Image-Guided High-Precision Radiotherapy

Preface
Contents
Part I: Target Volume Definition
1: Use of [18F]FDG PET/CT for Target Volume Definition in Radiotherapy
1.1 Introduction
1.2 [18F]FDG PET/CT
1.2.1 PET/CT
1.2.2 [18F]FDG
1.3 Technical Aspects
1.3.1 Patient Preparation
1.3.2 Image Acquisition and Reconstruction
1.3.3 Fusion and Registration of Treatment Planning PET/CT
1.3.3.1 Registration PET with CTAC
1.3.3.2 Registration PET(/CT) with Planning-CT
1.3.4 Motion
1.3.4.1 Types of Motion
1.3.4.2 Motion Correction Methods
Alignment
Compensation Methods for Respiratory Motion
1.4 Target Volume Delineation
1.4.1 PET/CT in Target Volume Delineation
1.4.2 PET/CT Segmentation Methods
1.4.2.1 Manual, Semi-Automatic and Automatic Segmentation Methods
1.4.2.2 Drawback of Segmentation Methods
1.4.2.3 Consensus Algorithms
1.4.3 Disease Sites
1.4.3.1 NSCLC and SCLC
1.4.3.2 HNSCC
1.4.3.3 Oesophageal Cancer
1.4.3.4 Gynaecological Tumours
1.4.3.5 Lymphoma
1.4.3.6 Other Tumours
1.5 Cons and Pitfalls
1.5.1 Limited Spatial Resolution and High Signal-to-Noise Ratio of PET
1.5.2 Interpretation Errors Due to Limited Specificity (and Sensitivity) of the Radiopharmaceutical [18F]FDG
1.6 Future Outlook
References
2: Specific PET Tracers for Solid Tumors and for Definition of the Biological Target Volume
