| Luo Rui,Liu Mingzhe,Wen Aiping,et al.Dose distribution prediction in cervical cancer brachytherapy based on 3D U-net[J].Chinese Journal of Radiological Medicine and Protection,2022,42(8):611-617 |
| Dose distribution prediction in cervical cancer brachytherapy based on 3D U-net |
| Received:March 05, 2022 |
| DOI：10.3760/cma.j.cn112271-20220305-00085 |
| KeyWords:Cervical cancer Brachytherapy Dose prediction Dose distribution 3D U-net |
| FundProject:四川省重点研发项目（2022YFG0194）；成都市科技局技术创新研发项目（2021-YF05-02107-SN）；四川省科技计划资助项目（2021YFG0320）；成都市科技局重点研发支撑计划（2019-YF09-00095-SN） |
| Author Name | Affiliation | E-mail | | Luo Rui | The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 614000, China | | | Liu Mingzhe | The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 614000, China | | | Wen Aiping | Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China | | | Yan Chuanjun | Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China | | | Luo Jingyue | Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China | | | Wang Pei | Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China | | | Li Jie | Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China | | | Wang Xianliang | Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, China | wangliu8687@163.com |
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| Abstract:: |
| Objective To establish a three-dimensional (3D) U-net-based deep learning model, and to predict the 3D dose distribution in CT-guided cervical cancer brachytherapy by using the established model.Methods The brachytherapy plans of 114 cervical cancer cases with a prescription dose of 6 Gy for each case were studied. These cases were divided into training, validation, and testing groups, including 84, 11, and 19 patients, respectively. A total of 500 epochs of training were performed by using a 3D U-net model. Then, the dosimetric parameters of the testing groups were individually evaluated, including the mean dose deviation (MDD) and mean absolute dose deviation (MADD) at the voxel level, the Dice similarity coefficient (DSC) of the volumes enclosed by isodose surfaces, the conformal index (CI) of the prescription dose, the D90 and average dose Dmean delivered to high-risk clinical target volumes (HR-CTVs), and the D1cm3 and D2cm3 delivered to bladders, recta, intestines, and colons, respectively.Results The overall MDD and MADD of the 3D dose matrix from 19 cases of the testing group were (-0.01 ±0.03) and (0.04 ±0.01) Gy, respectively. The CI of the prescription dose was 0.70 ±0.04. The DSC of 50%-150% prescription dose was 0.89-0.94. The mean deviation of D90 and Dmean to HR-CTVs were 2.22% and -4.30%, respectively. The maximum deviations of the D1cm3 and D2cm3 to bladders, recta, intestines, and colons were 2.46% and 2.58%, respectively. The 3D U-net deep learning model took 2.5 s on average to predict a patient's dose.Conclusions In this study, a 3D U-net-based deep learning model for predicting 3D dose distribution in the treatment of cervical cancer was established, thus laying a foundation for the automatic design of cervical cancer brachytherapy. |
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