TY  - JOUR
AU  - Chang, Panchun
AU  - Dang, Jun
AU  - Dai, Jianrong
AU  - Sun, Wenzheng
PY  - 2021
DA  - 2021/8/27
TI  - Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study
JO  - J Med Internet Res
SP  - e27235
VL  - 23
IS  - 8
KW  - radiation therapy
KW  - temporal convolutional neural network
KW  - respiratory signal prediction
KW  - neural network
KW  - deep learning model
KW  - dynamic tracking
AB  - Background: The dynamic tracking of tumors with radiation beams in radiation therapy requires the prediction of real-time target locations prior to beam delivery, as treatment involving radiation beams and gating tracking results in time latency. Objective: In this study, a deep learning model that was based on a temporal convolutional neural network was developed to predict internal target locations by using multiple external markers. Methods: Respiratory signals from 69 treatment fractions of 21 patients with cancer who were treated with the CyberKnife Synchrony device (Accuray Incorporated) were used to train and test the model. The reported model’s performance was evaluated by comparing the model to a long short-term memory model in terms of the root mean square errors (RMSEs) of real and predicted respiratory signals. The effect of the number of external markers was also investigated. Results: The average RMSEs of predicted (ahead time=400 ms) respiratory motion in the superior-inferior, anterior-posterior, and left-right directions and in 3D space were 0.49 mm, 0.28 mm, 0.25 mm, and 0.67 mm, respectively. Conclusions: The experiment results demonstrated that the temporal convolutional neural network–based respiratory prediction model could predict respiratory signals with submillimeter accuracy. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/8/e27235
UR  - https://doi.org/10.2196/27235
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34236336
DO  - 10.2196/27235
ID  - info:doi/10.2196/27235
ER  -