Application of statistical and computational methodology to predict brainstem dosimetry for trigeminal neuralgia stereotactic radiosurgery.

OBJECTIVES To apply advanced statistical and computational methodology in evaluating the impact of anatomical and technical variables on normal tissue dosimetry of trigeminal neuralgia (TN) stereotactic radiosurgery (SRS). METHODS Forty patients treated with LINAC-based TN SRS with 90 Gy maximum dose were randomly selected for the study. Parameters extracted from the treatment plans for the study included three dosimetric output variables: the maximum dose to the brainstem (BSmax), the volume of brainstem that received at least 10 Gy (V10BS), and the volume of normal brain that received at least 12 Gy (V12). We analyzed five anatomical variables: the incidence angle of the nerve with the brainstem surface (A), the nerve length (L), the nerve width as measured both axially (WA) and sagittally (WS), the distance measured along the nerve between the isocenter and the brainstem surface (D), and one technical variable: the utilized cone size (CS). Univariate correlation was calculated for each pair among all parameters. Multivariate regression models were fitted for the output parameters using the optimal input parameters selected by the Gaussian graphic model LASSO. Repeated twofold cross-validations were used to evaluate the models. RESULTS Median BSmax, V10BS, and V12 for the 40 patients were 35.7 Gy, 0.14 cc, and 1.28 cc, respectively. Median A, L, WA, WS, D, and CS were 43.7°, 8.8 mm, 2.8 mm, 2.7 mm, 4.8 mm, and 6 mm, respectively. Of the three output variables, BSmax most strongly correlated with the input variables. Specifically, it had strong, negative correlations with the input anatomical variables and a positive correlation with CS. The correlation between D and BSmax at -0.51 was the strongest correlation between single input and output parameters, followed by that between CS and V10BS at 0.45, and that between A and BSmax at -0.44. V12 was most correlated with cone size alone, rather than anatomy. LASSO identified an optimal 3-feature combination of A, D, and CS for BSmax and V10BS prediction. Using cross-validation, the multivariate regression models with the three selected features yielded stronger correlations than the correlation between the BSmax and V10BS themselves. CONCLUSIONS For the first time, an advanced statistical and computational methodology was applied to study the impact of anatomical and technical variables on TN SRS. The variables were found to impact brainstem doses, and reasonably strong correlation models were established using an optimized 3-feature combination including the nerve incidence angle, cone size, and isocenter-brainstem distance.