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Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization.

Hossein Jafarzadeh1, Majd Antaki1, Ximeng Mao2

  • 1Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada.

Physics in Medicine and Biology
|April 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces multi-objective Bayesian optimization with q-noisy expected hypervolume improvement (MOBO-qNEHVI) to automate high dose rate brachytherapy treatment planning. The method efficiently finds optimal plans, reducing reliance on planner experience and improving organ-at-risk sparing.

Keywords:
Bayesian optimizationRapidBrachyMCTPShigh dose rate brachytherapymixed integer optimizationmulti-criteria optimizationprostate cancer

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • High dose rate brachytherapy treatment plan optimization often relies on manual adjustments of penalty weights, which is time-consuming and experience-dependent.
  • Automating this process is crucial for improving efficiency and consistency in treatment planning.

Purpose of the Study:

  • To automate treatment plan optimization in high dose rate brachytherapy using multi-objective Bayesian optimization with q-noisy expected hypervolume improvement (MOBO-qNEHVI).
  • To investigate the relationship between MOBO-qNEHVI iterations and the rate of clinically acceptable plans.
  • To evaluate the performance time and efficiency of the automated optimization process.

Main Methods:

  • Retrospective analysis of 13 prostate cancer patient treatment plans using a research treatment planning system (RapidBrachyMTPS).
  • Application of MOBO-qNEHVI with fast mixed integer optimization (FMIO) to find patient-specific Pareto optimal penalty weight vectors.
  • Investigation of acceptance rates and performance times across various parameter configurations.

Main Results:

  • MOBO-qNEHVI successfully generated clinically acceptable treatment plans for all patients.
  • Plan acceptance rate increased logarithmically with more MOBO-qNEHVI iterations, while performance time grew exponentially.
  • Optimized parameters (6 iterations, 25 parallel FMIO samples) achieved target dose delivery with minimal organ-at-risk dose, yielding an 89.74% average acceptance rate within 66.6 seconds.

Conclusions:

  • MOBO-qNEHVI combined with FMIO offers an automated, patient-specific approach to optimize brachytherapy treatment plans within minutes.
  • This method reduces dependence on planner expertise and can minimize radiation dose to organs at risk.
  • The approach demonstrates significant potential for improving the quality and efficiency of brachytherapy treatment planning.