Proton arc therapy plan optimization with energy layer pre-selection driven by organ at risk sparing and delivery...

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Proton arc therapy (PAT) offers advanced cancer treatment but faces challenges in optimizing delivery time.
• Current methods for minimizing PAT delivery time are either computationally intensive or compromise treatment flexibility.

Purpose of the Study:

• To develop and evaluate a flexible method for pre-selecting energy layers (EL) in PAT treatment planning.
• To optimize PAT plans for reduced delivery time while ensuring high target coverage and organ-at-risk sparing.

Main Methods:

• A metaheuristic approach was used to minimize a bi-objective function for EL pre-selection, balancing delivery time and tumor coverage.
• The method was evaluated on three treatment sites and benchmarked against existing techniques like ELSA and IMPT in RayStation TPS.

Main Results:

• The algorithm efficiently generated Pareto-optimal EL pre-selections in approximately 5 minutes.
• Resulting PAT plans showed high target coverage, conformity, and effective organ sparing.
• Delivery times were reduced by 20%-40% compared to ELSA plans.

Conclusions:

• The proposed algorithm provides a fast and efficient solution for high-quality PAT plan generation.
• It enables reduced delivery times in PAT by optimizing proton arc sector placement.
• This method effectively balances treatment efficiency with clinical goals for target coverage and healthy tissue sparing.