Using a neural network to predict deviations in mean heart dose during the treatment of left-sided deep inspiration breath hold patients.

PURPOSE: We investigated if a neural network could be used to predict the change in mean heart dose when a patient's heart deviates from its planned position during radiotherapy treatment. METHODS: Predictions were made based on parameters available at the time of treatment planning. The dose prescription, deep inspiration breath-hold (DIBH) amplitude, heart volume, lung volume, V90% and mean heart dose were used to predict the increase in dose to the heart when a shift towards the treatment field was undertaken. The network was trained using 3 mm, 5 mm and 7 mm shifts in heart positions for 50 patients' giving 150 data points in total. The neural network architecture was also varied to find the most optimal network design. The final neural network was then tested using cross-validation to evaluate the model's ability to generalise to new data. RESULTS: The optimal neural network found was comprised of a single hidden layer of 30 neurons. Based on twenty train/test splits, 94% of all prediction errors were below 0.2 Gy, 97.3% were below 0.3 Gy and 100% were below 0.5 Gy. The average RMSE and maximum prediction error over all train/test splits were 0.13 Gy and 0.5 Gy respectively. CONCLUSIONS: Our approach using a neural network provides a clinically acceptable estimate of the increase in Mean Heart Dose (MHD), without the need for further imaging, contouring or evaluation. The trained neural network gives clinicians the information and tools required to evaluate what shift in heart position would be acceptable and which scenarios require immediate action before treatment continues.