Knowledge‐based radiation treatment planning: A data‐driven method survey

• Type of Material: Article
• Authors: Shadab Momin, Emory University; Yabo Fu, Emory University; Yang Lei, Emory University; Justin Roper, Emory University; Jeffrey Bradley, Emory University; Walter Curran Jr, Emory University; Tian Liu, Emory University; Xiaofeng Yang, Emory University
• Language: English
• Date: 2021-08-01
• Publisher: The American College of Medical Physics and American Institute of Physics
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/acm2.13337
• Title of Journal or Parent Work: Journal of Applied Clinical Medical Physics
• Volume: 22
• Issue: 8
• Start Page: 16
• End Page: 44
• Keywords: deep learning; knowledge‐based planning; radiation dose prediction methods; radiotherapy treatment planning; data‐driven methods; machine learning
• Research Categories: Health Sciences, Radiology; Health Sciences, Education

Abstract

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.