The role of artificial intelligence in radiotherapy clinical practice.
Guillaume LandryChristopher KurzAlberto TraversoPublished in: BJR open (2023)
This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical practice. We will discuss how AI has a place in the modern radiotherapy workflow at the level of automatic segmentation and planning, two applications which have seen real-work implementation. A special emphasis will be placed on the role AI can play in online adaptive radiotherapy, such as performed at MR-linacs, where online plan adaptation is a procedure which could benefit from automation to reduce on-couch time for patients. Pseudo-CT generation and AI for motion tracking will be introduced in the scope of online adaptive radiotherapy as well. We further discuss the use of AI for decision-making and response assessment, for example for personalized prescription and treatment selection, risk stratification for outcomes and toxicities, and AI for quantitative imaging and response assessment. Finally, the challenges of generalizability and ethical aspects will be covered. With this, we provide a comprehensive overview of the current and future applications of AI in radiotherapy.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- early stage
- big data
- locally advanced
- clinical practice
- radiation induced
- radiation therapy
- decision making
- high resolution
- social media
- computed tomography
- convolutional neural network
- squamous cell carcinoma
- rectal cancer
- health information
- minimally invasive
- type diabetes
- prognostic factors
- metabolic syndrome
- contrast enhanced
- magnetic resonance imaging
- combination therapy