The Imaging and Radiation Oncology Core Houston Quality Assurance Center, The University of Texas MD Anderson Cancer Center, Houston, Texas.
The Imaging and Radiation Oncology Core Houston Quality Assurance Center, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Int J Radiat Oncol Biol Phys. 2017 Nov 1;99(3):750-756. doi: 10.1016/j.ijrobp.2017.06.003. Epub 2017 Jun 13.
To compare analytic and Monte Carlo-based algorithms for proton dose calculations in the lung, benchmarked against anthropomorphic lung phantom measurements.
A heterogeneous anthropomorphic moving lung phantom has been irradiated at numerous proton therapy centers. At 5 centers the treatment plan could be calculated with both an analytic and Monte Carlo algorithm. The doses calculated in the treatment plans were compared with the doses delivered to the phantoms, which were measured using thermoluminescent dosimeters and film. Point doses were compared, as were planar doses using a gamma analysis.
The analytic algorithms overestimated the dose to the center of the target by an average of 7.2%, whereas the Monte Carlo algorithms were within 1.6% of the physical measurements on average. In some regions of the target volume, the analytic algorithm calculations differed from the measurement by up to 31% in the internal gross target volume (iGTV) (46% in the planning target volume), over-predicting the dose. All comparisons showed a region of at least 15% dose discrepancy within the iGTV between the analytic calculation and the measured dose. The Monte Carlo algorithm recalculations showed dramatically improved agreement with the measured doses, showing mean agreement within 4% for all cases and a maximum difference of 12% within the iGTV.
Analytic algorithms often do a poor job predicting proton dose in lung tumors, over-predicting the dose to the target by up to 46%, and should not be used unless extensive validation counters the consistent results of the present study. Monte Carlo algorithms showed dramatically improved agreement with physical measurements and should be implemented to better reflect actual delivered dose distributions.
比较分析算法和基于蒙特卡罗算法的质子剂量计算方法,以人体肺部模体测量为基准。
使用多个质子治疗中心的不均匀异质运动人体肺部模体进行照射。在 5 个中心,治疗计划可以通过分析算法和蒙特卡罗算法进行计算。将治疗计划中计算的剂量与通过热释光剂量计和胶片测量的模体输送剂量进行比较。进行了点剂量比较,以及使用伽马分析进行了平面剂量比较。
分析算法平均高估了靶心剂量 7.2%,而蒙特卡罗算法平均与物理测量值相差 1.6%。在靶区的某些区域,分析算法计算值与测量值相差高达 31%(内部大体靶区(iGTV)为 46%,计划靶区为 46%),过度预测剂量。所有比较均显示在 iGTV 内分析计算值与测量剂量之间存在至少 15%的剂量差异。蒙特卡罗算法重新计算显示出与测量剂量更显著的一致性,所有情况下平均一致性在 4%以内,iGTV 内最大差异为 12%。
分析算法通常不能很好地预测肺部肿瘤中的质子剂量,高估靶区剂量高达 46%,除非广泛验证能抵消本研究的一致结果,否则不应使用。蒙特卡罗算法与物理测量结果显示出显著改善的一致性,应实施以更好地反映实际输送的剂量分布。