  Typically, the current dose prediction models are limited to small amounts of
data and require re-training for a specific site, often leading to suboptimal
performance. We propose a site-agnostic, 3D dose distribution prediction model
using deep learning that can leverage data from any treatment site, thus
increasing the total data available to train the model. Applying our proposed
model to a new target treatment site requires only a brief fine-tuning of the
model to the new data and involves no modifications to the model input channels
or its parameters. Thus, it can be efficiently adapted to a different treatment
site, even with a small training dataset.
