  With the advancement of treatment modalities in radiation therapy for cancer
patients, outcomes have improved, but at the cost of increased treatment plan
complexity and planning time. The accurate prediction of dose distributions
would alleviate this issue by guiding clinical plan optimization to save time
and maintain high quality plans. We have modified a convolutional deep network
model, U-net (originally designed for segmentation purposes), for predicting
dose from patient image contours of the planning target volume (PTV) and organs
at risk (OAR). We show that, as an example, we are able to accurately predict
the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer
patients, where the average Dice similarity coefficient is 0.91 when comparing
the predicted vs. true isodose volumes between 0% and 100% of the prescription
dose. The average value of the absolute differences in [max, mean] dose is
found to be under 5% of the prescription dose, specifically for each structure
is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%,
1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%,
1.62%](Rectum) of the prescription dose. We thus managed to map a desired
radiation dose distribution from a patient's PTV and OAR contours. As an
additional advantage, relatively little data was used in the techniques and
models described in this paper.
