A prediction model of radiation-induced necrosis for intracranial radiosurgery based on target volume
This study aims to extend the observation that the 12 Gy-radiosurgical-volume (V12Gy) correlates with the incidence of radiation necrosis in patients with intracranial tumors treated with radiosurgery by using target volume to predict V12Gy. V12Gy based on the target volume was used to predict the radiation necrosis probability (P) directly. Also investigated was the reduction in radiation necrosis rates (ΔP) as a result of optimizing the prescription isodose lines for linac-based SRS.Methods:
Twenty concentric spherical targets and 22 patients with brain tumors were retrospectively studied. For each case, a standard clinical plan and an optimized plan with prescription isodose lines based on gradient index were created. V12Gy were extracted from both plans to analyze the correlation between V12Gy and target volume. The necrosis probability P as a function of V12Gy was evaluated. To account for variation in prescription, the relation between V12Gy and prescription was also investigated.Results:
A prediction model for radiation-induced necrosis was presented based on the retrospective study. The model directly relates the typical prescribed dose and the target volume to the radionecrosis probability; V12Gy increased linearly with the target volume (R2 > 0.99). The linear correlation was then integrated into a logistic model to predict P directly from the target volume. The change in V12Gy as a function of prescription was modeled using a single parameter, s (=−1.15). Relatively large ΔP was observed for target volumes between 7 and 28 cm3 with the maximum reduction (8–9%) occurring at approximately 18 cm3.Conclusions:
Based on the model results, optimizing the prescription isodose line for target volumes between 7 and 28 cm3 results in a significant reduction in necrosis probability. V12Gy based on the target volume could provide clinicians a predictor of radiation necrosis at the contouring stage thus facilitating treatment decisions.