Pre-treatment radiomics from radiotherapy dose regions predict distant brain metastases in stereotactic radiosurgery

Stereotactic radiosurgery (SRS) is frequently employed to treat brain metastases. However, <50% of patients treated with this method develop distant brain metastases (DBMs). As a result, these patients are followed using Magnetic Resonance Imaging (MRI) to identify DBM development. There is no current pre-treatment risk metric to identify which patients might be likely to develop DBMs. In this study, pre-treatment MRIs and radiotherapy planning data including structure sets and radiation dose maps were obtained for 81 SRS brain metastases treatment courses. Clinical variables including performance status, age, number of tumors, and primary tumor type were also collected. Pre-treatment MRIs were skull-stripped and normalized. 3D radiomic features from grey-intensity, Laws Energy, Gabor, Haralick, and CoLlAGe feature families were extracted from T1, T1 contrast-enhanced (T1w), T2, and FLAIR pre-treatment MRI sequences in brain regions receiving 0-25%, 25-50%, 50-75%, and 75-100% of prescribed radiation dose. A baseline classification model for DBM was created using clinical variables. Ablation studies were performed to determine which dose region and MRI sequence contained radiomic features most predictive for DBM development using machine learning (ML) classifiers. An ML classifier trained on 3D radiomic features from the 50-75% dose region of pre-treatment T1w MRI (AUC: 0.71, 95% CI: 0.68-0.74) outperformed the baseline model (AUC: 0.50, 95% CI: 0.47-0.53) for DBM prediction. In conclusion, we leverage radiotherapy dose regions to identify subcompartments for radiomic feature extraction from multi-parametric pre-treatment MRI data. We demonstrate that radiomic features from these dose regions can be used to predict DBM for SRS-treated brain metastases.