Sangwoon Jeong, Wonjoong Cheon, Sungkoo Cho, Youngyih Han
Abstract
For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated. Among the 540 respiration signals, 60 signals are used as test data. Each of the remaining 480 signals was spilt into training and validation data in a 7:3 ratio. A total of 1000 ms of the signal sequence (Ts) is entered to the models, and the signal at 500 ms afterward (Pt) is predicted (standard training condition).
Introduction
Radiation therapy can achieve high dose conformity with intensity-modulated radiation therapy and particle therapy, which can deliver a prescription dose to the target volume while minimizing undesirable doses near critical organs [1, 2]. However, respiratory movements of the patient can result in the administration of undesired doses to the target volume and nearby organs at risk (OARs) [3–5]. Several studies have shown that patient respiration causes organ movements up to 40.0, 39.0, 23.0, and 10.0 mm for the liver, pancreas, kidney, and prostate, respectively [6, 7].
To reduce the dose delivery uncertainty associated with patient respiration during radiation treatment, various methods and technologies, such as deep inspiration breath-hold (DIBH), chest compression, real-time tracking, and respiratory gating, have been introduced.
Materials and methods
The data used in this study consisted of 540 respiration signals obtained from 442 patients, who received proton therapy for liver, lung, and breast cancer treatments. The patients took CT simulation with guided free-breathing and were trained in advance by a medical physicist to ensure regular respiratory signals could be obtained using an in-house developed respiration guiding system.
Results
The prediction accuracy of the respiratory signal was calculated for LSTM, Bi-LSTM, and the Transformer. The results were obtained under the standard conditions of Ts = 1000 ms and Pt = 500 ms. Examples of the prediction results of LSTM, Bi-LSTM, and the Transformer are shown in Fig 3. The averaged RMSE and CC of the validation and test sets for the three different deep learning models are summarized in Table 1. In the test set for the LSTM, Bi-LSTM, and the Transformer, the RMSEs were 0.1907, 0.1930, and 0.1554, and the CCs were 0.9689, 0.9661, and 0.9768, respectively.
Discussion
For precise 4DRT with a respiratory-gated system, a model that can compensate for the latency of the beam delivery system is essential. Thus, we compared the respiratory signal prediction performance of three deep learning-based prediction models: LSTM, Bi-LSTM, and Transformer. The Transformer achieved the best prediction accuracy under standard conditions in both the validation and test sets. In the test set, the performances of the LSTM, Bi-LSTM, and Transformer had CC values of 0.9689, 0.9661, and 0.9768, respectively.
Additionally, we analyzed the effects of Ts and Pt on the prediction accuracy because the beam on/off latency differs depending on manufacturer, motion detection device, and linac model.
Conclusion
We successfully evaluated the clinical feasibility of LSTM, Bi-LSTM, and Transformer models for respiratory signal prediction. Among the deep learning-based models, the Transformer model was superior to the LSTM and Bi-LSTM models. Prediction accuracy was found to be affected by the training data length and the time distance to the prediction, and was considerably affected by irregular amplitude patterns.
Citation: Jeong S, Cheon W, Cho S, Han Y (2022) Clinical applicability of deep learning-based respiratory signal prediction models for four-dimensional radiation therapy. PLoS ONE 17(10): e0275719. https://doi.org/10.1371/journal.pone.0275719
Academic Editor: Ngie Min Ung, University of Malaya, MALAYSIA
Received: February 24, 2022; Accepted: September 21, 2022; Published: October 18, 2022
Copyright: © 2022 Jeong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the results presented in the study are available from GitHub (https://github.com/SangWoonJeong/Respiratory-prediction).
Funding: This work was supported by National Research Foundation of Korea (NRF) and the Korean government (MSIT) (NRF-2019R1F1A1062775 and 2019M2A2B4096537) Receiver of the funds: YH URL: https://www.nrf.re.kr/eng/index The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
