A Dose-Volume Response Model for Brain Metastases Treated With Frameless Single-Fraction Robotic Radiosurgery: Seeking to Better Predict Response to Treatment.

ABSTRACT

Purpose/Objective(s) To establish a dose-volume response relationship for brain metastases treated with single-fraction robotic stereotactic radiosurgery and identify predictors of local control. MATERIALS/METHODS: We reviewed a prospective institutional database of all patients treated for intact brain metastases with stereotactic radiosurgery alone using the CyberKnife robotic radiosurgery system from 2012 to 2015. Tumor response was determined based on Response Evaluation Criteria In Solid Tumors version 1.1. Survival was estimated using the Kaplan-Meier method. Logistic regression modeling was used to identify predictors of outcome and establish a dose-volume response relationship. Receiver operating characteristic curves were constructed to evaluate the predictive capability of the relationship. RESULTS: There were 357 metastases evaluated in 111 patients with a median diameter of 8.14 mm (2.00-40.77 mm). At 6 and 12 months, local control was 86.9% and 82.2%, respectively. For lesions of similar volumes, higher maximum dose, mean dose, and minimum dose (all P values <.05) predicted for better local control. Tumor volume and diameter were strongly correlated, and a dose-volume response relationship was constructed using mean dose per lesion diameter (Gy/mm) that was predictive of local control (odds ratio 1.34, 95% confidence interval 1.06-1.70). Area under the receiver operating characteristic curve for local control and mean dose by volume was 0.6199 with a threshold of 2.05 Gy/mm (local failure 7.6% above and 17.3% below 2.05 Gy/mm). CONCLUSION: A dose-volume response relationship exists for brain metastases treated with robotic stereotactic radiosurgery. Mean dose per volume is strongly predictive of local control and can be potentially useful during stereotactic radiosurgery plan evaluation while respecting previously established dose constraints.