Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors

Raphael J. Douglas, Adenike Olanrewaju, Lifei Zhang, Beth M. Beadle, Laurence E. Court

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines. Materials and methods: This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, and planning structures, such that a clinically acceptable plan is achieved in a single optimization. This model was used to generate and optimize volumetric-modulated arc therapy plans for a cohort of 50 patients treated for head-neck cancer. Plans were generated using the following treatment machines: Varian 2100, Elekta Versa HD, and Varian Halcyon. A noninferiority testing methodology was used to evaluate the hypothesis that normal and target metrics in our autoplans were no worse than a set of clinically-acceptable baseline plans by a margin of 1.8 Gy or 3% dose-volume. The quality of these plans were also compared through the use of common clinical dose-volume histogram criteria. Results: The Versa HD met our noninferiority criteria for 23 of 34 normal and target metrics; while the Halcyon and Varian 2100 machines met our criteria for 24 of 34 and 26 of 34 metrics, respectively. The experimental plans tended to have less volume coverage for prescription dose planning target volume and larger hotspot volumes. However, comparable plans were generated across different treatment machines. Conclusions: These results support the use of a head-neck RapidPlan models in centralized planning workflows that support clinics with different linac models/vendors, although some fine-tuning for targets may be necessary.

Original languageEnglish (US)
Article numbere13704
JournalJournal of applied clinical medical physics
Volume23
Issue number8
DOIs
StatePublished - Aug 2022

Keywords

  • automated planning
  • knowledge-based planning

ASJC Scopus subject areas

  • Radiation
  • Instrumentation
  • Radiology Nuclear Medicine and imaging

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