Multilayer perceptron neural network with regression and ranking loss for patient-specific quality assurance

Abstract

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.