicative features. Goal(s): To provide a framework for radiomics robustness, repeatability, and reproducibility. Approach: A Python implementation using the pyradiomics, scikit-learn, numpy, and other open-source library is provided as tool to quickly summarize the radiomic features surviving differing sampling, time point, or field strength acquisitions. Results: The QRadAR Toolbox provides researchers and clinicians with a collection of reliable features for downstream model input. Impact: The QRadAR Toolbox provides researchers and clinicians with a collection of reliable features for downstream model input by providing a providing Python a framework for radiomics robustness, repeatability, and reproducibility.