| 邓娟,刘高元,尹楚欧,等.基于统计过程控制的非正态放疗计划个体化质量保证[J].中华放射医学与防护杂志,2025,45(4):296-301.Deng Juan,Liu Gaoyuan,Yin Chuou,et al.Patient-specific quality assurance for non-normal radiotherapy plans based on statistical process control[J].Chin J Radiol Med Prot,2025,45(4):296-301 |
| 基于统计过程控制的非正态放疗计划个体化质量保证 |
| Patient-specific quality assurance for non-normal radiotherapy plans based on statistical process control |
| 投稿时间：2024-03-04 |
| DOI：10.3760/cma.j.cn112271-20240304-00083 |
| 中文关键词: 放射治疗 统计过程控制 非正态分布 患者个体化质量保证 |
| 英文关键词:Radiotherapy Statistical process control Non-normal distribution Patient-specific quality assurance |
| 基金项目:国家自然科学基金(12275012,12475309,12411530076,82202941);北京市自然科学基金(Z210008);中央高校基本科研业务费/北京大学临床医学+X 青年专项(PKU2024LCXQ033);国家重点研发计划(2019YFF01014405);教育部内地与港澳高等学校师生交流计划项目(万人计划7111400049);中国国际人才交流基金会国际青年人才来华交流项目(JC202502001F) |
| 作者 | 单位 | E-mail | | 邓娟 | 德阳市人民医院肿瘤科, 德阳 618000 | | | 刘高元 | 德阳市人民医院肿瘤科, 德阳 618000 | | | 尹楚欧 | 德阳市人民医院肿瘤科, 德阳 618000 | | | 刘江 | 德阳市人民医院肿瘤科, 德阳 618000 | | | 梅国建 | 德阳市人民医院肿瘤科, 德阳 618000 | | | 华凌 | 北京大学肿瘤医院暨北京市肿瘤防治研究所放疗科 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 余疏桐 | 北京大学肿瘤医院暨北京市肿瘤防治研究所放疗科 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 付新辉 | 北京大学肿瘤医院暨北京市肿瘤防治研究所放疗科 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | | | 林晨 | 北京大学物理学院重离子物理研究所 核物理与核技术国家重点实验室, 北京 100871 | | | 黎田 | 香港理工大学医疗科技及资讯学系, 香港 999077 | | | 张艺宝 | 北京大学肿瘤医院暨北京市肿瘤防治研究所放疗科 恶性肿瘤发病机制及转化研究教育部重点实验室, 北京 100142 | zhangyibao@pku.edu.cn |
|
| 摘要点击次数: 283 |
| 全文下载次数: 74 |
| 中文摘要: |
| 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。 |
| 英文摘要: |
| Objective 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. |
| HTML 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|