Standardization and Automation of Monitor Unit Calculation for Clinical Electron Simulation and Treatment In a Multi-Center Radiation Oncology Network
Abstract
Purpose
Urgent clinical electron radiotherapy for postoperative treatments (e.g., keloid) is often performed without CT-based planning. In a large hospital network with multiple treatment locations, variability in electron data and workflows across treatment machines, together with on-the-fly changes of treatment parameters including beam energy, applicator or cutout size, bolus thickness and source-to-surface distances (SSD) increases risk of calculation error and decreases efficiency. This work describes the development and clinical implementation of a standardized, automated electron monitor unit (MU) calculation workflow designed to improve efficiency, consistency, and safety.
Methods
With beam-match linacs across the network, an electronic databook was generated using Monte-Carlo dose calculations covering all clinical treatment parameters. Pre-manufactured electron cutouts with assigned identifiers were created using 3D-printed inserts, validated against databook values with cutout factor measurements, and distributed across treatment centers. A software tool interfaced to the record-and-verify (R&V) system was developed with machine-related parameters configured to guide users through a standardized workflow. A visualization tool integrated into the software enabled rapid, intuitive selection of electron energy, field size, and bolus thickness based on prescription and target configuration. It automatically updates cone/cutout factors and identifiers etc and calculates MU, and generates an RTP file and report for import into the R&V system. The efficiency and robustness of the workflow was validated in five patients.
Results
The measured cutout factors for all pre-manufactured cutouts were within 1.5% of the book values. Workflow validation indicated that an average saving of 10 minutes was achieved in calculation and documentation, no calculation error was observed due to on-the-fly setting changes. The databook was made available on the department intranet to verify software based MUs with hand calculations.
Conclusion
Standardization and automation of electron MU calculation across a multi-center network enables safe and efficient clinical electron simulation and treatment workflows in urgent clinical settings.