Prediction of dose distribution for VMAT radiotherapy in non-small cell lung cancer patients using MHA-resunet
10.3760/cma.j.cn112271-20231022-00132
- VernacularTitle:基于MHA-resunet神经网络的非小细胞肺癌VMAT放疗剂量分布预测研究
- Author:
Haifeng ZHANG
1
;
Yanjun YU
;
Fuli ZHANG
Author Information
1. 中国人民解放军总医院第七医学中心放疗科，北京 100700
- Keywords:
Non-small cell lung cancer;
Dose prediction;
Neural network;
Multi-head attention
- From:
Chinese Journal of Radiological Medicine and Protection
2024;44(6):523-530
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To apply deep neural networks to predict high-precision dose distribution in volume modulated arc therapy (VMAT) plans for non-small cell lung cancer (NSCLC) patients.Methods:This study developed a U-shaped network called MHA-resunet, which incorporated a large kernel dilated convolution module and a multi-head attention module. The model was trained from 151 VMAT plans of NSCLC patients. CT images, planning target volume (PTV) and organs at risk (OARs) were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks′performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics.Results:The MAE between the dose distribution predicted by MHA-resunet network and the manually planned dose distribution within the PTV area was 1.51 Gy, and the D98 and D95 errors in the PTV area were both < 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA-resunet was the smallest in the PTV area and in OARs except for the heart. Conclusions:The proposed MHA-resunet network improves the receptive field to learn the relative spatial relationship between the PTV area and the OARs, enabling accurate prediction of dose distribution in NSCLC patients undergoing VMAT radiotherapy.
