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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5525_Библиотеки_им_академика_М_И_Перельмана.pdf
Artificial Intelligence in Adaptive Radiation Therapy
5.2 Big data lifecycle in radiation oncology 5-5
5.2.1 Data aggregation and storage 5-5
5.2.2 Data sharing and security 5-6
5.2.3 Data visualization 5-7
5.2.4 Knowledge creation and implementation 5-8
5.2.5 Data archiving and deletion 5-8
5.3 Big data analytics with AI 5-9
5.3.1 Data processing and integration 5-9
5.3.2 AI modeling 5-10
5.4 The application of big data in radiation oncology 5-13
5.4.1 Medical image segmentation 5-13
5.4.2 Automatic treatment planning 5-14
5.4.3 Treatment response prediction 5-15
5.4.4 Quality assurance and patient safety 5-15
5.4.5 Clinical decision support 5-16
5.5 Challenges and future perspectives 5-17
5.6 Summary 5-19
Reference 5-19
6 Introduction to adaptive radiotherapy 6-1
Jack Neylon, Michael Vincent Lauria and Yi Lao
6.1 The road to ART 6-1
6.1.1 3D conformal radiotherapy (3DCRT) 6-2
6.1.2 Intensity modulated radiotherapy (IMRT) 6-3
6.1.3 Image-guided radiotherapy (IGRT) 6-3
6.1.4 Adaptive radiotherapy (ART) 6-4
6.2 ART workflow and implementation 6-4
6.2.1 ART workflow 6-4
6.2.2 Current practice 6-9
6.2.3 Clinical impact 6-10
6.3 Considerations for implementing online ART 6-12
6.3.1 Time as a limiting factor 6-12
6.3.2 Implications for fast and reliable re-planning 6-13
6.3.3 Implications for automated workflow 6-16
6.3.4 Clinical considerations 6-19
6.4 Summary 6-20
References 6-20
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Artificial Intelligence in Adaptive Radiation Therapy
7 Overview of artificial-intelligence driven adaptive therapy
7-1
workflow
Chenyang Shen, Justin Visak, Andrew Godley and Mu-Han Lin
7.1 Components of ART workflow 7-2
7.1.1 Simulation 7-2
7.1.2 Pre-planning 7-3
7.1.3 Online imaging and daily re-planning 7-4
7.1.4 Quality assurance 7-5
7.2 AI-driven ART 7-6
7.2.1 Simulation 7-6
7.2.2 Pre-planning 7-7
7.2.3 AI for delivery 7-9
7.3 Outlook and future directions 7-9
7.3.1 Real-time ART with AI 7-10
7.3.2 Dose escalation and functional adaption with AI 7-11
7.4 Summary 7-12
References 7-12
8 Imaging, imaging processing, and synthetic computed tomography 8-1
Tonghe Wang and Xiaofeng Yang
8.1 Introduction 8-1
8.2 Synthetic CT: deep learning methods 8-2
8.2.1 Conventional methods 8-2
8.2.2 U-Net 8-3
8.2.3 Generative adversarial networks 8-5
8.2.4 Denoising diffusion probabilistic model 8-7
8.3 Synthetic CT from CBCT 8-8
8.3.1 Noise and artifact reduction 8-9
8.3.2 Online dose calculation 8-10
8.3.3 Online image segmentation 8-11
8.4 Synthetic CT from MRI 8-12
8.4.1 Synthetic image accuracy 8-13
8.4.2 Dose calculation in MR-only radiation therapy 8-20
8.4.3 PET attenuation correction 8-21
8.4.4 Image registration 8-22
8.5 Discussion and outlook 8-23
8.6 Summary 8-24
References 8-25
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Artificial Intelligence in Adaptive Radiation Therapy
9 Artificial intelligence-based image registration and segmentation 9-1
Brian M Anderson and Kristy K Brock
9.1 AI-based image registration and segmentation for ART 9-1
9.1.1 Adaptive radiation therapy 9-1
9.2 Artificial intelligence 9-3
9.2.1 What is machine learning? 9-3
9.2.2 What is deep learning? 9-4
9.3 Deep learning: the basic components 9-4
9.3.1 Convolutional neural networks: looking at the picture 9-4
9.3.2 Pooling layers: keeping what matters most 9-5
9.3.3 Fully connected (dense) layers: bringing it all together 9-7
9.3.4 Activations 9-8
9.3.5 Loss: driving the model 9-10
9.3.6 Auto-encoders: remove the noise 9-10
9.3.7 Supervised versus unsupervised learning 9-10
