TY - JOUR
T1 - Artificial Intelligence in Radiotherapy Treatment Planning
T2 - Present and Future
AU - Wang, Chunhao
AU - Zhu, Xiaofeng
AU - Hong, Julian C.
AU - Zheng, Dandan
N1 - Publisher Copyright:
© The Author(s) 2019.
PY - 2019
Y1 - 2019
N2 - Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.
AB - Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.
KW - artificial intelligence machine learning radiotherapy treatment planning automation
UR - http://www.scopus.com/inward/record.url?scp=85071896375&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071896375&partnerID=8YFLogxK
U2 - 10.1177/1533033819873922
DO - 10.1177/1533033819873922
M3 - Review article
C2 - 31495281
AN - SCOPUS:85071896375
VL - 18
JO - Technology in Cancer Research and Treatment
JF - Technology in Cancer Research and Treatment
SN - 1533-0346
ER -