Pre-treatment Spatially-Aware MRI Radiomics Can Predict Distant Brain Metastases (DBMs) Following Stereotactic Radiosurgery/Radiation Therapy (SRS/SRT)

Purpose Stereotactic radiosurgery/radiotherapy (SRS/SRT) has increasingly been used to treat brain metastases. However, the development of distant brain metastases (DBMs) in the untreated brain remains a serious complication. We sought to develop a spatially aware radiomic signature to model the time-to-DBM development in a cohort of patients leveraging pre-treatment magnetic resonance imaging (MRI) and radiotherapy treatment planning data including radiation dose distribution maps. Methods and Materials We retrospectively analyzed a cohort of 105 patients with brain metastases treated by SRS/SRT with pre-treatment multi-parametric MRI (T1, T1 post-contrast, T2, FLAIR). 3D radiomic features were extracted from each MRI sequence within 5 isodose regions of interest (ROIs) identified via radiation dose distribution maps and gross target volume (GTV) contours. Clinical features including patient performance status, number of lesions treated, tumor volume, and tumor stage were collected to serve as a baseline for comparison. Cox proportional hazards (CPH) modelling and Kaplan Meier analysis were used to model time-to-DBM development. Results CPH models trained using radiomic features achieved a mean concordance index (c-index) of 0.63 (std=0.08) compared to a c-index of 0.49 (std=0.09) for CPH models trained using clinical factors. A CPH model trained using both radiomic and clinical features achieved a c-index of 0.69 (std=0.08). The identified radiomic signature was able to stratify patients into distinct risk groups with statistically significant differences (p=0.00007) in time-to-DBM development as measured by log-rank test. Clinical features were unable to do the same. Radiomic features from the peritumoral 50-75% isodose ROI and GTV region were most predictive of DBM development. Conclusions Our results suggest that radiomic features extracted from pre-treatment MRI and multiple isodose ROIs can model time-to-DBM development in patients receiving SRS/SRT for brain metastases, outperforming clinical feature baselines. Notably, we believe we are the first to leverage SRS/SRT dose maps for ROI identification and subsequent radiomic analysis of peritumoral and untargeted brain regions using multi-parametric MRI. We observed that the peritumoral environment may be implicated in DBM development for SRS/SRT-treated brain metastases. Our preliminary results might enable the identification of patients with predisposition to DBM development and prompt subsequent changes in disease management.