A radiomics-based machine learning pipeline to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest; dataset

Please read the README file for more details. `featurespace_metadata.json` includes radiomic features extracted from 1273 spinal lesions (healthy or metastatic) from radiotherapy planning-ct images using single point-based geometrical regions of interest (ROIs). `output` is a folder containing the results of our radiomic-based machine learning pipeline in differentiating between healthy bone (HB) and bone metastases (BM) lesions. The pipeline was trained and tested using several resampling techniques (RS), feature selection methods (FS), and machine learning classifiers (ML) on single-point-based geometric ROIs with various shapes and sizes.