Objective-function-guided automated VMAT planning reduces OAR dose, low-dose exposure, and inter-planner variability...

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Volumetric Modulated Arc Therapy (VMAT) is a sophisticated radiotherapy technique.
• Optimizing VMAT for breast cancer requires balancing target coverage with organ-at-risk (OAR) sparing.
• Current VMAT planning can involve manual adjustments, leading to variability.

Purpose of the Study:

• To develop and evaluate an automated VMAT planning framework for breast cancer.
• To systematically improve OAR sparing using objective function value (OFV)-guided optimization.
• To maintain clinically acceptable target coverage while reducing dose to organs at risk.

Main Methods:

• An automated Python-based framework using OFV-guided optimization was developed.
• The framework iteratively adapted MaxEUD constraints without manual intervention.
• The automated plans were compared to clinically delivered VMAT plans in 20 breast cancer patients.

Main Results:

• The automated VMAT approach significantly reduced mean organ doses and low-dose volumes for OARs, including the heart, lungs, and contralateral breast.
• Mean heart dose decreased significantly in both left-sided and right-sided breast cancer cases.
• A 16.9% reduction in monitor units and decreased inter-patient variability were observed, with preserved PTV coverage.

Conclusions:

• OFV-guided VMAT optimization enables reproducible and systematic OAR sparing in breast radiotherapy.
• The automated framework reduces organ doses, low-dose burden, monitor units, and inter-patient variability.
• This approach offers a standardized baseline for clinical VMAT planning and a foundation for future AI-driven optimization.