Uninvolved liver dose prediction in stereotactic body radiation therapy for liver cancer based on the neural network method

BACKGROUND The quality of a radiotherapy plan often depends on the knowledge and expertise of the plan designers.AIM To predict the uninvolved liver dose in stereotactic body radiotherapy(SBRT)for liver cancer using a neural network-based method.METHODS A total of 114 SBRT plans for liver cancer were used to test the neural network method.Sub-organs of the uninvolved liver were automatically generated.Correlations between the volume of each sub-organ,uninvolved liver dose,and neural network prediction model were established using MATLAB.Of the cases,70%were selected as the training set,15%as the validation set,and 15%as the test set.The regression R-value and mean square error(MSE)were used to evaluate the model.RESULTS The volume of the uninvolved liver was related to the volume of the corresponding sub-organs.For all sets of Rvalues of the prediction model,except for D_(n0)which was 0.7513,all R-values of D_(n10)-D_(n100)and D_(nmean)were>0.8.The MSE of the prediction model was also low.CONCLUSION We developed a neural network-based method to predict the uninvolved liver dose in SBRT for liver cancer.It is simple and easy to use and warrants further promotion and application.