Region-specific three-dimensional dose distribution prediction: a feasibility study on prostate VMAT cases

To investigate region-specific models for organ’s three-dimensional dose distribution prediction with neural network. The dose distribution from different bladder regions for 52 prostate volumetric modulated arc therapy cases were first analyzed, the two region-specific models were then built to predict the bladder dose distribution, the initial model and the refined model. For the initial model, the bladder was divided into overlapping region and nonoverlapping region, two artificial neural networks were established with each one corresponding to one region. For the refined model, the nonoverlapping region was further divided into three subregions, and four artificial neural network models were built in total. For each artificial neural network model, several spatial and volumetric features for the bladder were extracted as the input to the neural network. To investigate the feasibility and dose distribution prediction accuracy of the proposed two region-specific models, the mean absolute error, gamma passing rate, dose volume histogram, and dose distribution for the refined model were compared. According to the predicted dose from the initial model and the refined model, the average mean absolute error for all cases is reduced from 5.03 Gy in the initial model to 3.23 Gy in the refined model, the refined model reduce the mean absolute error about 2% relative to the prescription dose. The average area deviation of predicted dose volume histogram by the refined model is 5%, and the average gamma passing rate is 82% and 94% with the 3 mm/3% and 5 mm/5% criteria, which shows that the refined model proposed in this study has high dose-prediction accuracy. Two region-specific three-dimensional dose distribution prediction models for volumetric modulated arc therapy prostate cases based on neural network have been investigated, the models have shown that a more refined consideration of structures improved the accuracy of predicted dose distribution.