  In this work, we propose a Machine Learning model that generates an
adjustable 3D dose distribution for external beam radiation therapy for
head-and-neck cancer treatments. In contrast to existing Machine Learning
methods that provide a single model, we create pairs of models for each
organ-at-risk, namely lower-extreme and upper-extreme models. These model pairs
for an organ-at-risk propose doses that give lower and higher doses to that
organ-at-risk, while also encapsulating the dose trade-off to other
organs-at-risk. By weighting and combining the model pairs for all
organs-at-risk, we are able to dynamically create adjustable dose distributions
that can be used, in real-time, to move doses between organs-at-risk, thereby
customizing the dose distribution to the needs of a particular patient. We
leverage a key observation that the training data set inherently contains the
clinical trade-offs. We show that the adjustable distributions are able to
provide reasonable clinical dose latitude in the trade-off of doses between
organs-at-risk.
