  This paper surveys the data-driven dose prediction approaches introduced for
knowledge-based planning (KBP) in the last decade. These methods were
classified into two major categories according to their methods and techniques
of utilizing previous knowledge: traditional KBP methods and
deep-learning-based methods. Previous studies that required geometric or
anatomical features to either find the best matched case(s) from repository of
previously delivered treatment plans or build prediction models were included
in traditional methods category, whereas deep-learning-based methods included
studies that trained neural networks to make dose prediction. A comprehensive
review of each category is presented, highlighting key parameters, methods, and
their outlooks in terms of dose prediction over the years. We separated the
cited works according to the framework and cancer site in each category.
Finally, we briefly discuss the performance of both traditional KBP methods and
deep-learning-based methods, and future trends of both data-driven KBP
approaches.
