Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files
| Autor: | Ying Huang MS, Yifei Pi PhD, Kui Ma, Xiaojuan Miao, Sichao Fu ME, Zhen Zhu, Yifan Cheng, Zhepei Zhang, Hua Chen MS, Hao Wang MS, Hengle Gu MS, Yan Shao MS, Yanhua Duan ME, Aihui Feng MS, Weihai Zhuo PhD, Zhiyong Xu PhD |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Technology in Cancer Research & Treatment, Vol 21 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1533-0338 15330338 |
| DOI: | 10.1177/15330338221104881 |
| Popis: | Objectives: In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. Methods: A total of 112 IMRT plans for chest cancers were planned and measured by portal dosimetry equipped on TrueBeam linac. The convolutional neural network (CNN) based learning model was trained using delivery fluence as inputs and gamma passing rates (GPRs) of 4 different criteria (3%/3 mm, 2%/3 mm, 3%/2 mm, and 2%/2 mm) as outputs. Model performance for both validation and test sets was assessed using mean absolute error (MAE), mean squared error (MSE), root MSE (RMSE), Spearman rank correlation coefficients (Sr), and Determination coefficient ( R 2 ) between the measured and predicted GPR values. Results: In the test set, the MAE of the prediction model were 0.402, 0.511, 1.724, and 2.530, the MSE were 0.640, 0.986, 6.654, and 9.508, the RMSE were 0.800, 0.993, 2.580, and 3.083, the Sr were 0.643, 0.684, 0.821, and 0.824 ( P |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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