Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California, USA.
J Appl Clin Med Phys. 2022 Jun;23(6):e13585. doi: 10.1002/acm2.13585. Epub 2022 Mar 22.
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.
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.
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.
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.
开发了一种自动化的体内系统,用于检测患者解剖结构变化和机器输出,该系统使用新型分析方法对瓦里安 Halcyon 治疗过程中的每一个分次的体内电子射野影像装置 (EPID) 图像进行分析。该体内方法可识别常规质量保证 (QA) 无法检测到的误差,与日常机器性能检查 (MPC) 相辅相成,物理师工作量最小。
在治疗日结束时,自动下载并分析在 Halcyon 上治疗的所有分次的图像。对于图像分析,与第一次分次相比,计算高剂量感兴趣区域的平均差异。该指标已被证明可预测计划治疗体积 (PTV) 平均剂量的变化。对于以下两种情况,将发出警报:(A 类)分次的平均差异超过 10%,以防止出现较大误差;(B 类)连续三个分次的平均差异超过 ±3%,以防止出现系统性趋势。如果超过阈值,则向物理师发送电子邮件,对标记患者进行调查。为了跟踪机器输出变化,对于当天治疗的所有患者,将平均值和标准偏差上传到 QA 门户,并与审查后的 MPC 一起上传,以确保对 Halcyon 进行全面的 QA。为了指导临床实施,我们进行了一项回顾性研究,从 2017 年 11 月到 2020 年 12 月,根据治疗部位对误差进行分组。自 2021 年 1 月以来,该框架已被前瞻性使用。
在 1633 名患者(35759 个分次)的回顾性数据中,未发现 A 类错误,只有 45 名患者(2.76%)出现 B 类错误。这些 B 类偏差是由于头颈部体重减轻所致。在 6 个月的前瞻性使用期间(345 名患者),有 13 名患者(3.7%)出现 B 类错误,没有 A 类错误。
该自动化系统可防止体内发生的错误,从而提供更全面的 QA。这种全自动框架可以在其他装有 Halcyon 的中心实施,需要一台台式计算机和分析脚本。