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...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.展开更多
基金Supported by the Open Fund for Scientific Research of Jiangxi Cancer Hospital,No.2021J15the Gulin People's Hospital-The Affiliated Hospital of Southwest Medical University Science and Technology Strategic Cooperation Project,No.2022GLXNYDFY05the Sichuan Provincial Medical Research Project Plan,No.S21004.
文摘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.
