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Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources
Published on: October 6, 2023
Adapt-on-demand: Enabling flexible and scalable adaptive radiotherapy through workflow automation
Sean Domal1, Min Geon Choi1, Justin Visak1
1Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA.
Journal of Applied Clinical Medical Physics
|May 14, 2026
Summary
This study introduces an Adapt-on-Demand (AOD) framework that streamlines adaptive radiotherapy (ART) delivery using automated scripting. The AOD framework enhances clinical throughput and patient access to advanced radiation therapy techniques.
Area of Science:
- Radiation Oncology
- Medical Physics
- Health Informatics
Background:
- CT-guided adaptive radiotherapy (CTgART) provides clinical benefits but faces limitations due to resource demands and rigid workflows.
- Current systems often struggle to balance adaptive capabilities with high-throughput delivery needs.
Purpose of the Study:
- To present a flexible and scalable "Adapt-on-Demand" (AOD) framework.
- To integrate Varian Ethos adaptive capabilities with Varian Halcyon's high-throughput IGRT delivery.
- To improve the efficiency and accessibility of adaptive radiotherapy.
Main Methods:
- Developed an end-to-end dual-platform workflow using automated scripting (ESAPI).
- Implemented dynamic transitions between ART and IGRT delivery based on patient requirements.
- Evaluated workflow efficiency through time-analysis, ART utilization, and machine throughput tracking.
- Validated generalizability via multi-institutional deployment.
Main Results:
- Automated plan setup time reduced from 45 to 8 minutes (p < 0.001).
- Confirmed exact agreement between native and converted plans for MLC leaf positions.
- Increased ART utilization on the Ethos platform from 17% to 77% after offloading IGRT to Halcyon.
- Demonstrated successful, unmodified implementation at two external clinical sites.
Conclusions:
- The AOD framework enables flexible ART delivery without compromising workflow efficiency.
- Automating inter-platform transitions and reducing adaptive system burden improves clinical throughput.
- This scalable model enhances patient access to ART and highlights the importance of system interoperability.
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