Department of Radiation Oncology, University of Pennsylvania, 3400 Civic Central Blvd, Philadelphia, PA, 19104, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
Med Phys. 2018 Dec;45(12):5631-5642. doi: 10.1002/mp.13231. Epub 2018 Oct 26.
Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open-source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites.
First, MCsquare was validated using tissue-mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head-and-neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose-volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3 mm criteria), and estimations of tumor control probability (TCP).
Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head-and-neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%).
Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head-and-neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open-source availability.
蒙特卡罗（MC）剂量计算通常优于商业 TPS 中用于模拟剂量分布的分析剂量计算（ADC），尤其是对于肺部和头颈部等不均匀部位。本研究的目的是提供一种经过验证的、快速的、开源的 MC 代码 MCsquare，以评估 ADC 中的近似值对覆盖各种部位的临床铅笔束扫描（PBS）计划的影响。
首先，使用组织模拟 IROC 肺部体模测量对 MCsquare 进行验证，并与用于患者剂量计算的通用蒙特卡罗 TOPAS 进行基准测试。然后，对总共 50 名患者（包括肝脏、骨盆、大脑、头颈部和肺部）进行了 MCsquare 与 ADC 之间的比较分析，每个部位有 10 名患者。基于剂量-体积直方图（靶 Dmean、D95、均匀性指数、V95）、3D 伽马指数分析（使用 3%/3mm 标准）和肿瘤控制概率（TCP）的估计，评估了 TOPAS、MCsquare 和 ADC 之间的差异。
MCsquare 与 TOPAS 之间的比较显示，所有剂量学指标/TCP 值的差异小于 1.8%，并且目标内体素的 3D 伽马指数通过率超过 99%。当比较 ADC 和 MCsquare 时，发现所有指数的方差随着组织不均匀性程度的增加而增加。在肺部的情况下，ADC 的 D95 值与相应的 MCsquare 统计值相差高达 6.5%。靶体积内体素的中位伽马指数通过率从肝脏的 99.3%降至肺部的 75.8%。对于肺部（≤10.5%）和头颈部（≤6.2%），TCP 差异可能较大，而对于大脑、骨盆和肝脏（≤1.5%），差异较小。
鉴于分析中发现的差异，质子治疗需要使用准确的剂量计算算法，如蒙特卡罗模拟，特别是对于高不均匀性的疾病部位，如头颈部和肺部。MCsquare 的建立可以促进任何机构的患者计划审查，并由于其准确性、速度和开源可用性，有可能在临床试验中提供无偏比较。
