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Related Experiment Video

Updated: Sep 5, 2025

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
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Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors.

Raphael J Douglas1, Adenike Olanrewaju1, Lifei Zhang1

  • 1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Journal of Applied Clinical Medical Physics
|July 6, 2022
PubMed
Summary

Knowledge-based planning (KBP) ensures consistent radiation therapy plans across different machines. This study found comparable quality, supporting centralized planning workflows despite minor target adjustments needed for specific linacs.

Keywords:
automated planningknowledge-based planning

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Cancer Treatment

Background:

  • Knowledge-based planning (KBP) is crucial for quality control in intensity-modulated radiation therapy (IMRT).
  • Previous studies confirmed KBP's consistency across institutions and planners.
  • This research investigates KBP consistency across diverse treatment machines.

Purpose of the Study:

  • To evaluate the consistency of radiation therapy plan quality using a KBP model across different linear accelerator (linac) machines.
  • To assess if KBP-generated plans maintain quality standards regardless of the treatment machine used.

Main Methods:

  • A Varian Medical Systems RapidPlan model was adapted with specific planning objectives and constraints.
  • Volumetric-modulated arc therapy (VMAT) plans were generated for 50 head-and-neck cancer patients.
  • Plans were created using Varian 2100, Elekta Versa HD, and Varian Halcyon machines, with noninferiority testing against baseline plans.

Main Results:

  • The Elekta Versa HD met noninferiority criteria for 23/34 metrics, Halcyon for 24/34, and Varian 2100 for 26/34.
  • Experimental plans showed slightly reduced target volume coverage and increased hotspot volumes.
  • Overall, comparable plan quality was achieved across the different treatment machines.

Conclusions:

  • Head-and-neck KBP models are suitable for centralized planning across various linac models and vendors.
  • Minor fine-tuning of planning targets may be required for optimal results on different machines.