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

This study presents and evaluates an automated volumetric modulated arc therapy (VMAT) planning framework for breast cancer based on objective function value (OFV)-guided optimization. The primary objective is to systematically improve organ-at-risk sparing through automated and reproducible optimization of planning constraints while maintaining clinically acceptable target coverage. An OFV-guided optimization workflow was empirically developed using a separate sensitivity dataset and subsequently evaluated in 20 clinical breast cancer patients (13 left-sided, 7 right-sided). The automated Python-based framework iteratively adapts MaxEUD constraints during optimization until dose metrics converge, without manual intervention. Automatically generated plans were compared to clinically delivered VMAT plans using target coverage, dose-volume metrics, and monitor units as a surrogate for delivery efficiency. The automated approach consistently achieved significant reductions in mean organ doses and low-dose volumes (e.g., [Formula: see text], [Formula: see text]) while preserving PTV coverage. Mean heart dose decreased from [Formula: see text] Gy to [Formula: see text] Gy for left-sided cases and from [Formula: see text] Gy to [Formula: see text] Gy for right-sided cases ([Formula: see text]). Significant dose reductions were also observed for the ipsilateral lung, contralateral lung, and contralateral breast, accompanied by a 16.9 % reduction in monitor units ([Formula: see text]) and reduced inter-patient variability. In contrast, no statistically significant difference was observed for lung [Formula: see text]. In conclusion, OFV-guided VMAT optimization enables reproducible and systematic improvement of organ-at-risk sparing in breast radiotherapy. By reducing organ doses, low-dose burden, monitor units, and inter-patient variability without compromising target coverage, the proposed framework could provide a robust and standardized baseline for clinical VMAT planning and a consistent foundation for future data-driven and machine-learning-based optimization approaches.