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Artificial Intelligence in Radiation Therapy [Kõva köide]

Edited by (Osaka University)
Teised raamatud teemal:
Artificial Intelligence in Radiation TherapySuurem pilt
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780750333375.html
Märksõnad:
Preface x
Editor biography xi
List of contributors
xii
1 Introduction
1(1)
Iori Sumida
References
2
2 Artificial intelligence and machine learning
1(1)
Omid Nohadani
2.1 Introduction
1(2)
2.1.1 Foundations, similarities, and differences
2(1)
2.1.2 Connection to decision making
3(1)
2.2 Overview of learning methods
3(2)
2.2.1 Supervised learning
3(1)
2.2.2 Unsupervised learning
4(1)
2.2.3 Semi-supervised learning
4(1)
2.2.4 Reinforcement learning
4(1)
2.3 Common algorithms
5(13)
2.3.1 Gaussian mixture models
5(2)
2.3.2 Regression and classification algorithms
7(3)
2.3.3 Decision-tree algorithms
10(1)
2.3.4 Optimal trees
11(1)
2.3.5 Neural networks
12(6)
2.4 Summary
18(1)
2.5 Acknowledgement
18(1)
References
18
3 Overview of AI applications in radiation therapy
1(1)
Yang Sheng
Jiahan Zhang
3.1 Opportunities of AI applications in modern radiotherapy workflow
1(15)
3.2 Summary
16(1)
References
16
4 Introduction to CT/MR simulation in radiotherapy
1(1)
Iori Sumida
Noriyuki Kadoya
4.1 Simulation procedure in the radiation therapy process
1(2)
4.2 Immobilization device for radiation therapy
3(1)
4.2.1 Systematic error and random error
3(1)
4.2.2 Reproducibility of patient setup
4(1)
4.3 Image quality and acquisition time
4(2)
4.4 Image deformation
6(1)
4.4.1 Deformable image registration
6(2)
4.4.2 AI driven image deformation
8(3)
4.4.3 Practical implementation of AI
11(9)
References
20
5 Organ delineation
1(1)
Xiao Ying
Men Kuo
5.1 Introduction to organ delineation in radiotherapy
1(3)
5.1.1 Organ delineation in the radiation therapy process
1(2)
5.1.2 Impact of delineation accuracies
3(1)
5.2 Organ delineation methodologies
4(8)
5.2.1 Automated image segmentation techniques and deep learning applications
4(8)
5.3 Implementation for clinical diseases: targets and normal structures
12(2)
5.3.1 Head and neck and brain structures
12(1)
5.3.2 Thoracic and gastrointestinal structures
13(1)
5.3.3 Pelvic structures
13(1)
5.4 Best practice implementation of AI driven delineation
14(3)
5.5 Future developments and outlook
17(1)
References
18
6 Automated treatment planning
1(1)
Charles Huang
Lei Xing
6.1 Goals and motivations of treatment planning
1(2)
6.2 Automated treatment planning overview
3(1)
6.3 Knowledge-based planning
3(1)
6.4 Protocol-based planning
4(1)
6.5 Multicriteria optimization
5(3)
References
8
7 Artificial intelligence in adaptive radiation therapy
1(1)
Yi Wang
Bin Cai
Leigh Conroy
X. Sharon Qi
7.1 Introduction
1(4)
7.1.1 Advantages of ART
2(1)
7.1.2 Types of ART, current status and challenges
3(1)
7.1.3 Overview of current workflow of ART and current challenges
4(1)
7.1.4 AI and Al-assisted technologies for ART
5(1)
7.2 The role of AI in ART workflow
5(8)
7.2.1 Deep learning for improving in-room image quality and generating pseudo-CT
7(1)
7.2.2 Deep learning for deformable image registration and auto-segmentation
8(1)
7.2.3 Machine learning for decision support on daily adaptation
