Abstract：
BACKGROUND: Both geometric and dosimetric components are commonly considered when determining the margin for planning target volume (PTV). As dose distribution is shaped by controlling beam aperture in peripheral dose prescription and dose-escalated simultaneously integrated boost techniques, adjusting the margin by incorporating the variable dosimetric component into the PTV margin is inappropriate; therefore, geometric components should be accurately estimated for margin calculations.
OBJECTIVE: We introduced an asymmetric margin-calculation theory using the guide to the expression of uncertainty in measurement (GUM) and intra-fractional motion. The margins in fiducial marker-based real-time tumor tracking (RTTT) for lung, liver, and pancreatic cancers were calculated and were then evaluated using Monte Carlo (MC) simulations.
METHODS: A total of 74 705, 73 235, and 164 968 sets of intra- and inter-fractional positional data were analyzed for 48 lung, 48 liver, and 25 pancreatic cancer patients, respectively, in RTTT clinical trials. The 2.5th and 97.5th percentiles of the positional error were considered representative values of each fraction of the disease site. The population-based statistics of the probability distributions of these representative positional errors (PD-RPEs) were calculated in six directions. A margin covering 95% of the population was calculated using the proposed formula. The content rate in which the clinical target volume (CTV) was included in the PTV was calculated through MC simulations using the PD-RPEs.
RESULTS: The margins required for RTTT were at most 6.2, 4.6, and 3.9 mm for lung, liver, and pancreatic cancer, respectively. MC simulations revealed that the median content rates using the proposed margins satisfied 95% for lung and liver cancers and 93% for pancreatic cancer, closer to the expected rates than the margins according to van Herk\'s formula.
CONCLUSIONS: Our proposed formula based on the GUM and motion probability distributions (MPD) accurately calculated the practical margin size for fiducial marker-based RTTT. This was verified through MC simulations.
OBJECTIVE: We introduced an asymmetric margin-calculation theory using the guide to the expression of uncertainty in measurement (GUM) and intra-fractional motion. The margins in fiducial marker-based real-time tumor tracking (RTTT) for lung, liver, and pancreatic cancers were calculated and were then evaluated using Monte Carlo (MC) simulations.
METHODS: A total of 74 705, 73 235, and 164 968 sets of intra- and inter-fractional positional data were analyzed for 48 lung, 48 liver, and 25 pancreatic cancer patients, respectively, in RTTT clinical trials. The 2.5th and 97.5th percentiles of the positional error were considered representative values of each fraction of the disease site. The population-based statistics of the probability distributions of these representative positional errors (PD-RPEs) were calculated in six directions. A margin covering 95% of the population was calculated using the proposed formula. The content rate in which the clinical target volume (CTV) was included in the PTV was calculated through MC simulations using the PD-RPEs.
RESULTS: The margins required for RTTT were at most 6.2, 4.6, and 3.9 mm for lung, liver, and pancreatic cancer, respectively. MC simulations revealed that the median content rates using the proposed margins satisfied 95% for lung and liver cancers and 93% for pancreatic cancer, closer to the expected rates than the margins according to van Herk\'s formula.
CONCLUSIONS: Our proposed formula based on the GUM and motion probability distributions (MPD) accurately calculated the practical margin size for fiducial marker-based RTTT. This was verified through MC simulations.
摘要：
背景：在确定计划目标体积（PTV）的余量时，通常会同时考虑几何和剂量学分量。由于剂量分布是通过控制周围剂量处方和剂量递增同时集成的增强技术中的光束孔径来形成的，通过将可变剂量测定分量并入PTV余量来调整余量是不合适的；因此，几何成分应准确估计余量计算。
目的：我们引入了一种非对称余量计算理论，该理论使用了测量（GUM）和分数内运动不确定度的表达指南。基于基准标记的肺实时肿瘤跟踪（RTTT）的边缘，肝脏,计算和胰腺癌，然后使用蒙特卡洛（MC）模拟进行评估。
方法：共分析了48个肺的74.705、73.235和164.968组分支内和分支间位置数据，48肝，和25名胰腺癌患者，分别,在RTTT临床试验中。位置误差的第2.5百分位数和第97.5百分位数被认为是疾病部位的每个部分的代表值。在六个方向上计算了这些代表性位置误差（PD-RPE）的概率分布的基于总体的统计信息。使用拟议的公式计算了覆盖95%人口的利润率。通过使用PD-RPE的MC模拟来计算PTV中包括临床靶体积(CTV)的含量率。
结果：对于肺，RTTT所需的边缘至多为6.2、4.6和3.9mm，肝脏,和胰腺癌，分别。MC模拟显示，使用拟议边缘的中位含量率满足肺癌和肝癌的95％和胰腺癌的93％，根据范·赫克的公式，比利润率更接近预期利率。
结论：我们提出的基于GUM和运动概率分布（MPD）的公式准确地计算了基于基准标记的RTTT的实际边缘大小。通过MC仿真验证了这一点。
目的：我们引入了一种非对称余量计算理论，该理论使用了测量（GUM）和分数内运动不确定度的表达指南。基于基准标记的肺实时肿瘤跟踪（RTTT）的边缘，肝脏,计算和胰腺癌，然后使用蒙特卡洛（MC）模拟进行评估。
方法：共分析了48个肺的74.705、73.235和164.968组分支内和分支间位置数据，48肝，和25名胰腺癌患者，分别,在RTTT临床试验中。位置误差的第2.5百分位数和第97.5百分位数被认为是疾病部位的每个部分的代表值。在六个方向上计算了这些代表性位置误差（PD-RPE）的概率分布的基于总体的统计信息。使用拟议的公式计算了覆盖95%人口的利润率。通过使用PD-RPE的MC模拟来计算PTV中包括临床靶体积(CTV)的含量率。
结果：对于肺，RTTT所需的边缘至多为6.2、4.6和3.9mm，肝脏,和胰腺癌，分别。MC模拟显示，使用拟议边缘的中位含量率满足肺癌和肝癌的95％和胰腺癌的93％，根据范·赫克的公式，比利润率更接近预期利率。
结论：我们提出的基于GUM和运动概率分布（MPD）的公式准确地计算了基于基准标记的RTTT的实际边缘大小。通过MC仿真验证了这一点。
