  Radiation therapy of thoracic and abdominal tumors requires incorporating the
respiratory motion into treatments. To precisely account for the patient
respiratory motions and predict the respiratory signals, a generalized model
for predictions of different types of respiratory motions is desired. The aim
of this study is to explore the feasibility of developing a Long Short-Term
Memory (LSTM)-based model for the respiratory signal prediction. To achieve
that, 1703 sets of Real-Time Position Management data were collected from
retrospective studies across three clinical institutions. These datasets were
separated as the training, internal validity and external validity groups. 1187
datasets were used for model development and the remaining 516 datasets were
used to test the generality power of the model. Furthermore, an exhaustive grid
search was implemented to find the optimal hyper-parameters of the LSTM model.
The hyper-parameters are the number of LSTM layers, the number of hidden units,
the optimizer, the learning rate, the number of epochs, and the length of time
lags. Our model achieved superior accuracy over conventional artificial neural
network models: with a prediction window of 500ms, the LSTM model achieved an
average relative Mean Absolute Error (MAE) of 0.037, an average Root Mean
Square Error (RMSE) of 0.048, and a Maximum Error (ME) of 1.687 in the internal
validity data, and an average relative MAE of 0.112, an average RMSE of 0.139
and an ME of 1.811 in the external validity data. Compared to the LSTM model
trained with default hyper-parameters, the MAE of the optimized model results
decreased by 20%, indicating the importance of tuning the hyper-parameters of
LSTM models to obtain superior accuracy. This study demonstrates the potential
of deep LSTM models for the respiratory signal prediction and illustrates the
impacts of major hyper-parameters in LSTM models.