9.3.8 Pre-trained convolutional neural networks 9-11
9.4 Image registration: bringing two images together 9-12
9.4.1 Registration similarity metrics 9-13
9.4.2 Types of registrations 9-15
9.5 AI-based image registration 9-16
9.5.1 Supervised learning 9-16
9.5.2 Unsupervised learning 9-17
9.5.3 Registration in ART 9-18
9.5.4 Commonalities in architectures 9-18
9.6 Image segmentation 9-18
9.6.1 Introduction: coloring by the numbers 9-18
9.6.2 Segmentation networks 9-21
9.6.3 Best practices 9-23
9.7 Summary 9-25
References 9-25
10 Artificial intelligence-assisted dose prediction and re-planning 10-1
Ivan Vazquez, Laurence E Court and Ming Yang
10.1 Introduction 10-1
10.1.1 Overview of chapter content 10-3
10.2 The landscape of AI-assisted dose prediction 10-3
10.2.1 Traditional machine learning for dose prediction 10-4
10.2.2 Deep learning-based dose prediction 10-5
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10.2.3 Challenges in AI-assisted dose prediction 10-10
10.3 Re-planning workflows powered by AI 10-12
10.3.1 Deep learning for re-planning pipelines 10-13
10.4 Future directions of AI-assisted dose prediction and re-planning 10-15
10.5 Summary 10-16
References 10-17
11 Artificial intelligence-based intrafraction motion monitoring
11-1
for precise adaptive radiation therapy delivery
Lianli Liu and James M Balter
11.1 Introduction 11-1
11.2 Real-time ART during treatment delivery: current status and
challenges
11.2.1 Imaging-based motion monitoring 11-3
11.2.2 Delivery system actions 11-3
11.2.3 Challenges for real-time ART implementation 11-4
11.3 AI in real-time ART workflows 11-6
11.3.1 Improving intrafraction motion monitoring through AI 11-6
11.3.2 Mitigating system latency through AI 11-12
11.4 AI for ART delivery: future directions 11-16
11.4.1 Management of non-respiratory motion 11-16
11.4.2 Training AI models with small or unpaired datasets 11-17
11.4.3 Uncertainty quantification for AI models 11-19
11.4.4 Biology-guided ART delivery 11-20
11.5 Summary 11-22
References 11-23
11-2
12 Artificial intelligence for quality assurance in adaptive
12-1
radiation therapy
Sang Kyu Lee and Maria Chan
12.1 Introduction 12-1
12.2 Patient QA 12-1
12.2.1 Pre-planning QA 12-1
12.2.2 Pre-treatment plan QA 12-4
12.2.3 On-treatment QA 12-10
12.3 Treatment delivery systems and instruments 12-10
12.3.1 Machine commissioning 12-10
12.3.2 Machine QA 12-14
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Artificial Intelligence in Adaptive Radiation Therapy
12.3.3 Dosimetry tool QA 12-15
12.4 Summary 12-17
References 12-17
13 Artificial intelligence empowered response prediction and
13-1
adaptation
Denis Dudas and Issam El Naqa
13.1 Data resources for response modeling in radiotherapy 13-1
13.1.1 Clinical data 13-2
13.1.2 Imaging (radiomics) 13-3
13.1.3 Treatment planning (dosiomics) 13-5
13.1.4 Multiomics 13-7
13.2 Radiotherapy treatment outcome modeling 13-8
13.2.1 TCP/NTCP in radiotherapy 13-8
13.2.2 Clinical outcomes versus PROs 13-10
13.2.3 Machine learning response prediction 13-11
13.2.4 Explainability of ML response models 13-15
13.2.5 Sample use cases 13-16
13.3 AI response-based adaptive radiotherapy 13-19
13.3.1 Requirements and challenges 13-19
13.3.2 Prediction versus treatment optimization 13-22
13.3.3 Sample use cases 13-23
13.4 Challenges and recommendations 13-24
13.5 Summary 13-26
Acknowledgments 13-27
References 13-27
14 Challenges of artificial intelligence implementation in adaptive
14-1
radiation therapy
Yi Wang and X. Sharon Qi
14.1 Overview of challenges in AI-driven ART 14-1
14.2 Data challenges 14-2
14.2.1 Data availability 14-2
14.2.2 Data quality 14-3
14.2.3 Data privacy 14-3
14.3 Technical challenges 14-4
14.3.1 Model accuracy and efficiency 14-4
