Predictive Value of Pre-Treatment MRI Radiomics for Distant Brain Metastases Following Stereotactic Radiosurgery/Radiotherapy

Local intracranial therapy for brain metastases (BM) has taken on particular importance as survival among metastatic patients improves. However, the development of distant BMs (DBMs) outside the treated area remains a stubborn problem for which canonical clinical features (age, histology, ECOG PS) have limited predictive capability. In this study, we hypothesized that MRI-based "radiomic" features (sub-visual cues extracted from diagnostic images) can accurately predict the time-to-DBM development (TTDD) on a retrospectively curated dataset of patients treated with stereotactic radiosurgery/radiotherapy (SRS/SRT).We queried our treatment planning system for patients treated with brain SRS/SRT between 2014 and 2021, and curated the incidence/timing of DBMs manually. Pre-RT MRI sequences (T1 pre, T1 post, T2, and FLAIR) and planning data were obtained for each patient. MRI and CT simulations were co-registered using affine transformations, and regions of interest (ROIs) were identified based on contoured structures (GTV) and discrete isodose ranges (0-25%, 25-50%, 50-75%, 75%+). Radiomic features were extracted from these ROIs, and clinical features (ECOG PS, tumor volume, age) were recorded for baseline comparison. Features were selected using Wald test scores from univariate Cox proportional hazard (CPH) models. Multivariate CPH models were then trained to predict TTDD using combinations of selected features. Predictive capability was evaluated using concordance index (c-index) values. A radiomic risk score (RRS) was created to discriminate patients with low and high-risk for DBMs, and evaluated using a log-rank test.A total of 105 patients were selected with a median follow up of 356 days. 53 patients developed DBMs (median time 118 days). Radiomic CPH models achieved a c-index of 0.63 compared to clinical baseline of 0.49. The combination of radiomic and clinical features achieved the highest c-index of 0.69. Overall, radiomic features with and without clinical features were able to stratify patients into low and high-risk groups with statistically significant differences in TTDD development (see Table 1). Clinical features alone were not significant. The most predictive radiomic features were identified within the T1 pre-contrast MRI from the 50-75% isodose regions, followed by T2 FLAIR/GTV and T2/GTV combinations.Radiomic features from routine MR scans were more predictive of TTDD than baseline clinical features. The contribution from the 50-75% isodose region suggests importance within the peritumoral environment in addition to the tumor itself.