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Purpose: Accurate prediction of beam delivery time (BDT) is critical for operational efficiency, 4D dose calculations, and advanced proton therapy techniques. Despite its importance, no machine-specific BDT model exists for Mevion systems. Methods: ) served as the target variable. A Random Forest model was trained with cross-validation and tested on held-out data. SHAP (SHapley Additive exPlanations) analysis quantified feature contributions. Results: ) remained within intrinsic delivery variability. Conclusions: This study presents the first machine learning-based BDT model for the Mevion S250i system, accurately capturing both predictive performance and machine-specific temporal dynamics. Explainable AI analysis using SHAP provided detailed insights into the operational characteristics of the system, highlighting the contributions of energy layer switching, AA adjustments, and spot position shifts to delivery time. The proposed BDT model demonstrated strong predictive performance across the clinical applications evaluated, supporting its potential use for interplay assessment, 4D dose calculation, and delivery time-based plan optimization.
Cartechini et al. (Mon,) studied this question.