Including robustness in multi-criteria optimization for intensity-modulated proton therapy.

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Intensity-modulated proton therapy (IMPT) offers precise dose delivery but is sensitive to uncertainties.
• Multi-criteria optimization (MCO) frameworks explore trade-offs between various treatment planning objectives.
• Integrating robustness into MCO is crucial for reliable IMPT outcomes.

Purpose of the Study:

• To develop and evaluate a method for incorporating robustness into the MCO framework for IMPT.
• To simultaneously optimize for nominal plan quality and robustness against treatment uncertainties.
• To enable interactive selection of treatment plans balancing competing objectives.

Main Methods:

• Robustness was integrated into MCO by adding robustified objectives and constraints.
• Uncertainties were modeled using pre-calculated dose-influence matrices for nominal and error scenarios.
• A linear projection solver handled large-scale optimization problems efficiently.
• The method was demonstrated on base-of-skull and chordoma cases.

Main Results:

• The robust optimization method significantly reduced plan sensitivity to setup and range errors compared to safety margins.
• Analysis of a chordoma case revealed trade-offs between target coverage, organ-at-risk sparing, and robustness.
• The MCO approach effectively illustrated the interplay between robustness and nominal plan quality.
• Robust optimization for each Pareto optimal plan was computationally efficient (<5 minutes per plan).

Conclusions:

• The proposed method effectively reduces uncertainties in IMPT by optimizing for robustness within an MCO framework.
• Interactive exploration of the Pareto surface allows clinicians to select preferred plans balancing objectives and robustness.
• This approach offers a computationally feasible solution for robust IMPT planning.