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Abstract：
OBJECTIVE: To assess the impact of the planner\'s experience and optimization algorithm on the plan quality and complexity of total marrow and lymphoid irradiation (TMLI) delivered by means of volumetric modulated arc therapy (VMAT) over 2010-2022 at our institute.
METHODS: Eighty-two consecutive TMLI plans were considered. Three complexity indices were computed to characterize the plans in terms of leaf gap size, irregularity of beam apertures, and modulation complexity. Dosimetric points of the target volume (D2%) and organs at risk (OAR) (Dmean) were automatically extracted to combine them with plan complexity and obtain a global quality score (GQS). The analysis was stratified based on the different optimization algorithms used over the years, including a knowledge-based (KB) model. Patient-specific quality assurance (QA) using Portal Dosimetry was performed retrospectively, and the gamma agreement index (GAI) was investigated in conjunction with plan complexity.
RESULTS: Plan complexity significantly reduced over the years (r = -0.50, p < 0.01). Significant differences in plan complexity and plan dosimetric quality among the different algorithms were observed. Moreover, the KB model allowed to achieve significantly better dosimetric results to the OARs. The plan quality remained similar or even improved during the years and when moving to a newer algorithm, with GQS increasing from 0.019 ± 0.002 to 0.025 ± 0.003 (p < 0.01). The significant correlation between GQS and time (r = 0.33, p = 0.01) indicated that the planner\'s experience was relevant to improve the plan quality of TMLI plans. Significant correlations between the GAI and the complexity metrics (r = -0.71, p < 0.01) were also found.
CONCLUSIONS: Both the planner\'s experience and algorithm version are crucial to achieve an optimal plan quality in TMLI plans. Thus, the impact of the optimization algorithm should be carefully evaluated when a new algorithm is introduced and in system upgrades. Knowledge-based strategies can be useful to increase standardization and improve plan quality of TMLI treatments.
METHODS: Eighty-two consecutive TMLI plans were considered. Three complexity indices were computed to characterize the plans in terms of leaf gap size, irregularity of beam apertures, and modulation complexity. Dosimetric points of the target volume (D2%) and organs at risk (OAR) (Dmean) were automatically extracted to combine them with plan complexity and obtain a global quality score (GQS). The analysis was stratified based on the different optimization algorithms used over the years, including a knowledge-based (KB) model. Patient-specific quality assurance (QA) using Portal Dosimetry was performed retrospectively, and the gamma agreement index (GAI) was investigated in conjunction with plan complexity.
RESULTS: Plan complexity significantly reduced over the years (r = -0.50, p < 0.01). Significant differences in plan complexity and plan dosimetric quality among the different algorithms were observed. Moreover, the KB model allowed to achieve significantly better dosimetric results to the OARs. The plan quality remained similar or even improved during the years and when moving to a newer algorithm, with GQS increasing from 0.019 ± 0.002 to 0.025 ± 0.003 (p < 0.01). The significant correlation between GQS and time (r = 0.33, p = 0.01) indicated that the planner\'s experience was relevant to improve the plan quality of TMLI plans. Significant correlations between the GAI and the complexity metrics (r = -0.71, p < 0.01) were also found.
CONCLUSIONS: Both the planner\'s experience and algorithm version are crucial to achieve an optimal plan quality in TMLI plans. Thus, the impact of the optimization algorithm should be carefully evaluated when a new algorithm is introduced and in system upgrades. Knowledge-based strategies can be useful to increase standardization and improve plan quality of TMLI treatments.
摘要：
目的：评估计划者的经验和优化算法对2010-2022年通过体积调节电弧疗法（VMAT）进行的全骨髓和淋巴照射（TMLI）计划质量和复杂性的影响。
方法：考虑了82个连续的TMLI计划。计算了三个复杂性指数，以叶片间隙大小来表征计划，光束孔径的不规则性，和调制复杂性。自动提取目标体积（D2%）和危险器官（OAR）（Dmean）的剂量测定点，以将它们与计划复杂性结合起来，并获得全局质量评分（GQS）。根据多年来使用的不同优化算法进行了分层分析，包括基于知识的(KB)模型。回顾性地使用门静脉剂量学进行患者特定的质量保证（QA），并结合计划复杂性研究了伽马一致性指数(GAI)。
结果：多年来，计划复杂性显着降低（r=-0.50，p<0.01）。观察到不同算法之间计划复杂性和计划剂量测定质量的显着差异。此外，KB模型允许对OAR实现明显更好的剂量测定结果。多年来，计划质量保持相似甚至提高，并且当移动到一个新的算法时，GQS从0.019±0.002增加到0.025±0.003(p<0.01)。GQS与时间之间的显着相关性（r=0.33，p=0.01）表明计划者的经验与提高TMLI计划的计划质量有关。还发现GAI和复杂性度量之间的显著相关性(r=-0.71,p<0.01)。
结论：计划者的经验和算法版本对于实现TMLI计划中的最佳计划质量至关重要。因此，在引入新算法和系统升级时，应仔细评估优化算法的影响。基于知识的策略可用于提高标准化和提高TMLI治疗的计划质量。
方法：考虑了82个连续的TMLI计划。计算了三个复杂性指数，以叶片间隙大小来表征计划，光束孔径的不规则性，和调制复杂性。自动提取目标体积（D2%）和危险器官（OAR）（Dmean）的剂量测定点，以将它们与计划复杂性结合起来，并获得全局质量评分（GQS）。根据多年来使用的不同优化算法进行了分层分析，包括基于知识的(KB)模型。回顾性地使用门静脉剂量学进行患者特定的质量保证（QA），并结合计划复杂性研究了伽马一致性指数(GAI)。
结果：多年来，计划复杂性显着降低（r=-0.50，p<0.01）。观察到不同算法之间计划复杂性和计划剂量测定质量的显着差异。此外，KB模型允许对OAR实现明显更好的剂量测定结果。多年来，计划质量保持相似甚至提高，并且当移动到一个新的算法时，GQS从0.019±0.002增加到0.025±0.003(p<0.01)。GQS与时间之间的显着相关性（r=0.33，p=0.01）表明计划者的经验与提高TMLI计划的计划质量有关。还发现GAI和复杂性度量之间的显著相关性(r=-0.71,p<0.01)。
结论：计划者的经验和算法版本对于实现TMLI计划中的最佳计划质量至关重要。因此，在引入新算法和系统升级时，应仔细评估优化算法的影响。基于知识的策略可用于提高标准化和提高TMLI治疗的计划质量。
