{Reference Type}: Journal Article
{Title}: Using eclipse scripting to fully automate in-vivo image analysis to improve treatment quality and safety.
{Author}: Chalise AR;Bojechko C;
{Journal}: J Appl Clin Med Phys
{Volume}: 23
{Issue}: 6
{Year}: Jun 2022
{Factor}: 2.243
{DOI}: 10.1002/acm2.13585
{Abstract}: <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.