Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients With Asymmetric Network

作者：Yan Shao, Xiaoying Zhang, Ge Wu, Qingtao Gu, Jiyong Wang, Yanchen Ying, Aihui Feng, Guotong Xie, Qing Kong, Zhiyong Xu · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2020 · DOI：10.1109/jbhi.2020.3025712 · 被引用次数：43 · 研究领域：Advanced Radiotherapy Techniques、Radiomics and Machine Learning in Medical Imaging、Medical Imaging Techniques and Applications

The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy. In A-Net, the encoder and decoder are asymmetric, able to preserve input information and to adapt the limitation of GPU memory. Squeeze and excitation (SE) units are used to improve the data-fitting ability. A loss function involving both the dose distribution and prescription dose as ground truth are designed. In the experiment, A-Net is separately trained and tested in the 50Gy and 60Gy dataset and most of the metrics A-Net achieve similar performance as HD-Unet and 3D-Unet, and some metrics slightly better. In the 50Gy-and-60Gy-combined dataset, most of the A-Net's metrics perform better than the other two. In conclusion, A-Net can accurately predict the IMRT dose distribution in the three datasets of 50Gy and 50Gy-and-60Gy-combined dataset.