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An effective and optimized patient-specific QA workload reduction for VMAT plans after MLC-modelling optimization.

Thomas Marsac1, Arianna Batista Camejo2, Sophie Chiavassa1

  • 1Medical Physics Department, Institut de Cancréologie de l'Ouest, Bd du Professeur Jacques Monod, 44805 Saint Herblain Cedex, France.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|February 26, 2023
PubMed
Summary

This study identified the Modulation Complexity Score for VMAT (MCSv) as an optimal metric to reduce patient-specific quality assurance (PSQA) workload. Implementing this metric and optimizing MLC modeling reduced PSQA by approximately 30%.

Keywords:
Complexity metricsMLC modellingPatient-specific quality assuranceRadiotherapyWorkload reduction

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Quality Assurance

Background:

  • Patient-specific quality assurance (PSQA) is crucial for radiation therapy.
  • Complexity metrics can characterize modulated treatment plans.
  • Reducing PSQA workload is a key objective for efficiency.

Purpose of the Study:

  • Identify optimal complexity metrics to reduce PSQA workload.
  • Optimize MLC modeling to improve measurement and calculation agreement.
  • Streamline PSQA processes for VMAT plans.

Main Methods:

  • Evaluated correlation and sensitivity of complexity metrics with PSQA results.
  • Determined thresholds for stopping PSQA based on metric values.
  • Reviewed MLC modeling using a new methodology with ionization chamber measurements.

Main Results:

  • Modulation Complexity Score for VMAT (MCSv) was the most effective metric.
  • Optimized MLC modeling reduced the number of controlled plans.
  • Plans with MCSv >= 0.34 are now treated without PSQA.

Conclusions:

  • PSQA workload was reduced by approximately 30% through rationalization.
  • Re-evaluating MLC modeling and complexity metrics can streamline PSQA.
  • Improved integration of these tools into treatment planning systems is beneficial.