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Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study
- Source :
- Journal of Medical Internet Research
- Publication Year :
- 2021
- Publisher :
- JMIR Publications, 2021.
-
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.
- Subjects :
- Computer science
neural network
Health Informatics
deep learning model
Tracking (particle physics)
Convolutional neural network
radiation therapy
Root mean square
Motion
Cyberknife
Neoplasms
dynamic tracking
Humans
Latency (engineering)
respiratory signal prediction
Original Paper
Artificial neural network
business.industry
Deep learning
Respiration
Pattern recognition
Memory model
Artificial intelligence
Neural Networks, Computer
business
temporal convolutional neural network
Subjects
Details
- Language :
- English
- ISSN :
- 14388871 and 14394456
- Volume :
- 23
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- Journal of Medical Internet Research
- Accession number :
- edsair.doi.dedup.....fc7be6ab3195d48eb0d4ac493ab3059d