Open AccessKnowledge‐based radiation treatment planning: A data‐driven method survey
Author(s) -
Momin Shadab,
Fu Yabo,
Lei Yang,
Roper Justin,
Bradley Jeffrey D.,
Curran Walter J.,
Liu Tian,
Yang Xiaofeng
Publication year - 2021
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1002/acm2.13337
Subject(s) - computer science , artificial intelligence , radiation treatment planning , artificial neural network , key (lock) , machine learning , data mining , radiation therapy , medicine , radiology , computer security
This paper surveys the data‐driven dose prediction methods investigated for knowledge‐based planning (KBP) in the last decade. These methods were classified into two major categories—traditional KBP methods and deep‐learning (DL) methods—according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best‐matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements 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 DL methods, then discuss future trends of both data‐driven KBP methods to dose prediction.