A multicriteria framework with voxel-dependent parameters for radiotherapy treatment plan optimization

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Current radiotherapy treatment planning relies on organ-based objective functions and parameters.
• Adjusting parameters per voxel shows potential for improved plan quality but lacks mathematical grounding.
• Unanswered questions exist regarding objective function selection, Pareto optimality, and organ-based vs. voxel-based model relationships.

Purpose of the Study:

• To establish a novel mathematical framework for radiotherapy treatment optimization.
• To incorporate voxel-dependent optimization parameters into the framework.
• To clarify the mathematical underpinnings of voxel-based optimization and its relationship to organ-based methods.

Main Methods:

• Development of a new mathematical framework for radiotherapy treatment optimization.
• Inclusion of two key theorems within the framework.
• Analysis of voxel-dependent vs. organ-dependent parameter adjustments and objective function impacts.

Main Results:

• The voxel-based model's Pareto surface is independent of objective function selection, unlike the organ-based model.
• Voxel-based parameter adjustment explores a significantly larger Pareto surface, enabling better clinical trade-offs.
• All Pareto solutions from organ-based models are subsets of the unique Pareto surface generated by the voxel-based model.

Conclusions:

• A new mathematical framework for radiotherapy optimization using voxel-based parameters has been successfully developed.
• This framework allows for improved plan quality through voxel-based weighting factor adjustments.
• The unique and extensive Pareto surface generated by the voxel-based model encompasses all organ-based solutions.