Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study (Preprint)

Abstract

BACKGROUND Dynamic tracking of tumor with radiation beam in radiation therapy requires prediction of real-time target location ahead of beam delivery as the treatment with beam or gating tracking brings in time latency. OBJECTIVE A deep learning model based on a temporal convolutional neural network (TCN) using multiple external makers was developed to predict internal target location through multiple external markers in this study. METHODS The respiratory signals from 69 treatment fractions of 21 cancer patients treated with the Cyberknife Synchrony device were used to train and test the model. The reported model’s performance was evaluated through comparing with a long short term memory model in terms of root-mean-square-error (RMSE) between real and predicted respiratory signals. Besides, the effect of external marker number was also investigated. RESULTS The average RMSEs (mm) for 480-ms ahead of prediction using TCN model in the superior–inferior (SI), anterior–posterior (AP) and left–right (LR) and radial directions were 0.49, 0.28, 0.25 and 0.67, respectively. CONCLUSIONS The experiment results demonstrated that the TCN respiratory prediction model could predict the respiratory signals with sub-millimeter accuracy.