  Knowledge-based planning (KBP) is an automated approach to radiation therapy
treatment planning that involves predicting desirable treatment plans before
they are then corrected to deliverable ones. We propose a generative
adversarial network (GAN) approach for predicting desirable 3D dose
distributions that eschews the previous paradigms of site-specific feature
engineering and predicting low-dimensional representations of the plan.
Experiments on a dataset of oropharyngeal cancer patients show that our
approach significantly outperforms previous methods on several clinical
satisfaction criteria and similarity metrics.
