Modeling gamma knife radiosurgical toxicity for multiple brain metastases

Background and Purpose: Radiation oncology protocols for single fraction radiosurgery recommend setting dosing criteria based on assumed risk of radionecrosis, which can be predicted by the 12 Gy normal brain volume (V12). In this study, we show that tumor surface area (SA) and a simple power-law model using only preplan variables can estimate and minimize radiosurgical toxicity. Materials and Methods: A 245-patient cohort with 1217 brain metastases treated with single or distributed Gamma Knife sessions was reviewed retrospectively. Univariate and multivariable linear regression models and power-law models determined which modeling parameters best predicted V12. The V12 power-law model, represented by a product of normalized Rx dose Rx_n, and tumor longest axial dimension LAD (V12 ∼ Rx_n^1.5*LAD²), was independently validated using a secondary 63-patient cohort with 302 brain metastases. Results: Surface area was the best univariate linear predictor of V12 (adjR² = 0.770), followed by longest axial dimension (adjR² = 0.755) and volume (adjR² = 0.745). The power-law model accounted for 90% variance in V12 for 1217 metastatic lesions (adjR² = 0.906) and 245 patients (adjR² = 0.896). The average difference ΔV₁₂ between predicted and measured V12s was (0.28 ± 0.55) cm³ per lesion and (1.0 ± 1.2) cm³ per patient. The power-law predictive capability was validated using a secondary 63-patient dataset (adjR² = 0.867) with 302 brain metastases (adjR² = 0.825). Conclusion: Surface area was the most accurate univariate predictor of V12 for metastatic lesions. We developed a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.