基于机器人多叶准直器的计划：计划复杂性研究。
Robotic MLC-based plans: A study of plan complexity.
机构信息
Department of Medical Physics, Radiation Oncology IFCA, Florence, 50139, Italy.
Department of Medical Physics, Hospital Universitari Sant Joan de Reus, IISPV, Tarragona, 43204, Spain.
出版信息
Med Phys. 2021 Mar;48(3):942-952. doi: 10.1002/mp.14667. Epub 2021 Jan 21.
PURPOSE
The utility of complexity metrics has been assessed for IMRT and VMAT treatment plans, but this analysis has never been performed for CyberKnife (CK) plans. The purpose of this study is to perform a complexity analysis of CK MLC plans, adapting and computing complexity indices previously defined for IMRT plans. Metrics were used to compare the complexity of plans created by two optimization systems and to study correlations between plan complexity and patient-specific quality assurance (PSQA) results. Relationships between pairs of metrics were also analyzed to get insight into possible interdependencies.
METHODS
Two independent in-house software platforms were developed to compute six complexity metrics: modulation complexity score (MCS), edge metric (EM), plan irregularity (PI), plan modulation (PM), leaf gap (LG), and small aperture score (SAS10). MCS and PM definitions were adapted to account for CK plans characteristics. The computed metrics were used to compare the existing optimization algorithms (sequential and VOLO) in terms of plan complexity over 24 selected cases. Metrics were then computed over a large number (103) of VOLO SBRT clinical plans from different treatment sites, mainly liver, prostate, pancreas, and spine. Pearson's r was used to study relationships between each pair of metrics. Correlation between complexity indices and PSQA results expressed as gamma index passing rates (GPR) at (3%, 1 mm) and (2%, 1 mm) was finally analyzed. Correlation was regarded as weak for absolute Pearson's r values in the range 0.2-0.39, moderate 0.4-0.59, strong 0.6-0.79, and very strong 0.8-1.
RESULTS
When compared to VOLO, sequential plans exhibited a higher complexity degree, showing lower MCS and LG values and higher EM, PM and PI values. Differences were significant for 5/6 metrics (Wilcoxon P < 0.05). The analysis of VOLO clinical plans highlighted different degrees of complexity among plans from different treatment sites, increasing from liver to prostate, pancreas, and finally, spine. Analysis of dependencies between pairs of metrics showed a very strong significant negative correlation (P < 0.01), respectively, between MCS and PM (r = -0.97), and EM and LG (-0.82). Most of the remaining pairs showed moderate to strong correlations with the exception of PI, which showed weaker correlations with the other metrics. A moderate significant correlation was observed with GPR values both at (3%, 1 mm) and (2%, 1 mm) for all metrics except PI, which showed no correlation.
CONCLUSIONS
Modulation complexity metrics were computed for CK MLC-based plans for the first time and some metrics' definitions were adapted to CK plans peculiarities. The computed metrics proved a useful tool for comparing optimization algorithms and for characterizing CK clinical plans. Strong and very strong correlations were found between some pairs of metrics. Some significant correlations were found with PSQA GPR, indicating that some indices are promising for rationalizing and reducing PSQA workload. Our results set the basis for evaluating new optimization algorithms and TPS versions in the future, as well as for comparing the complexity of CK MLC-based plans in multicenter and multiplatform comparisons.
目的
已经评估了复杂度指标在调强放疗（IMRT）和容积旋转调强放疗（VMAT）治疗计划中的效用，但从未对射波刀（CyberKnife，CK）计划进行过这种分析。本研究的目的是对 CK 多叶准直器（MLC）计划进行复杂度分析，适当地计算之前为 IMRT 计划定义的复杂度指数。使用这些指标来比较由两种优化系统创建的计划的复杂度，并研究计划复杂度与患者特定质量保证（PSQA）结果之间的相关性。还分析了指标对之间的关系，以深入了解可能的相互依赖关系。
方法
开发了两个独立的内部软件平台来计算六个复杂度指标：调制复杂度评分（MCS）、边缘指标（EM）、计划不规则性（PI）、计划调制（PM）、叶片间隙（LG）和小孔径评分（SAS10）。MCS 和 PM 的定义进行了调整，以适应 CK 计划的特点。将计算出的指标用于比较 24 个选定病例中现有的优化算法（顺序和 VOLO）在计划复杂度方面的差异。然后，在来自不同治疗部位（主要是肝脏、前列腺、胰腺和脊柱）的大量（103）VOLO SBRT 临床计划中计算了这些指标。使用 Pearson r 研究每对指标之间的关系。最后，分析了复杂度指数与 PSQA 结果（表达为 3%、1mm 和 2%、1mm 的伽玛指数通过率（GPR））之间的相关性。Pearson r 的绝对值在 0.2-0.39 之间表示弱相关，在 0.4-0.59 之间表示中度相关，在 0.6-0.79 之间表示强相关，在 0.8-1 之间表示非常强相关。
结果
与 VOLO 相比，顺序计划显示出更高的复杂度，具有更低的 MCS 和 LG 值以及更高的 EM、PM 和 PI 值。对于 5/6 个指标（Wilcoxon P<0.05），差异具有统计学意义。对 VOLO 临床计划的分析显示，来自不同治疗部位的计划之间存在不同程度的复杂度，从肝脏到前列腺、胰腺，最后到脊柱，复杂性逐渐增加。对指标对之间的依赖性分析表明，MCS 和 PM（r=-0.97）以及 EM 和 LG（r=-0.82）之间存在非常强的显著负相关。除了 PI 之外，大多数其他对之间的相关性表现为中度到强，PI 与其他指标的相关性较弱。除了 PI 之外，所有指标都与（3%、1mm）和（2%、1mm）的 GPR 值表现出中度显著相关性，PI 则没有相关性。
结论
首次为基于 CK MLC 的计划计算了调制复杂度指标，并对某些指标的定义进行了调整，以适应 CK 计划的特点。计算出的指标证明是比较优化算法和描述 CK 临床计划的有用工具。一些指标对之间存在强和非常强的相关性。与 PSQA GPR 存在一些显著相关性，表明某些指标有望实现合理化并减少 PSQA 工作量。我们的结果为未来评估新的优化算法和治疗计划系统版本以及在多中心和多平台比较中评估基于 CK MLC 的计划的复杂性奠定了基础。
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