In Medical physics ; h5-index 59.0
BACKGROUND : Weak correlation between gamma passing rates (GPR) and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose-volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient-specific quality assurance (PSQA). However, it difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly.
PURPOSE : To develop a DVH-based pre-treatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning (ML) models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH-based PSQA.
METHODS : A DL model with a 3D squeeze-and-excitation residual blocks incorporated into a modified U-net was developed to predict the measured PSQA DVHs of 208 head and neck (H&N) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training.
RESULTS : The prediction model was able to achieve a good agreement between predicted, measured and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for Dmean of PTV6900 (p = 0.001), D50 of PTV6000 (p = 0.014), D2 of PTV5400 (p = 0.009), D50 of left parotid (p = 0.015) and Dmax of left inner ear (p = 0.007). The XGBoost model achieved an area under curves (AUC), accuracy, sensitivity and specificity of 0.89 vs. 0.88, 0.89 vs. 0.86, 0. 71 vs. 0.71, and 0.95 vs. 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases.
CONCLUSIONS : The successful prediction of PSQA doses and classification of PoF for H&N VMAT PSQA indicating that this DVH-based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery. This article is protected by copyright. All rights reserved.
Gong Changfei, Zhu Kecheng, Lin Chengyin, Han Ce, Lu Zhongjie, Chen Yuanhua, Yu Changhui, Hou Liqiao, Zhou Yongqiang, Yi Jinling, Ai Yao, Xiang Xiaojun, Xie Congying, Jin Xiance
Dose-volume histogram, Patient-specific quality assurance, Volumetric modulated arc therapy, deep convolutional neural networks, machine learning