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A multi-criteria optimization approach for HDR prostate brachytherapy: I. Pareto surface approximation
Songye Cui1,2, Philippe Després1,2, Luc Beaulieu1,2
1Department of Physics, Engineering Physics and Optics and Cancer Research Center, Université Laval, Quebec City, QC, G1V 0A6, Canada.
Physics in Medicine and Biology
|September 19, 2018
Summary
This study introduces a new multi-criteria optimization (MCO) algorithm to improve high dose rate (HDR) brachytherapy planning. The algorithm efficiently characterizes Pareto surfaces for prostate cancer, aiding in the selection of optimal treatment plans.
Area of Science:
- Medical Physics
- Radiation Oncology
- Computational Biology
Background:
- Current high dose rate (HDR) brachytherapy planning is iterative and time-consuming.
- Optimizing treatment plans requires balancing multiple competing objectives.
Purpose of the Study:
- To develop a novel inverse planning algorithm using multi-criteria optimization (MCO) for HDR brachytherapy.
- To improve the efficiency and effectiveness of prostate brachytherapy treatment planning.
Main Methods:
- Implemented a Pareto surface approximation algorithm within Inverse Planning Simulated Annealing.
- Generated 300 Pareto optimal plans for 140 prostate cancer cases, totaling 42,000 plans.
- Developed polynomial regression models to predict the clinically acceptable region based on anchor plans.
Main Results:
- Successfully approximated and characterized Pareto surfaces for HDR brachytherapy prostate cases.
- Identified the clinically acceptable region on the Pareto surface.
- Established a relationship between anchor plans and the clinically acceptable region.
Conclusions:
- The proposed MCO algorithm effectively characterizes Pareto surfaces for HDR brachytherapy.
- Regression models can rapidly predict the clinically acceptable region, potentially streamlining the planning process.
- This approach may help define the most relevant solution space for HDR brachytherapy planning.
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