Abstract：Objective To evaluate the feasibility of predicting lung cancer target position by online optical surface motion monitoring. Methods CT images obtained in different ways of stereotactic body radiotherapy (SBRT) plans from 16 lung cancer cases were selected for experimental simulation. The planned CT and the original target position were taken as the reference, and the 10 phases of CT in four dimension CT and each cone beam (CBCT) were taken as the floating objects, on which the floating target location was delineated. The binocular visual surface imaging method was used to obtain point cloud data of reference and floating image body surface, while the point cloud feature information was extracted for comparison. Based on the random forest algorithm, the feature information difference and the corresponding target area position difference were fitted, and an online prediction model of the target area position was constructed. Results The model had a high prediction success rate for the target position. The variance explainded and root mean squared error (RMSE) of left-right, superior-inferior, anterior-posterior directions were 99.76%, 99.25%, 99.58%, and 0.0447 mm, 0.0837 mm, 0.0616 mm, respectively. Conclusion The online monitoring of lung SBRT target position proposed in this study is feasible, which can provide reference for online monitoring and verification of target position and dose evaluation in clinical radiotherapy.
Huang Taiming,Guan Qi,Zhong Jiajian et al. Feasibility study of predicting lung tumor target movement based on body surface motion monitoring[J]. Chinese Journal of Radiation Oncology, 2023, 32(2): 138-144.
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