14.3.2 Model robustness and generalizability 14-5
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Artificial Intelligence in Adaptive Radiation Therapy
14.3.3 Model explainability and interpretability 14-6
14.3.4 Computational efficiency 14-6
14.4 Challenges associated with online and real-time workflows 14-7
14.4.1 Image quality 14-7
14.4.2 Dose calculation 14-8
14.4.3 Real-time ART 14-8
14.5 Operational challenges 14-9
14.5.1 Clinical validation 14-9
14.5.2 Workflow integration 14-10
14.5.3 Staff training 14-10
14.5.4 User experiences 14-11
14.5.5 Quality management program 14-11
14.5.6 Financial challenges 14-12
14.6 Ethical, regulatory, and legal challenges 14-13
14.6.1 Ethical issues 14-13
14.6.2 Regulatory and legal issues 14-14
14.7 Summary 14-14
References 14-15
15 Offline computed tomography-based and online cone beam
15-1
computed tomography-based adaptive radiation therapy
Joel A Pogue, Natalie Viscariello, Dennis N Stanley, Joseph Harms,
Richard A Popple and Carlos E Cardenas
15.1 Clinical considerations for CT-based offline ART 15-1
15.1.1 Patient and site selection 15-2
15.1.2 Re-simulation 15-3
15.1.3 Re-planning 15-4
15.1.4 Plan summation and evaluation 15-4
15.1.5 Patient specific quality assurance 15-5
15.1.6 Limitations and future directions 15-6
15.2 Clinical considerations for CBCT/CT-based online ART 15-6
15.2.1 Online-ART-specific challenges 15-7
15.2.2 Patient and site selection 15-8
15.2.3 Simulation 15-8
15.2.4 Pre-planning review 15-10
15.2.5 Reference planning 15-11
15.2.6 Patient specific quality assurance 15-14
15.2.7 Online ART workflow 15-15
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Artificial Intelligence in Adaptive Radiation Therapy
15.2.8 Offline contour and plan evaluation 15-18
15.2.9 Limitations and future directions 15-19
15.3 Summary 15-19
References 15-19
16 Artificial intelligence in MRI-guided adaptive radiation therapy 16-1
Lauren Smith, Yao Zhao, Jinzhong Yang and X. Sharon Qi
16.1 Introduction 16-1
16.2 Overview of MRI-guided ART systems 16-2
16.2.1 High field MRI system 16-2
16.2.2 Low-field MRI system 16-4
16.3 MRI-guided ART workflow 16-5
16.3.1 Offline MRI-guided ART workflow 16-5
16.3.2 Online MRI-guided ART workflow 16-6
16.3.3 Challenges in MRI ART workflow 16-10
16.4 AI applications for MRI-guided ART 16-13
16.4.1 Synthetic CT generation 16-14
16.4.2 Auto-segmentation 16-15
16.4.3 Image registration 16-17
16.4.4 Others 16-18
16.4.5 Future AI development and implementation 16-18
16.5 Summary 16-18
References 16-19
17 Functional imaging-guided adaptive radiation therapy 17-1
Bin Han and Yu Gao
17.1 Functional PET-guided ART 17-1
17.1.1 PET-based functional imaging overview 17-2
17.1.2 From anatomy to function: the power of PET in radiation
therapy
17.1.3 Practicalities and clinical implications of PET-guided
adaptive radiation therapy
17.1.4 Artificial intelligence in PET-guided adaptive radiation therapy 17-8
17.1.5 Conclusions and future prospects 17-9
17.2 Functional MRI-guided ART 17-9
17.2.1 From anatomy to function: the power of functional MRI in
radiation therapy
17.2.2 Practicalities and clinical implications of functional
MRI-guided adaptive radiation therapy
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17-5
17-6
17-10
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Artificial Intelligence in Adaptive Radiation Therapy
17.2.3 Artificial intelligence in functional MRI-guided adaptive
radiation therapy
17.2.4 Conclusion and future prospects 17-16
17.3 Summary 17-17
References 17-17
17-14
18 Artificial intelligence in proton adaptive radiation therapy 18-1
Brian Winey
18.1 Proton ART 18-1
18.1.1 Clinical context and necessity 18-1
18.1.2 Patient populations 18-3
