  We propose a novel domain specific loss, which is a differentiable loss
function based on the dose volume histogram, and combine it with an adversarial
loss for the training of deep neural networks to generate Pareto optimal dose
distributions. The mean squared error (MSE) loss, dose volume histogram (DVH)
loss, and adversarial (ADV) loss were used to train 4 instances of the neural
network model: 1) MSE, 2) MSE+ADV, 3) MSE+DVH, and 4) MSE+DVH+ADV. 70 prostate
patients were acquired, and the dose influence arrays were calculated for each
patient. 1200 Pareto surface plans per patient were generated by
pseudo-randomizing the tradeoff weights (84,000 plans total). We divided the
data into 54 training, 6 validation, and 10 testing patients. Each model was
trained for 100,000 iterations, with a batch size of 2. The prediction time of
each model is 0.052 seconds. Quantitatively, the MSE+DVH+ADV model had the
lowest prediction error of 0.038 (conformation), 0.026 (homogeneity), 0.298
(R50), 1.65% (D95), 2.14% (D98), 2.43% (D99). The MSE model had the worst
prediction error of 0.134 (conformation), 0.041 (homogeneity), 0.520 (R50),
3.91% (D95), 4.33% (D98), 4.60% (D99). For both the mean dose PTV error and the
max dose PTV, Body, Bladder and rectum error, the MSE+DVH+ADV outperformed all
other models. All model's predictions have an average mean and max dose error
less than 2.8% and 4.2%, respectively. Expert human domain specific knowledge
can be the largest driver in the performance improvement, and adversarial
learning can be used to further capture nuanced features. The real-time
prediction capabilities allow for a physician to quickly navigate the tradeoff
space, and produce a dose distribution as a tangible endpoint for the
dosimetrist to use for planning. This can considerably reduce the treatment
planning time, allowing for clinicians to focus their efforts on challenging
cases.
