Abstract：
OBJECTIVE: An automated, in-vivo system to detect patient anatomy changes and machine output was developed using novel analysis of in-vivo electronic portal imaging device (EPID) images for every fraction of treatment on a Varian Halcyon. In-vivo approach identifies errors that go undetected by routine quality assurance (QA) to compliment daily machine performance check (MPC), with minimal physicist workload.
METHODS: Images for all fractions treated on a Halcyon were automatically downloaded and analyzed at the end of treatment day. For image analysis, compared to first fraction, the mean difference of high-dose region of interest is calculated. This metric has shown to predict changes in planning treatment volume (PTV) mean dose. Flags are raised for: (Type-A) treatment fraction whose mean difference exceeds 10%, to protect against large errors, and (Type-B) patients with three consecutive fractions with mean exceeding ±3%, to protect against systematic trends. If a threshold is exceeded, a physicist is e-mailed, a report for flagged patients, for investigation. To track machine output changes, for all patients treated on a day, the average and standard deviations are uploaded to a QA portal, along with the reviewed MPC, ensuring comprehensive QA for the Halcyon. To guide clinical implementation, a retrospective study from November 2017 till December 2020 was conducted, which grouped errors by treatment site. This framework has been used prospectively since January 2021.
RESULTS: From retrospective data of 1633 patients (35 759 fractions), no Type-A errors were found and only 45 patients (2.76%) had Type-B errors. These Type-B deviations were due to head-and-neck weight loss. For 6 months of prospective use (345 patients), 13 patients (3.7%) had Type-B errors and no Type-A errors.
CONCLUSIONS: This automated system protects against errors that can occur in vivo to provide a more comprehensive QA. This fully automated framework can be implemented in other centers with a Halcyon, requiring a desktop computer and analysis scripts.
METHODS: Images for all fractions treated on a Halcyon were automatically downloaded and analyzed at the end of treatment day. For image analysis, compared to first fraction, the mean difference of high-dose region of interest is calculated. This metric has shown to predict changes in planning treatment volume (PTV) mean dose. Flags are raised for: (Type-A) treatment fraction whose mean difference exceeds 10%, to protect against large errors, and (Type-B) patients with three consecutive fractions with mean exceeding ±3%, to protect against systematic trends. If a threshold is exceeded, a physicist is e-mailed, a report for flagged patients, for investigation. To track machine output changes, for all patients treated on a day, the average and standard deviations are uploaded to a QA portal, along with the reviewed MPC, ensuring comprehensive QA for the Halcyon. To guide clinical implementation, a retrospective study from November 2017 till December 2020 was conducted, which grouped errors by treatment site. This framework has been used prospectively since January 2021.
RESULTS: From retrospective data of 1633 patients (35 759 fractions), no Type-A errors were found and only 45 patients (2.76%) had Type-B errors. These Type-B deviations were due to head-and-neck weight loss. For 6 months of prospective use (345 patients), 13 patients (3.7%) had Type-B errors and no Type-A errors.
CONCLUSIONS: This automated system protects against errors that can occur in vivo to provide a more comprehensive QA. This fully automated framework can be implemented in other centers with a Halcyon, requiring a desktop computer and analysis scripts.
摘要：
目标：自动，使用对VarianHalcyon治疗的每个部分的体内电子射野成像设备（EPID）图像的新颖分析，开发了用于检测患者解剖结构变化和机器输出的体内系统。体内方法识别常规质量保证（QA）未检测到的错误，以补充日常机器性能检查（MPC），以最小的物理学家工作量。
方法:在治疗日结束时自动下载并分析在Halcyon上处理的所有级分的图像。对于图像分析，与第一部分相比，计算高剂量感兴趣区域的平均差。该度量已显示出预测计划治疗体积(PTV)平均剂量的变化。提高标志：（A型）平均差异超过10％的治疗分数，为了防止大错误，和（B型）三个连续分数平均超过±3％的患者，防止系统性趋势。如果超过阈值，一个物理学家收到电子邮件,被标记的病人的报告，为了调查。要跟踪机器输出的变化，对于所有接受治疗的患者，平均值和标准偏差被上传到QA门户，连同审查的MPC，确保Halcyon的全面质量保证。指导临床实施,2017年11月至2020年12月进行了一项回顾性研究，按治疗部位对错误进行分组。自2021年1月以来，该框架一直在使用。
结果：来自1633例患者（35759个分数）的回顾性数据，未发现A型错误,只有45例(2.76%)患者出现B型错误.这些B型偏差是由于头颈部体重减轻。对于6个月的前瞻性使用（345名患者），13例患者（3.7%）有B型错误，无A型错误。
结论：该自动化系统可防止体内可能发生的错误，以提供更全面的QA。这个完全自动化的框架可以在其他中心与Halcyon实现，需要台式计算机和分析脚本。
方法:在治疗日结束时自动下载并分析在Halcyon上处理的所有级分的图像。对于图像分析，与第一部分相比，计算高剂量感兴趣区域的平均差。该度量已显示出预测计划治疗体积(PTV)平均剂量的变化。提高标志：（A型）平均差异超过10％的治疗分数，为了防止大错误，和（B型）三个连续分数平均超过±3％的患者，防止系统性趋势。如果超过阈值，一个物理学家收到电子邮件,被标记的病人的报告，为了调查。要跟踪机器输出的变化，对于所有接受治疗的患者，平均值和标准偏差被上传到QA门户，连同审查的MPC，确保Halcyon的全面质量保证。指导临床实施,2017年11月至2020年12月进行了一项回顾性研究，按治疗部位对错误进行分组。自2021年1月以来，该框架一直在使用。
结果：来自1633例患者（35759个分数）的回顾性数据，未发现A型错误,只有45例(2.76%)患者出现B型错误.这些B型偏差是由于头颈部体重减轻。对于6个月的前瞻性使用（345名患者），13例患者（3.7%）有B型错误，无A型错误。
结论：该自动化系统可防止体内可能发生的错误，以提供更全面的QA。这个完全自动化的框架可以在其他中心与Halcyon实现，需要台式计算机和分析脚本。