18.1.3 Imaging and adaptive workflows 18-4
18.1.4 Rationale for AI in proton ART 18-5
18.2 AI in proton ART 18-6
18.2.1 Imaging 18-6
18.2.2 Deformable and rigid registration 18-7
18.2.3 Contour propagation 18-8
18.2.4 Dose calculations 18-8
18.2.5 Plan optimization 18-8
18.2.6 Other developments 18-9
18.3 Implementation of adaptive proton therapy 18-9
18.4 Summary 18-9
References 18-10
19 Artificial intelligence in clinical trials 19-1
Sang Ho Lee, Huaizhi Geng and Ying Xiao
19.1 Designing clinical trials with AI 19-1
19.1.1 The essential role of clinical trials 19-1
19.1.2 Trial protocols and methodologies 19-2
19.1.3 AI-driven clinical trial design and execution 19-3
19.1.4 Incorporation of digital twins (DTs) in clinical trials 19-3
19.2 Implementation of AI in ongoing clinical trials 19-4
19.2.1 Integration with existing clinical trial frameworks 19-4
19.2.2 Quality assurance, compliance, and standardization 19-7
19.3 Case studies of AI in adaptive radiotherapy trials 19-8
19.3.1 Overview of guidance for advanced radiotherapy in
clinical trials
19.3.2 AI in the radiotherapy clinical trial quality assurance
processes
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19-8
19-9
Artificial Intelligence in Adaptive Radiation Therapy
19.4 Ethical and regulatory considerations 19-10
19.4.1 Patient consent and data privacy 19-10
19.4.2 Bias, fairness, and transparency 19-11
19.4.3 Regulatory guidelines and compliance 19-12
19.5 Future directions and challenges 19-13
19.5.1 Emerging technologies and techniques 19-13
19.5.2 Alternative strategies 19-14
19.6 Conclusion 19-15
19.7 Summary 19-15
References 19-16
20 Safety and training considerations in the clinical implementation
20-1
of artificial intelligence adaptive radiation therapy
Kelly Nealon and Jennifer Pursley
20.1 Risk management 20-1
20.1.1 Prospective risk assessments 20-2
20.1.2 Root cause analysis 20-5
20.2 Staff training 20-6
20.2.1 Process mapping of AI ART workflow 20-7
20.2.2 Role-specific training processes 20-7
20.3 End-to-end testing 20-13
20.3.1 Phantom selection 20-13
20.3.2 End-to-end adaptive delivery 20-15
20.4 Summary 20-16
References 20-16
21 Ethical and regulatory considerations in artificial intelligence
21-1
for adaptive radiation therapy
Dandan Zheng, Megan Hyun and Andrew Fanning
21.1 Background of ethics and regulations in AI for ART 21-1
21.1.1 A brief bioethics overview 21-1
21.1.2 Regulatory state of affairs for AI in medicine 21-2
21.1.3 Ethical and regulatory concerns for AI in ART 21-3
21.1.4 Some approaches to ethical AI in medicine 21-4
21.2 Background of AI for ART 21-5
21.2.1 General workflow of ART 21-5
21.2.2 AI in ART 21-7
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Artificial Intelligence in Adaptive Radiation Therapy
21.3 Ethical and regulatory considerations in AI for ART 21-10
21.3.1 Ethical and regulatory concerns in the uses of AI for ART 21-10
21.3.2 Applying ethics approaches to AI in ART 21-13
21.3.3 An ethical argument for AI in ART 21-15
21.4 Summary 21-17
References 21-17
22 Recent advances and future of artificial intelligence-augmented
22-1
adaptive radiation therapy
Oscar Pastor-Serrano, Xianjin Dai and Lei Xing
22.1 Introduction 22-1
22.1.1 Clinical need for adaptive radiation therapy 22-2
22.1.2 Challenges in the clinical implementation of ART 22-3
22.1.3 AI and AI in healthcare 22-4
22.2 Recent advancements in AI in ART 22-5
22.2.1 AI-augmented imaging and image analysis 22-5
22.2.2 AI-based image segmentation and registration 22-9
22.2.3 AI-augmented dose calculation 22-10
22.2.4 AI-augmented treatment planning 22-15
22.2.5 The workflow of future AI-ART 22-18
22.3 Summary 22-19
References 22-19
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