Cherenkov and Plan Integration for Real-Time Delivery Verification: The Opportunity for Automated Visualization and Guidance of All Treatments in EBRT

Abstract

Initiatives in the national radiation oncology incident learning system (RO-ILS) have been exceptionally useful in discovery of factors that lead to incidents and learning of best practices that can help make the national practice of RO safer as a whole. Incident learning systems come from the flight industry, where both visual cues and instrument control are essential parts of implementation, and similarly both human and instrument tools are used in RO. However, numerous RO studies have reported that human factors are a leading cause of incidents discovered and that timelines, such as QA, setup, delivery, and verification are areas where most incidents are found. Tools such as Cherenkov imaging and SGRT can be used to automate many of the riskiest human decisions and/or providing both human vision and instrument-guided oversight in these areas of treatment delivery process. The value of continuous online imaging is reviewed and the two parts have been tested towards complete automation. The conceptual framework of comparing images to the patient treatment plan is outlined with software examples. Development towards a combined SGRT & Cherenkov imaging system that could achieve fully automated incident detection is outlined. Cherenkov imaging has shown direct visualization of many instances of beam delivery to patients that were sub-optimal. These are seen mostly in isolated cases of normal tissue in the beam where it was not expected, such as limbs, chin, contralateral breast or axilla. Incorrect placement of bolus is also readily visualized. Comparisons can be made on a day-to-day basis, but also on a delivery to plan basis if the plan was incorporated into the treatment delivery process. Analysis of the incidents seen indicates that there are automatable metrics of image quality that could have detected them. Overall, if the system detection of variations were fully automated, these could be detected without human intervention. The capabilities for reducing nearly all human error in setup and delivery are available or emerging, and Cherenkov imaging is perhaps one of the most direct ways to capture and computationally analyze the treatment in real time. These tools require further integration with automated analysis and plan integration, but the initial steps are well underway and individual parts are now possible with advances in R&D of the system integration.