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Review
. 2019 Jan 1:18:1533033819873922.
doi: 10.1177/1533033819873922.

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future

Chunhao Wang  1 , Xiaofeng Zhu  2 , Julian C Hong  1   3 , Dandan Zheng  4
Affiliations
Review

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future

Chunhao Wang et al. Technol Cancer Res Treat. .

Abstract

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.

Keywords: artificial intelligence machine learning radiotherapy treatment planning automation.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
A, A brief workflow of manual treatment planning. B, A brief workflow of ARIR in treatment planning. C, A brief workflow of KBP in treatment planning. D, A brief workflow of MCO in treatment planning. E, A brief workflow of AI use in future treatment planning. AI indicates artificial intelligence; ARIR, automated rule implementation and reasoning; KBP, knowledge-based planning; MCO, multicriteria optimization.
See this image and copyright information in PMC

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