School of Health Sciences, Purdue University, West Lafayette, Indiana 47907, USA.
Med Phys. 2011 Jul;38(7):4036-44. doi: 10.1118/1.3596527.
The CyberKnife uses an online prediction model to improve radiation delivery when treating lung tumors. This study evaluates the prediction model used by the CyberKnife radiation therapy system in terms of treatment margins about the gross tumor volume (GTV).
From the data log files produced by the CyberKnife synchrony model, the uncertainty in radiation delivery can be calculated. Modeler points indicate the tracked position of the tumor and Predictor points predict the position about 115 ms in the future. The discrepancy between Predictor points and their corresponding Modeler points was analyzed for 100 treatment model data sets from 23 de-identified lung patients. The treatment margins were determined in each anatomic direction to cover an arbitrary volume of the GTV, derived from the Modeler points, when the radiation is targeted at the Predictor points. Each treatment model had about 30 min of motion data, of which about 10 min constituted treatment time; only these 10 min were used in the analysis. The frequencies of margin sizes were analyzed and truncated Gaussian normal functions were fit to each direction's distribution. The standard deviation of each Gaussian distribution was then used to describe the necessary margin expansions in each signed dimension in order to achieve the desired coverage. In this study, 95% modeler point coverage was compared to 99% modeler coverage. Two other error sources were investigated: the correlation error and the targeting error. These were added to the prediction error to give an aggregate error for the CyberKnife during treatment of lung tumors.
Considering the magnitude of 2sigma from the mean of the Gaussian in each signed dimension, the margin expansions needed for 95% modeler point coverage were 1.2 mm in the lateral (LAT) direction and 1.7 mm in the anterior-posterior (AP) direction. For the superior-inferior (SI) direction, the fit was poor; but empirically, the expansions were 3.5 mm. For 99% modeler point coverage, the AP margin was 3.6 mm and the lateral margin was 2.9 mm. The SI margins for 99% modeler point coverage were highly variable. The aggregate error at 95% was 6.9 mm in the SI direction, 4.6 mm in the AP direction, and 3.5 in the lateral direction.
The Predictor points follow the Modeler points closely. Margins were found in each clinical direction that would provide 95% modeler point coverage for 95% of the models reviewed in this study. Similar margins were found in two clinical directions for 99% modeler point coverage in 95% of models. These results can offer guidance in the selection of CTV margins for treatment with the CyberKnife.
CyberKnife 使用在线预测模型来提高治疗肺部肿瘤时的放射剂量传递。本研究评估了 CyberKnife 放射治疗系统在大体肿瘤体积（GTV）治疗边界方面使用的预测模型。
从 CyberKnife 同步模型生成的数据日志文件中，可以计算出放射剂量传递的不确定性。Modeler 点表示肿瘤的跟踪位置，而 Predictor 点则预测未来约 115ms 的位置。分析了来自 23 名匿名肺患者的 100 个治疗模型数据集的 100 个治疗模型数据集的 Predictor 点与相应的 Modeler 点之间的差异。当辐射靶向 Predictor 点时，在每个解剖方向上确定了覆盖 GTV 的任意体积的治疗边界。每个治疗模型都有大约 30 分钟的运动数据，其中大约 10 分钟构成治疗时间；仅使用这些 10 分钟进行分析。分析了边界尺寸的频率，并为每个方向的分布拟合截断高斯正态函数。然后，使用每个高斯分布的标准差来描述在每个有符号维度上实现所需覆盖所需的必要边界扩展。在本研究中，将 95%的 Modeler 点覆盖率与 99%的 Modeler 覆盖率进行了比较。还研究了另外两个误差源：相关误差和靶向误差。这些误差被添加到 CyberKnife 在治疗肺部肿瘤期间的预测误差中，以获得总体误差。
考虑到每个有符号维度高斯平均值的 2sigma 幅度，对于 95%的 Modeler 点覆盖率，侧向（LAT）方向的边界扩展需要 1.2mm，前后（AP）方向需要 1.7mm。对于上下（SI）方向，拟合效果不佳；但是经验上，扩展为 3.5mm。对于 99%的 Modeler 点覆盖率，AP 边界为 3.6mm，侧向边界为 2.9mm。99%的 Modeler 点覆盖率的 SI 边界变化很大。SI 方向的总误差为 6.9mm，AP 方向的总误差为 4.6mm，侧向方向的总误差为 3.5mm。
Predictor 点紧跟 Modeler 点。在本研究回顾的所有模型中，在每个临床方向上都找到了可以为 95%的模型提供 95%的 Modeler 点覆盖率的边界。对于 99%的 Modeler 点覆盖率，在两个临床方向上找到了相似的边界，在 95%的模型中覆盖率为 99%。这些结果可为使用 CyberKnife 进行治疗时 CTV 边界的选择提供指导。
