目的:从可行性角度构建肺癌立体定向放射治疗(SBRT)的肿瘤运动监测模型。
方法:共收集22例患者的32个治疗方案,以其计划CT和计划目标体积(PTV)的质心位置为参考。获取4DCT中不同呼吸阶段的图像以重新定义目标并获得浮动PTV质心位置。根据规划CT和CBCT配准参数,完成了数据扩增,产生2130个实验记录用于分析。我们采用堆叠多学习集成方法来拟合身体表面的3D点云变化和目标位置的变化,以构建肿瘤运动监测模型。并使用均方根误差(RMSE)和R-Square(R2)评估预测精度。
结果:堆叠集合模型的预测位移在每个方向上都与参考值高度吻合。在第一层模型中,X方向(RMSE=0.019~0.145mm,R2=0.9793~0.9996)和Z方向(RMSE=0.051~0.168mm,R2=0.9736~0.9976)显示最佳结果,而Y方向排在后面(RMSE=0.088~0.224毫米,R2=0.9553~0.9933)。第二层模型总结了第一层单元模型的优点,和0.015毫米的RMSE,0.083mm,0.041mm,X的R2分别为0.9998、0.9931、0.9984,Y,获得了Z。
结论:肺癌SBRT的肿瘤运动监测方法具有潜在的应用前景,非侵入性,无标记,和实时。
OBJECTIVE: To construct a tumor motion monitoring model for stereotactic body radiation therapy (SBRT) of lung cancer from a feasibility perspective.
METHODS: A total of 32 treatment plans for 22 patients were collected, whose planning CT and the centroid position of the planning target volume (PTV) were used as the reference. Images of different respiratory phases in 4DCT were acquired to redefine the targets and obtain the floating PTV centroid positions. In accordance with the planning CT and CBCT registration parameters, data augmentation was accomplished, yielding 2130 experimental recordings for analysis. We employed a stacking multi-learning ensemble approach to fit the 3D point cloud variations of body surface and the change of target position to construct the tumor motion monitoring model, and the prediction accuracy was assess using root mean squared error (RMSE) and R-Square (R2).
RESULTS: The prediction displacement of the stacking ensemble model shows a high degree of agreement with the reference value in each direction. In the first layer of model, the X direction (RMSE =0.019 ∼ 0.145mm, R2 =0.9793∼0.9996) and the Z direction (RMSE = 0.051 ∼ 0.168 mm, R2 = 0.9736∼0.9976) show the best results, while the Y direction ranked behind (RMSE = 0.088 ∼ 0.224 mm, R2 = 0.9553∼ 0.9933). The second layer model summarizes the advantages of unit models of first layer, and RMSE of 0.015 mm, 0.083 mm, 0.041 mm, and R2 of 0.9998, 0.9931, 0.9984 respectively for X, Y, Z were obtained.
CONCLUSIONS: The tumor motion monitoring method for SBRT of lung cancer has potential application of non-ionization, non-invasive, markerless, and real-time.