Feasibility study on liver tumor motion prediction based on back propagation neural network

Objective This study was performed to determine the feasibility of liver tumor motion prediction based on back propagation(BP) neural network.

Methods A liver cancer patient was scanned using X-ray volume imaging, and all breath motion figures were recorded.The tumor was located using an iodized oil mark.The mark motion track was gathered through image processing.A BP model was established based on the marked track.This model was used for tumor prediction.The results were compared with the true mark track.

Results Accurate prediction of liver tumor was achieved via BP neural network, with a deviation of less than 1 pixel.However, the predicted value was less accurate at the peak of the breath motion curve, with a deviation of less than 2 pixels.

Conclusions BP neural network is proposed as a new approach for liver tumor motion prediction.This network is beneficial to enhance the accuracy of liver stereotactic body radiation therapy and real-time adaptive radiation therapy.The proposed approach could be applied clinically.