  We demonstrate the application of mixture density networks (MDNs) in the
context of automated radiation therapy treatment planning. It is shown that an
MDN can produce good predictions of dose distributions as well as reflect
uncertain decision making associated with inherently conflicting clinical
tradeoffs, in contrast to deterministic methods previously investigated in
literature. A two-component Gaussian MDN is trained on a set of treatment plans
for postoperative prostate patients with varying extents to which rectum dose
sparing was prioritized over target coverage. Examination on a test set of
patients shows that the predicted modes follow their respective ground truths
well both spatially and in terms of their dose-volume histograms. A special
dose mimicking method based on the MDN output is used to produce deliverable
plans and thereby showcase the usability of voxel-wise predictive densities.
Thus, this type of MDN may serve to support clinicians in managing clinical
tradeoffs and has the potential to improve quality of plans produced by an
automated treatment planning pipeline.
