Radiomics to Predict Local Progression of Non-Spine Bone Metastases Following Stereotactic Radiotherapy
Abstract
Patients diagnosed with non-spine bone metastases benefit from stereotactic body radiotherapy (SBRT), however, 5-20% of patients still develop local progression. We aim to create a machine learning model integrating clinical, radiotherapy dose, and radiomic features to predict which patients will experience local progression following SBRT. We analyzed 179 treated metastatic lesions in 130 patients. Radiomic features were extracted from radiotherapy target volumes. Lesions were separated by patient into an approximate 70/30 training (n = 119) and testing (n = 60) split. Feature selection was performed, and machine learning models were created using dose, clinical, and radiomics features. The clinical model achieved a testing AUC of 0.61 [95% CI: 0.47 – 0.74] and the combined model had a testing AUC of 0.77 [95% CI: 0.64 – 0.89]. This preliminary study demonstrates the potential utility for a radiomics-based machine learning model for the prediction of local progression in non-spine bone metastases following SBRT.