Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future

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.

Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application

“…On the other hand, an AI should "see all kinds of cases, " including both good cases and suboptimal cases. Otherwise, an AI agent trained by only good cases will be less likely to perform well when encountering an unprecedented "bad" input (28). As a result, small animal experiments which simulate radiotherapy treatments with both "good" and "bad" qualities can increase the robustness of DDD-PIOP towards common clinical application.…”

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

“…In addition, it could enable comparisons of treatment techniques with minimal bias, study eligibility and shared/informed decision making for personalized treatment planning (e.g. patient selection) [61,62].…”

“…Lastly, validation and test sets typically consist of minimally 10 patients for both types of models [77]. In case of large variation within the data and/or results, it is advisable to evaluate more patients [62,78].…”

A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning

“…In recent years, a number of deep learning (DL)-based ATP techniques have been proposed using various DL neural networks (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33). Several review articles on AI in radiation oncology (34)(35)(36), and radiotherapy treatment planning (37)(38)(39), have been published, which demonstrated the interests on AI and the significance of ATP, summarization of the achievements and challenges, as well as insightful discussion on future studies. No comprehensive review specifically focused on deep learningbased automated radiotherapy planning was published.…”