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 R-values 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.