Enhancing patient‐specific quality assurance for VMAT for breast cancer treatment: A machine learning approach for gamma passing rate (GPR) prediction

Abstract

BACKGROUND: Modern radiation therapy for breast cancer has significantly advanced with the adoption of volumetric modulated arc therapy (VMAT), offering enhanced precision and improved treatment efficiency. PURPOSE: To ensure the accuracy and precision of such complex treatments, a robust patient-specific quality assurance (PSQA) protocol is essential. This study investigates the potential of machine learning (ML) models to predict gamma passing rates (GPR), a key metric in PSQA. METHODS: A dataset comprising 863 VMAT plans was used to develop and compare seven ML models: Histogram-based gradient boosting regressor, random forest regressor, extra trees regressor, gradient boosting regressor, linear regression, AdaBoost regressor, and Multi-layer perceptron regressor. These models incorporated anatomical, dosimetric, and plan complexity features. RESULTS: Among the evaluated models, the extra trees regressor (ETR), random forest regressor (RFR), and gradient boosting regressor (GBR) demonstrated the best performance, achieving mean absolute errors (MAEs) of 0.51%, 0.52%, and 0.51%, and mean squared errors (MSEs) of 0.0051%, 0.0051%, and 0.0052%, respectively, on the validation dataset. CONCLUSIONS: This study highlights the promise of ML-based approaches in streamlining PSQA processes, thereby supporting the quality assurance of breast cancer treatments using VMAT.