Artificial Intelligence in Radiation Therapy

PRELIMS.pdf
	Preface
	Editor biography
		Iori Sumida
	List of contributors
CH001.pdf
	Chapter 1 Introduction
		References
CH002.pdf
	Chapter 2 Artificial intelligence and machine learning
		2.1 Introduction
			2.1.1 Foundations, similarities, and differences
			2.1.2 Connection to decision making
		2.2 Overview of learning methods
			2.2.1 Supervised learning
			2.2.2 Unsupervised learning
			2.2.3 Semi-supervised learning
			2.2.4 Reinforcement learning
		2.3 Common algorithms
			2.3.1 Gaussian mixture models
			2.3.2 Regression and classification algorithms
			2.3.3 Decision-tree algorithms
			2.3.4 Optimal trees
			2.3.5 Neural networks
		2.4 Summary
		2.5 Acknowledgement
		References
CH003.pdf
	Chapter 3 Overview of AI applications in radiation therapy
		3.1 Opportunities of AI applications in modern radiotherapy workflow
		3.2 Summary
		References
CH004.pdf
	Chapter 4 Introduction to CT/MR simulation in radiotherapy
		4.1 Simulation procedure in the radiation therapy process
		4.2 Immobilization device for radiation therapy
			4.2.1 Systematic error and random error
			4.2.2 Reproducibility of patient setup
		4.3 Image quality and acquisition time
		4.4 Image deformation
			4.4.1 Deformable image registration
			4.4.2 AI driven image deformation
			4.4.3 Practical implementation of AI
		References
CH005.pdf
	Chapter 5 Organ delineation
		5.1 Introduction to organ delineation in radiotherapy
			5.1.1 Organ delineation in the radiation therapy process
			5.1.2 Impact of delineation accuracies
		5.2 Organ delineation methodologies
			5.2.1 Automated image segmentation techniques and deep learning applications
		5.3 Implementation for clinical diseases: targets and normal structures
			5.3.1 Head and neck and brain structures
			5.3.2 Thoracic and gastrointestinal structures
			5.3.3 Pelvic structures
		5.4 Best practice implementation of AI driven delineation
		5.5 Future developments and outlook
		References
CH006.pdf
	Chapter 6 Automated treatment planning
		6.1 Goals and motivations of treatment planning
		6.2 Automated treatment planning overview
		6.3 Knowledge-based planning
		6.4 Protocol-based planning
		6.5 Multicriteria optimization
		References
CH007.pdf
	Chapter 7 Artificial intelligence in adaptive radiation therapy
		7.1 Introduction
			7.1.1 Advantages of ART
			7.1.2 Types of ART, current status and challenges
			7.1.3 Overview of current workflow of ART and current challenges
			7.1.4 AI and AI-assisted technologies for ART
		7.2 The role of AI in ART workflow
			7.2.1 Deep learning for improving in-room image quality and generating pseudo-CT
			7.2.2 Deep learning for deformable image registration and auto-segmentation
			7.2.3 Machine learning for decision support on daily adaptation
			7.2.4 Machine learning for online re-optimization
			7.2.5 AI for quality assurance, verification, and error detection
			7.2.6 AI for physics plan check
			7.2.7 Considerations for education and training
		7.3 Existing AI solutions for ART
			7.3.1 Ethos online ART platform from Varian medical
			7.3.2 Machine learning solutions from RaySearch Laboratories
			7.3.3 PreciseART offline dose monitoring platform from Accuray
		7.4 Summary
		References
CH008.pdf
	Chapter 8 AI-augmented image guidance for radiation therapy delivery
		8.1 Introduction to image guidance for radiotherapy
			8.1.1 Background
			8.1.2 Current image guidance solutions
			8.1.3 AI tools and networks for image guidance
		8.2 Image guidance for interfraction motion
			8.2.1 Patients setup based on orthogonal kV images
			8.2.2 Pretreatment daily cone-beam CT imaging
		8.3 Image guidance for intrafraction motion
			8.3.1 Real-time monitoring methods
			8.3.2 Real-time needle and fiducial segmentation
		8.4 Real-time 3D IGRT on standard linac
		8.5 Summary
		References
CH009.pdf
	Chapter 9 AI for quality management in radiation therapy
		9.1 QA versus QC
		9.2 AI for chart review
		9.3 AI for patient specific QA and gamma passing rate prediction
		9.4 AI for dosimetric and mechanical QA for linear accelerators
			9.4.1 Output factor and monitor unit
			9.4.2 Linac mechanical error detection
		9.5 Summary
		References
CH010.pdf
	Chapter 10 Data-driven approaches in radiotherapy outcome modeling
		10.1 Introduction
		10.2 Analytical dose–response models and extensions
			10.2.1 Linear-quadratic model and equivalent dose
			10.2.2 Tumor control probability and normal tissue complication probability
		10.3 Overview of machine learning models
			10.3.1 Endpoint prediction: regression and classification
			10.3.2 Inclusion of imaging data
			10.3.3 Survival prediction models
			10.3.4 Performance evaluation metrics
		10.4 Practical considerations—building models for radiation oncology
			10.4.1 Input data
			10.4.2 Feature importance and selection
			10.4.3 Tuning hyperparameters
			10.4.4 Resampling: cross-validation and bootstrapping
			10.4.5 Nested cross-validation and final model selection
			10.4.6 Model validation
		10.5 Including dose distributions into data-driven outcome models
			10.5.1 Voxel-based analysis
		10.6 Model reporting: TRIPOD and study analysis plans
			10.6.1 Study analysis plans
		10.7 Conclusion and future challenges
		References
CH011.pdf
	Chapter 11 Challenges in artificial intelligence development of radiotherapy
		11.1 Radiomics: past, current, and future
			11.1.1 Multiparametric radiomics
			11.1.2 Multi-radiomics
			11.1.3 Artificial intelligence (AI)-empowered radiomics
			11.1.4 Precision radiotherapy
		11.2 AI and multi-radiomics as a hybrid way for AI development
		11.3 Ethics and regulations for artificial intelligence using biomedical informatics
		11.4 Heterogeneous biomedical data management
		11.5 Human harms due to AI
		References