Affiliation:
1. Radiation Oncology Department The Seventh Medical Center of Chinese PLA General Hospital Beijing China
2. School of Automation Science and Electrical Engineering Beihang University Beijing China
Abstract
AbstractBackgroundThe current level of automation in the production of radiotherapy plans for lung cancer patients is relatively low. With the development of artificial intelligence, it has become a reality to use neural networks to predict dose distributions and provide assistance for radiation therapy planning. However, due to the significant individual variability in the distribution of non‐small cell lung cancer (NSCLC) planning target volume (PTV) and the complex spatial relationships between the PTV and organs at risk (OARs), there is still a lack of a high‐precision dose prediction network tailored to the characteristics of NSCLC.PurposeTo assist in the development of volumetric modulated arc therapy (VMAT) plans for non‐small cell lung cancer patients, a deep neural network is proposed to predict high‐precision dose distribution.MethodsThis study has developed a network called MHA‐ResUNet, which combines a large‐kernel dilated convolution module and multi‐head attention (MHA) modules. The network was trained based on 80 VMAT plans of NSCLC patients. CT images, PTV, and 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.ResultsThe MAE between the predicted dose distribution and the manually planned dose distribution within the PTV is 1.43 Gy, and the D95 error is less than 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA‐ResUNet is the smallest in PTV and OARs.ConclusionsThe proposed MHA‐ResUNet network improves the receptive field and filters the shallow features to learn the relative spatial relation between the PTV and the OARs, enabling accurate prediction of dose distributions in NSCLC patients undergoing VMAT radiotherapy.