Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA.
J Appl Clin Med Phys. 2023 Aug;24(8):e13990. doi: 10.1002/acm2.13990. Epub 2023 Apr 8.
Quality assurance measurement of IMRT/VMAT treatment plans is resource intensive, and other more efficient methods to achieve the same confidence are desirable.
We aimed to analyze treatment plans in the context of the treatment planning systems that created them, in order to predict which ones will fail a standard quality assurance measurement. To do so, we sought to create a tool external to the treatment planning system that could analyze a set of MLC positions and provide information that could be used to calculate various evaluation metrics.
The tool was created in Python to read in DICOM plan files and determine the beam fluence fraction incident on each of seven different zones, each classified based on the RayStation MLC model. The fractions, termed grid point fractions, were validated by analyzing simple test plans. The average grid point fractions, over all control points for 46 plans were then computed. These values were then compared with gamma analysis pass percentages and median dose differences to determine if any significant correlations existed.
Significant correlation was found between the grid point fraction metrics and median dose differences, but not with gamma analysis pass percentages. Correlations were positive or negative, suggesting differing model parameter value sensitivities, as well as potential insight into the treatment planning system dose model.
By decomposing MLC control points into different transmission zones, it is possible to create a metric that predicts whether the analyzed plan will pass a quality assurance measurement from a dose calculation accuracy standpoint. The tool and metrics developed in this work have potential applications in comparing clinical beam models or identifying their weak points. Implementing the tool within a treatment planning system would also provide more potential plan optimization parameters.
调强放疗/容积旋转调强治疗计划的质量保证测量需要大量资源，因此需要寻求更有效的方法来达到相同的置信度。
我们旨在根据创建它们的治疗计划系统来分析治疗计划，以预测哪些计划将无法通过标准质量保证测量。为此，我们试图创建一个独立于治疗计划系统的工具，该工具可以分析一组多叶准直器位置，并提供可用于计算各种评估指标的信息。
该工具是用 Python 编写的，可以读取 DICOM 计划文件，并确定每个七个不同区域的射束通量分数，每个区域都根据 RayStation MLC 模型进行分类。这些分数称为网格点分数，通过分析简单的测试计划进行验证。然后计算 46 个计划中所有控制点的平均网格点分数。然后将这些值与伽马分析通过率和中位数剂量差异进行比较，以确定是否存在任何显著相关性。
发现网格点分数指标与中位数剂量差异之间存在显著相关性，但与伽马分析通过率无关。相关性为正或负，表明模型参数值的敏感性不同，同时也为治疗计划系统剂量模型提供了潜在的见解。
通过将多叶准直器控制点分解为不同的透射区域，可以创建一个从剂量计算准确性角度预测所分析计划是否通过质量保证测量的指标。本研究中开发的工具和指标具有比较临床射束模型或识别其弱点的潜在应用。在治疗计划系统中实现该工具还可以提供更多潜在的计划优化参数。
