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Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic. [ABSTRACT FROM AUTHOR]

Patient-specific quality assurance (QA) for Volumetric Modulated Arc Therapy (VMAT) plans is routinely performed in the clinical. However, it is labor-intensive and time-consuming for medical physicists. QA prediction models can address these shortcomings and improve efficiency. Current approaches mainly focus on single cancer and single modality data. They are not applicable to clinical practice. To assess the accuracy of QA results for VMAT plans, this paper presents a new model that learns complementary features from the multi-modal data to predict the gamma passing rate (GPR). According to the characteristics of VMAT plans, a feature-data fusion approach is designed to fuse the features of imaging and non-imaging information in the model. In this study, 690 VMAT plans are collected encompassing more than ten diseases. The model can accurately predict the most VMAT plans at all three gamma criteria: 2%/2 mm, 3%/2 mm and 3%/3 mm. The mean absolute error between the predicted and measured GPR is 2.17%, 1.16% and 0.71%, respectively. The maximum deviation between the predicted and measured GPR is 3.46%, 4.6%, 8.56%, respectively. The proposed model is effective, and the features of the two modalities significantly influence QA results. [ABSTRACT FROM AUTHOR]

Patient-specific quality assurance (PSQA) of volumetric modulated arc therapy (VMAT) treatment plans is crucial to enable the plans to be validated for clinical acceptance. However, performing PSQA for clinical delivery is labor-intensive and time-consuming. The existing prediction models do not take into account the dynamic delivery process of VMAT plans. To solve the above problems and improve accuracy of PSQA, this paper presents a multilayer perceptron (MLP) neural network model with regression and ranking loss to predict the gamma passing rate (GPR). The proposed model combines a convolutional neural network with multiple MLP blocks for extracting inter-image correlation features of plan files during dynamic delivery. To focus on the similarity and specificity of multiple VMAT plans, a regression and ranking loss function with dynamic weights is proposed to optimize the training process. In addition, a clinical workflow is proposed to combine the designed model with measurement-based PSQA to screen potential risky plans better. A total of 690 VMAT plans from multiple treatment sites are collected to validate the performance. For 2%/2 mm, 3%/2 mm and 3%/3 mm, the best result of mean absolute error and max error between measured and predicted GPR are 2.17%, 1.25%, 0.74%, and 7.89%, 4.29%, 3.05%, respectively. Experimental results demonstrate that the proposed method has a state-of-the-art performance and can improve the VMAT PSQA process and reduce PSQA workloads. [ABSTRACT FROM AUTHOR]