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Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT
Lukas Wimmert1,2,3, Maximilian Nielsen1,2,3, Frederic Madesta1,2,3
1Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Medical Physics
|March 27, 2024
Summary
The Lstm model best predicts respiratory motion for lung cancer radiotherapy, showing minimal error even with longer prediction times. It also performs better on unusual breathing patterns compared to simpler models.
Area of Science:
- Medical Physics
- Radiotherapy
- Machine Learning
Background:
- Respiratory motion significantly impacts thoracic and abdominal tumor radiotherapy.
- External breathing signals are used as surrogates for tumor motion, but system latencies cause temporal lags.
- Respiratory signal prediction models are crucial for improving imaging and dose delivery by compensating for time delays.
Purpose of the Study:
- To compare six state-of-the-art machine and deep learning models for respiratory signal prediction.
- To evaluate real-time and real-world applicability of these models.
- To ensure reproducibility by providing open-source models and data.
Main Methods:
- Utilized 2502 clinical breathing signals for training, validation, and testing.
- Compared Linear, Dlinear, Xgboost, Lstm, Trans-Enc, and Trans-TSF models.
- Assessed prediction performance across 480, 680, and 920 ms horizons and analyzed robustness on out-of-distribution (OOD) signals.
Main Results:
- Lstm model demonstrated the lowest prediction errors across all horizons.
- Prediction accuracy decreased for out-of-distribution (OOD) signals, with Lstm and Trans-Enc showing less performance loss.
- Most models, except Trans-Enc, exhibited inference times suitable for real-time application.
Conclusions:
- The Lstm model offers the most accurate respiratory signal prediction.
- Simpler prediction models struggle with OOD signals due to limited signal history access.
