Study of three-dimensional dose distribution prediction model in radiotherapy planning based on full convolution network
10.3760/cma.j.cn113030-20191018-00427
- VernacularTitle:全卷积网络放疗三维剂量分布预测研究
- Author:
Xue BAI
1
;
Shengye WANG
;
Binbing WANG
;
Jie ZHANG
;
Kainan SHAO
;
Yiwei YANG
;
Guoping SHAN
;
Ming CHEN
Author Information
1. 中国科学院大学附属肿瘤医院 浙江省肿瘤医院放射物理室，杭州 310022
- From:
Chinese Journal of Radiation Oncology
2020;29(8):666-670
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore a three-dimensional dose distribution prediction method for the left breast cancer radiotherapy planning based on full convolution network (FCN), and to evaluate the accuracy of the prediction model.Methods:FCN was utilized to achieve three-dimensional dose distribution prediction. First, a volumetric modulated arc therapy (VMAT) plan dataset with 60 cases of left breast cancer was built. Ten cases were randomly chosen from the dataset as the test set, and the remaining 50 cases were used as the training set. Then, a U-Net model was built with the organ structure matrix as inputs and dose distribution matrix as outputs. Finally, the model was adopted to predict the dose distribution of the cases in the test set, and the predicted 3D doses were compared with actual planned results.Results:The mean absolute differences of PTV, ipsilateral lung, heart, whole lung and spinal cord for 10 cases were (119.95±9.04) cGy, (214.02±9.04) cGy, (116.23±30.96) cGy, (127.67±69.19) cGy, and (37.28±18.66) cGy, respectively. The Dice similarity coefficient (DSC) of the prediction dose and the planned dose in the 80% and 100% prescription dose range were 0.92±0.01 and 0.92±0.01. The γ rate of 3 mm/3% in the area of 80% and 10% prescription dose range were 0.85±0.03 and 0.84±0.02. Conclusion:FCN can be used to predict the three-dimensional dose distribution of left breast cancer patients undergoing VMAT.
