Abstract
The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose–volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.
Highlights
In recent decades, the quality of radiotherapy, such as intensitymodulated radiation therapy (IMRT) and volumetric-modulated arc therapy, has been greatly improved by inverse planning; these highquality radiotherapy treatment techniques can prescribe a sufficiently high dose to the target while sparing normal tissues [1–5]
In each patient, the achievable dose–volume histogram (DVH) is unknown at the time of optimization, and the dose constraints commonly use recommended values from previous studies, such as the quantitative analyses of normal tissue effects in the clinic (QUANTEC) guidelines [8], in which tolerance doses were defined by population data
To reduce dependence on hand-crafted features, we investigated a convolutional neural network (CNN) approach that specializes in image processing based on deep learning
Summary
The quality of radiotherapy, such as intensitymodulated radiation therapy (IMRT) and volumetric-modulated arc therapy, has been greatly improved by inverse planning; these highquality radiotherapy treatment techniques can prescribe a sufficiently high dose to the target while sparing normal tissues [1–5].
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