Department of Computer Science and Technology, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan, P. R. China.
Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China.
Int J Neural Syst. 2022 Jan;32(1):2150055. doi: 10.1142/S0129065721500556.
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.
患者特异性质量保证（QA）对于容积旋转调强放射治疗（VMAT）计划在临床中是常规进行的。然而，对于医学物理学家来说，这是一项劳动密集型且耗时的工作。QA 预测模型可以解决这些缺点并提高效率。目前的方法主要集中在单一癌症和单一模式数据上。它们不适用于临床实践。为了评估 VMAT 计划 QA 结果的准确性，本文提出了一种新的模型，该模型从多模态数据中学习互补特征，以预测伽马通过率（GPR）。根据 VMAT 计划的特点，设计了一种特征-数据融合方法，以在模型中融合成像和非成像信息的特征。在这项研究中，收集了 690 个涵盖超过十种疾病的 VMAT 计划。该模型可以准确地预测所有三个伽马标准（2%/2mm、3%/2mm 和 3%/3mm）的大多数 VMAT 计划。预测的和测量的 GPR 之间的平均绝对误差分别为 2.17%、1.16%和 0.71%。预测的和测量的 GPR 之间的最大偏差分别为 3.46%、4.6%和 8.56%。所提出的模型是有效的，两种模式的特征对 QA 结果有显著影响。
