Prediction of Radiation Therapy Dose for Lung Cancer IMRT Technique using Support Vector Regression Model

Abstract

Optimal dose distribution in the treatment planning system (TPS) is crucial before being applied to radiotherapy patients. However, TPS still uses optimization methods that are time-consuming and user-dependent. This study aimed to evaluate the automatic dose prediction model, support vector regression (SVR), and compare it with the clinically planned dose of lung cancer patients. Sixty patients treated with intensity-modulated radiation therapy (IMRT) were used as the objects in this study. The target dose distribution was evaluated based on the conformity index (CI), and dose homogeneity was evaluated with the homogeneity index (HI). In contrast, the mean and maximum doses were used to evaluate organs at risk (right lung, left lung, heart, and spinal cord). Statistical analysis was performed using the Wilcoxon test. A value of <0.05 indicates a significant difference between the two datasets. The mean CI of the SVR and clinical are 1.154±0.003 and 1.181±0.136. The mean HI for SVR and clinical was 0.075±0.016 and 0.083±0.030. the Wilcoxon test showed no statistically significant difference between the two results. The maximum cardiac dose showed a statistically significant difference (p=0.042), while the mean dose and maximum dose of other OARs did not show a statistically significant difference. The study showed no significant difference between the two strategies, except for the maximum heart dose. The model provides information about dose distribution that can be applied clinically to determine the best technique to use in patients.