Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction
| العنوان: | Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction |
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| المؤلفون: | Aihui Feng, Yan Shao, Hua Chen, Hao Wang, Yanhua Duan, Xiaojuan Miao, Kui Ma, Jiyong Wang, Sichao Fu, Hengle Gu, Ying Huang, Yifei Pi, Ruxin Cai, Zhiyong Xu, Weihai Zhuo |
| المصدر: | Frontiers in Oncology, Vol 11 (2021) Frontiers in Oncology |
| بيانات النشر: | Frontiers Media S.A., 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Cancer Research, Mean squared error, Dose distribution, quality assurance, 030218 nuclear medicine & medical imaging, Medical physicist, 03 medical and health sciences, 0302 clinical medicine, Virtual patient, Dosimetry, radiotherapy, RC254-282, Original Research, Mathematics, business.industry, deep learning, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, dose difference, Patient specific, prediction model, Oncology, 030220 oncology & carcinogenesis, Test set, Nuclear medicine, business, Quality assurance |
| الوصف: | The dose verification in radiotherapy quality assurance (QA) is time-consuming and places a heavy workload on medical physicists. To provide a clinical tool to perform patient specific QA accurately, the UNet++ is investigated to classify failed or pass fields (the GPR lower than 85% is considered “failed” while the GPR higher than 85% is considered “pass”), predict gamma passing rates (GPR) for different gamma criteria, and predict dose difference from virtual patient-specific quality assurance in radiotherapy. UNet++ was trained and validated with 473 fields and tested with 95 fields. All plans used Portal Dosimetry for dose verification pre-treatment. Planar dose distribution of each field was used as the input for UNet++, with QA classification results, gamma passing rates of different gamma criteria, and dose difference were used as the output. In the test set, the accuracy of the classification model was 95.79%. The mean absolute error (MAE) were 0.82, 0.88, 2.11, 2.52, and the root mean squared error (RMSE) were 1.38, 1.57, 3.33, 3.72 for 3%/3mm, 3%/2 mm, 2%/3 mm, 2%/2 mm, respectively. The trend and position of the predicted dose difference were consistent with the measured dose difference. In conclusion, the Virtual QA based on UNet++ can be used to classify the field passed or not, predict gamma pass rate for different gamma criteria, and predict dose difference. The results show that UNet++ based Virtual QA is promising in quality assurance for radiotherapy. |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d49bb6dfe51d8266cedf46aa65446a1 https://www.frontiersin.org/articles/10.3389/fonc.2021.700343/full |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....3d49bb6dfe51d8266cedf46aa65446a1 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |