Abstract
Abstract INTRODUCTION Radiation necrosis (RN) is a long-term complication of Gamma-Knife (GK) Radiosurgery, an established treatment for metastatic brain tumors. Few dosimetric parameters have shown to predict RN, including tumor target volume, treatment dose, and V12 Gy (volume of normal brain receiving 12 Gy). Vascularity has yet to be explored as an independent predictor of RN. OBJECTIVE To evaluate the role of vascular overlap in GK-induced RN. METHODS All metastatic brain tumor patients treated with GK between 2014-2019 were retrospectively screened for post-treatment radiographic RN. Vascular overlap, defined by the MCA-ACA and MCA-PCA border regions, was determined by two blinded neurosurgeons and one radiation oncologist. Multivariate logistic regression analysis was performed to determine factors predictive of RN using variables determined significant by univariate analysis and clinical relevance. Covariates included V12 as a binary variable (≥8 vs. <8 cm3), vascular overlap, supratentorial location, and eloquent location. RESULTS 6% (20/321) of all metastatic brain tumor patients developed RN and were included for analysis, with the final cohort being 60% (12/20) female with a mean age of 59 years, comprising 77 lesions. V12 (≥8 cm3) showed a significant increase in risk of developing RN [OR = 8.18; (coef, 95% CI: 2.10 (0.49, 3.72), p=0.011)]. Conversely, vascular overlap showed a protective effect against RN [OR = 0.23 (-1.49 (-2.79, -0.19), p=0.024)], with a lower rate of RN in areas of vascular overlap (10%) than non-vascular overlap (26%). Neither supratentorial location [OR = 3.31 (1.20 (-0.76, 3.16), p=0.231)] nor location within eloquent brain [OR = 0.96 (-0.04 (-1.48, 1.39), p=0.95)] showed significance. CONCLUSION For the first time, we show that patients with metastatic brain lesions in anatomical regions of vascular overlap treated with GK are less likely to develop RN. Future work should validate these findings with the goal of optimizing radiation dosing to mitigate RN incidence and recurrence rates.