Efficient dose–volume histogram–based pretreatment patient‐specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy. Issue 12 (17th October 2022)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Medical physics. Volume 49:Issue 12(2022)
• Page Start: 7779
• Page End: 7790
• Publication Date: 2022-10-17
• Subjects: deep convolutional neural networks -- dose–volume histogram -- machine learning -- patient‐specific quality assurance -- volumetric modulated arc therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16010
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Weak correlation between gamma passing rates 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 is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly. Purpose: To develop a DVH‐based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH‐based PSQA. Methods: A DL model with a three‐dimensional 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 D mean of PTV6900 ( p = 0.001), D 50 of PTV6000 ( p = 0.014), D 2 of PTV5400 ( p = 0.009), D 50 of left parotid ( p = 0.015), and D max of left inner ear ( p = 0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 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.