What question did this study set out to answer?

The study aims to evaluate the effectiveness of a hybrid deep-learning model for predicting internal organ motion during radiotherapy, particularly under varying breathing conditions.

April 17, 2026Open Access

A Feasibility Study of Hybrid Deep-Learning Prediction with Online Adaptation of Breathing Irregularities for Long-Term Internal Organ Motion During Radiotherapy

Puntos clave

The study aims to evaluate the effectiveness of a hybrid deep-learning model for predicting internal organ motion during radiotherapy, particularly under varying breathing conditions.
Collected respiratory motion waveforms from ten volunteers during two 4DMRI scans.
Developed subject-specific LSTM models trained on mid-term data to predict internal waveforms.
Assessed prediction accuracy using Pearson's correlation and enhanced predictions through a hybrid LSTM-TCC approach.
Evaluated model performance using root mean square error and area under the receiver operating characteristic curve.
LSTM models demonstrated improved prediction accuracy (C = 0.89 ± 0.07) over native waveforms and TCC method.
LSTM-TCC successfully maintained accuracy even when LSTM predictions faltered in some subjects.
Temporal error in predicting inspiratory peaks was smaller for LSTM-TCC compared to LSTM.

Resumen

IntroductionAlthough long short-term memory (LSTM) networks have been tested to predict short-term respiratory motion, their performance in long-term forecasting under breathing irregularities must be assessed. We aim to evaluate and enhance the long-term prediction of internal motion from an external surrogate using subject-specific LSTM models through a hybrid, adaptive approach.MethodsConcurrent internal navigator and external bellows respiratory-motion waveforms were acquired for ten volunteers during two four-dimensional magnetic resonance imaging (4DMRI) scans lasting 3-10 min each. Approximately 20 min intervened between the first (mid-term) and second (long-term) scan. After training on the first half of the mid-term data, subject-specific LSTM models were applied to the remaining mid-term and entire long-term datasets to predict internal waveforms. The accuracy of a model's prediction was assessed with Pearson's correlation (C), referenced to the native waveforms, maximized through the time-domain cross-correlation (TCC), and enhanced by correcting residual phase shifts in the LSTM models using a hybrid (LSTM-TCC) approach. Hyperparameter selection by minimizing the root mean square error (RMSE) to identify high-performance (C ≥ 0.8) LSTM models was evaluated by the area under the receiver operating characteristic curve (AUROC). The temporal accuracy of inspiratory-peak predictions was characterized.ResultsCompared to the native waveforms (C = 0.42 ± 0.28) and TCC method (C = 0.77 ± 0.09), the LSTM models yielded more accurate predictions (C = 0.89 ± 0.07) in the mid-term scans. Over 20-30 min, LSTM predictions faltered (C C = 0.90 ± 0.09). The temporal error in predicting inspiratory peaks was smaller for LSTM-TCC (Δt = 0.15 ± 0.11sec) than LSTM (Δt = 0.18 ± 0.15sec). RMSE reliably identified high-performance models: AUROCLSTMmid-term=0.82, AUROCLSTMlong-term=0.74, and AUROChybridlong-term=0.83.ConclusionThe feasibility of a novel adaptive subject-specific LSTM-TCC modeling was tested in 10 subjects, demonstrating that high accuracy of external-to-internal motion predictions in 3-10 min can be extended to 30 min overcoming breathing irregularities without remodeling. Further investigations of the adaptive LSTM-TCC model are warranted as a potential clinical solution.

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