[en] 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. (authors)