A feasibility study of deep learning prediction model for VMAT patient-specific QA.

Purpose: This study introduces a deep learning (DL) model that leverages doses calculated from both a treatment planning system (TPS) and independent dose verification software using Monte Carlo (MC) simulations, aiming to predict the gamma passing rate (GPR) in VMAT patient-specific QA more accurately. Materials and method: We utilized data from 710 clinical VMAT plans measured with an ArcCHECK phantom. These plans were recalculated on an ArcCHECK phantom image using Pinnacle TPS and MC algorithms, and the planar dose distributions corresponding to the detector element surfaces were utilized as input for the DL model. A convolutional neural network (CNN) comprising four layers was employed for model training. The model's performance was evaluated through multiple predictive error metrics and receiver operator characteristic (ROC) curves for various gamma criteria. Results: The mean absolute errors (MAE) between measured GPR and predicted GPR are 1.1%, 1.9%, 1.7%, and 2.6% for the 3%/3mm, 3%/2mm, 2%/3mm, and 2%/2mm gamma criteria, respectively. The correlation coefficients between predicted GPR and measured GPR are 0.69, 0.72, 0.68, and 0.71 for each gamma criterion. The AUC (Area Under the Curve) values based on ROC curve for the four gamma criteria are 0.90, 0.92, 0.93, and 0.89, indicating high classification performance. Conclusion: This DL-based approach showcases significant potential in enhancing the efficiency and accuracy of VMAT patient-specific QA. This approach promises to be a useful tool for reducing the workload of patient-specific quality assurance.