9(1)
7.2.4 Machine learning for online re-optimization
9(1)
7.2.5 AI for quality assurance, verification, and error detection
10(1)
7.2.6 AI for physics plan check
10(3)
7.2.7 Considerations for education and training
13(1)
7.3 Existing AI solutions for ART
13(4)
7.3.1 Ethos online ART platform from Varian medical
14(2)
7.3.2 Machine learning solutions from RaySearch Laboratories
16(1)
7.3.3 PreciseART offline dose monitoring platform from Accuray
17(1)
7.4 Summary
17(1)
References
17
8 AI-augmented image guidance for radiation therapy delivery
1(1)
Zhao Wei
8.1 Introduction to image guidance for radiotherapy
1(7)
8.1.1 Background
1(1)
8.1.2 Current image guidance solutions
2(5)
8.1.3 AI tools and networks for image guidance
7(1)
8.2 Image guidance for interfraction motion
8(5)
8.2.1 Patients setup based on orthogonal kV images
8(2)
8.2.2 Pretreatment daily cone-beam CT imaging
10(3)
8.3 Image guidance for intrafraction motion
13(5)
8.3.1 Real-time monitoring methods
13(3)
8.3.2 Real-time needle and fiducial segmentation
16(2)
8.4 Real-time 3D IGRT on standard linac
18(1)
8.5 Summary
19(1)
References
20
9 AI for quality management in radiation therapy
1(1)
Quan Chen
Yi Rong
Zhichao Wang
Tianye Niu
9.1 QA versus QC
1(1)
9.2 AI for chart review
2(2)
9.3 AI for patient specific QA and gamma passing rate prediction
4(2)
9.4 AI for dosimetric and mechanical QA for linear accelerators
6(1)
9.4.1 Output factor and monitor unit
6(1)
9.4.2 Linac mechanical error detection
6(1)
9.5 Summary
7(1)
References
7
10 Data-driven approaches in radiotherapy outcome modeling
1(1)
Ibrahim Chamseddine
Yejin Kim
Clemens Grassberger
10.1 Introduction
1(1)
10.2 Analytical dose-response models and extensions
2(2)
10.2.1 Linear-quadratic model and equivalent dose
2(1)
10.2.2 Tumor control probability and normal tissue complication probability
3(1)
10.3 Overview of machine learning models
4(6)
10.3.1 Endpoint prediction: regression and classification
4(3)
10.3.2 Inclusion of imaging data
7(1)
10.3.3 Survival prediction models
8(1)
10.3.4 Performance evaluation metrics
8(2)
10.4 Practical considerations---building models for radiation oncology
10(5)
10.4.1 Input data
10(1)
10.4.2 Feature importance and selection
11(1)
10.4.3 Tuning hyperparameters
12(1)
10.4.4 Resampling: cross-validation and bootstrapping
12(1)
10.4.5 Nested cross-validation and final model selection
13(1)
10.4.6 Model validation
14(1)
10.5 Including dose distributions into data-driven outcome models
15(1)
10.5.1 Voxel-based analysis
15(1)
10.6 Model reporting: TRIPOD and study analysis plans
16(1)
10.6.1 Study analysis plans
17(1)
10.7 Conclusion and future challenges
17(1)
References
18
11 Challenges in artificial intelligence development of radiotherapy
1(9)
Huanmei Wu
Jay S. Patel
11.1 Radiomics: past, current, and future
1(3)
11.1.1 Multiparametric radiomics
2(1)
11.1.2 Multi-radiomics
2(1)
11.1.3 Artificial intelligence (AI)-empowered radiomics
3(1)
11.1.4 Precision radiotherapy
4(1)
11.2 AI and multi-radiomics as a hybrid way for AI development
4(1)
11.3 Ethics and regulations for artificial intelligence using biomedical informatics
5(2)
11.4 Heterogeneous biomedical data management
7(2)
11.5 Human harms due to AI
9(1)
References 10