摘要目的:通过Johnson转换将统计过程控制(SPC)技术运用到非正态分布的放疗计划个体化质量保证(PSQA)过程，建立基于治疗部位、剂量/距离评估标准的个体化容差限值和干预限值，为改进调强放疗计划验证和剂量投照精度提供参考。方法:选取在Halcyon上执行的951例PSQA γ分析数据，按部位分为6个组，其中脑102例，头颈100例，乳腺229例，肺154例，食管223例，盆腔143例。采用Minitab 21软件进行统计学分析，分别对各组数据进行Anderson-Darling正态性检验( α＝ 0.05)。对非正态分布数据先通过Johnson转换为正态分布，再建立相应部位的容差限值及干预限值，并与基于正态分布的Shewhart控制图进行比较。 结果:6个组的PSQA结果均为非正态分布( P<0.05)。3%/2 mm标准下头颈、乳腺、肺、食管、盆腔经Johnson转换后得到的容差限值为95.13%～96.16%，干预限值为94.19% ～ 95.91%，2%/2 mm标准下容差限值为91.15%～94.86%，干预限值为89.94%～94.78%。若忽略正态假设前提，直接采用Shewhart得到的容差限值高于Johnson变换后的容差限值，增加了非正态PSQA过程的假阳性率。 结论:直接将SPC技术应用于偏态过程会导致虚警率的增加和错误的过程解释。通过Johnson转换的SPC技术能更加有效地监控非正态PSQA过程，根据不同部位设置相应的限值有助于及时发现引起过程失控的因素。

abstractsObjective:To apply statistical process control (SPC) techniques to the quality assurance of non-normal radiotherapy plans through Johnson transformation, establishing patient-specific tolerance and action limits based on treatment sites and dose/distance assessment criteria, thereby enhancing the intensity-modulated radiation therapy (IMRT) verification accuracy and dose delivery precision.Methods:In this study, 951 gamma analysis data of patient-specific quality assurance (PSQA) executed on the Halcyon accelerator platform were selected and categorized into six groups based on treatment sites, including brain (102 cases), head and neck (100 cases), breast (229 cases), lung (154 cases), esophagus (223 cases), and pelvic (143 cases) groups. The six groups of data were statistically analyzed through Anderson-Darling normality tests ( α = 0.05) using Minitab 21 software. Non-normal data were transformed into normal data through Johnson transformation and then were used to establish treatment site-specific tolerance and action limits, which were compared with the Shewhart control charts based on normal distributions. Results:The PSQA result of the six groups all exhibited non-normal distributions ( P < 0.05). Through Johnson transformation, the tolerance and action limits for the head and neck, breast, lung, esophagus, and pelvic areas under the 3%/2 mm criterion ranged from 95.13% to 96.16% and 94.19% to 95.91%, respectively. In contrast, the tolerance and action limits ranged from 91.15% to 94.86% and 89.94% to 94.78% under the 2%/2 mm criterion. Directly applying Shewhart control charts without normality assumptions yielded higher tolerance limits compared to the application of Johnson transformation, increasing the false positive rate in the non-normal PSQA process. Conclusions:Applying the SPC techniques directly to a non-normal process can lead to an increased false alarm rate and wrong process interpretation. The SPC techniques combined with Johnson transformation enable more effective monitoring of a non-normal PSQA process, facilitating timely identification of potential factors that may lead to an out-of-control process based on the treatment site-specific limits.