@Article{info:doi/10.2196/27235,
author="Chang, Panchun
and Dang, Jun
and Dai, Jianrong
and Sun, Wenzheng",
title="Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study",
journal="J Med Internet Res",
year="2021",
month="Aug",
day="27",
volume="23",
number="8",
pages="e27235",
keywords="radiation therapy; temporal convolutional neural network; respiratory signal prediction; neural network; deep learning model; dynamic tracking",
abstract="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. ",
issn="1438-8871",
doi="10.2196/27235",
url="https://www.jmir.org/2021/8/e27235",
url="https://doi.org/10.2196/27235",
url="http://www.ncbi.nlm.nih.gov/pubmed/34236336"
}