2.1 Introduction
2.2 Brain Tumors
2.2.1 Glioma Hypoxia and Activated Microglia
2.2.2 Meningioma
2.3 Head and Neck Squamous Cell Carcinomas (HNSCC)
2.4 Non-small Cell Lung Cancer
2.5 Prostate Cancer
2.5.1 PET in Primary Staging
2.5.2 Salvage Radiotherapy in Recurrent Prostate Cancer
2.6 Future Prospects
References
3: Use of Anatomical and Functional MRI in Radiation Treatment Planning
3.1 Introduction
3.2 Brain
3.2.1 High-Grade Gliomas
3.2.1.1 What Is the MR’s Standard Acquisition Protocol for RT Planning?
3.2.1.2 Which Are the MRI Studies Currently Available?
3.2.1.3 How Can MRI Be Used in Treatment Planning Workflow in High-Grade Glioma Radiotherapy?
3.2.1.4 Which MRI Sequences Can Be Used?
3.2.1.5 What Are the Recommendations for Contouring?
3.2.1.6 Which Characteristics Should an MRI Have for Adjuvant Radiotherapy Planning?
3.2.2 Low-Grade Gliomas
3.2.2.1 Can MRI Be Useful in Contouring of Low-Grade Gliomas?
3.2.3 Brain Metastases
3.2.3.1 Can MRI Be Useful in Radiotherapy Workflow for Brain Metastases?
3.2.3.2 How Can MRI Help in Defining GTV?
3.2.3.3 Is the Use of Co-Registration with MRI Also Recommended in Whole-Brain Treatment?
3.2.4 Meningiomas
3.2.4.1 Does MRI Play a Role in the Contouring of Meningiomas?
3.2.4.2 What Are the Recommendations for Contouring?
3.3 Head and Neck Cancer
3.3.1 How Can MRI Be Integrated into Treatment Planning for HNC?
3.3.2 What Sequences Does the Standard MRI Acquisition Protocol Provide?
3.3.3 How Can MRI Be Used for Nasopharyngeal Cancer Segmentation?
3.3.4 How Can MRI Be Used for Segmentation of Oropharyngeal Cancer?
3.3.5 How MRI Be Used in Segmentation of Tumours of the Oral Cavity?
3.3.6 How Can MRI Be Used in Glottic Cancer Segmentation?
3.3.7 How Can MRI Be Used in Hypopharyngeal Cancer Segmentation?
3.3.8 How Can MRI Be Used in Paranasal Sinus Cancer Segmentation?
3.3.9 How Can MRI Be Used in Major Salivary Glands Cancer Segmentation?
3.3.10 Can DWI Sequences Be Used in Segmentation in HNC?
3.3.11 How Can Functional MRI Imaging Be Used in HNC?
3.3.12 Are New MRI Imaging Methods Evolving to Support Segmentation in HNC?
3.4 Liver
3.4.1 What Is the Role of MRI in the Radiotherapy Workflow for Hepatic Lesions?
3.4.2 What Do Liver Metastases and HCC Lesions Look Like in the Different MRI Sequences?
3.5 Pancreatic Cancer
3.5.1 How Can MRI Be Used in Pancreatic Cancer Segmentation?
3.5.2 How Can MRI Be Included in the Pancreatic RT Treatment Planning Workflow?
3.5.3 What Does Pancreatic Cancer and Surrounding Organs at Risk Look like in MRI?
3.5.4 Are There Any Recommendations to Follow in the Different Segmentation Steps?
3.6 Prostate Cancer
3.6.1 What Are the Recommendations for the Use of MRI in the Simulation Phase of Radiotherapy Planning?
3.6.2 How Does Prostate Cancer Show Up on MRI Images?
3.6.3 Can MRI Help in Identifying Intraprostatic Tumour Lesions to Guide the Definition of a Radiotherapy Boost?
3.6.4 What Is the Use of MRI in Salvage Radiation Therapy after Radical Prostatectomy and for Local Recurrence?
3.6.5 What Is the Role of MRI in PC Brachytherapy?
3.6.6 What Are the Perspectives of MRI for PC in the Future?
3.7 Cervical Cancer
3.7.1 How Can I Use the Different MRI Sequences for EBRT Target Volume Delineation?
3.7.2 Can MRI Give Information Also during Treatment?
3.7.3 How Can MRI Be Implemented in Brachytherapy Planning Workflow?
3.8 Rectal Cancer
3.8.1 How Can MRI Be Included into LARC RT Treatment Planning?
3.8.2 What Are the Recommendations for Rectal Cancer Segmentation?
3.8.3 Can MRI Be Useful in Defining Treatment Response and Guiding Escalation Dose Protocols?
3.9 Anal Cancer
3.9.1 What Are the Potential Uses of MRI in Treatment Planning in Anal Cancer?
3.9.2 What Are the MRI Sequences that Can Help with Contouring?
3.9.3 Can MRI Be Useful in Brachytherapy Planning?
3.9.4 Can MRI Be Helpful in Follow-Up?
3.9.5 How Can the MRI Be Used in the Future?
References
Part II: Image-Guided Radiation Therapy Techniques
4: In-Room Systems for Patient Positioning and Motion Control
4.1 Introduction
4.2 X-Ray Imaging Systems
4.2.1 Electronic Portal Imaging
4.2.2 Planar kV X-Ray Imaging
4.2.3 Cone-Beam Computed Tomography
4.2.4 Helical Tomotherapy System
4.2.5 In-Room Computed Tomography System on Rails
4.3 Surrogate Tracking
4.3.1 Optical Guidance
4.3.2 Infrared Markers
4.3.3 Spirometry
4.3.4 Mechanical Gauges
4.3.5 Electromagnetic Transponders
4.4 Ultrasound
References
5: IMRT/VMAT-SABR
5.1 The Technology-Driven Evolution of Radiotherapy
5.2 The Radiation Therapy Workflow
5.2.1 Immobilization: Dealing with Patient Position Reproducibility in Radiotherapy
5.2.2 Image Acquisition
5.2.3 Contouring, Margin Calculation and Dose Prescription
5.2.4 Treatment Planning
5.2.5 Treatment Delivery
5.3 Radiotherapy Techniques
5.3.1 Conventional Radiotherapy: Three-Dimensional Conformal RT
5.3.2 From Convex to Concave: The Need for Modulations
5.3.3 Modulated Techniques: IMRT and VMAT
5.3.3.1 The Need for Image Guidance and Motion Control in Modulated Techniques
5.3.4 Stereotactic Ablative Body Radiotherapy
5.4 Considerations on Dose Calculations for IMRT, VMAT and SABR Treatment Planning
5.5 Dealing with Organ Motion in Radiotherapy
References
6: Magnetic Resonance-Guided Adaptive Radiotherapy: Technical Concepts
6.1 Image Guidance and Adaptive Radiotherapy
6.1.1 Development of MR-Guided Radiotherapy Systems
6.1.2 The ViewRay System
6.1.3 The Unity System
6.1.4 MRgRT Systems under Development
6.2 The Effect of the Magnetic Field on the Dose Distribution
6.2.1 The Lorentz Force
6.3 The Dose Distribution in a Transverse Magnetic Field
6.3.1 Beam Entry
6.3.2 Beam Exit
6.3.3 Penumbra
6.3.4 Central Axis
6.4 Dose Measurements in a Magnetic Field
6.5 Reference Dosimetry
6.6 Relative Dosimetry
6.7 Magnetic Resonance Imaging for MRgRT
6.7.1 Magnetic Resonance Imaging
6.7.2 Safety
6.7.3 Image Acquisition
6.7.4 Image Quality
6.8 Treatment Planning for the MR-Linac
6.8.1 Quality Assurance
6.9 Workflows for Adaptive MRgRT
6.9.1 Offline Workflows
6.9.2 Online Workflows
6.9.3 Intra-Fraction Monitoring and Adaptation
6.10 The Future of MRgRT
References
7: MR-Integrated Linear Accelerators: First Clinical Results
7.1 Introduction
7.2 Clinical Sites
7.2.1 Brain and Spine
7.2.2 Head and Neck
7.2.3 Thoracic Tumors
7.2.4 Abdominal Tumors
7.2.5 Pelvic Tumors
References
8: Image-Guided Adaptive Brachytherapy
8.1 Introduction
8.2 Clinical Aspects in Imaging for Cervix Brachytherapy
8.2.1 Ultrasound
8.2.2 MRI
8.2.3 Image-Guided Adaptive Brachytherapy
8.3 Clinical Aspects in Imaging for Prostate Brachytherapy
8.3.1 US-Guided Applications
8.3.2 CT during Implantation and CT Postplanning
8.3.3 CT- and MRI-Based Treatment Planning
8.3.4 Focused Boost
8.3.5 Focal and Partial Prostate Brachytherapy
8.4 Physics Imaging Aspects for Brachytherapy
8.4.1 Applicator Reconstruction Using MR and/or CT
8.4.2 CT-MR Registration
8.4.3 Ultrasound and Brachytherapy
8.4.4 Treatment Planning
8.5 Conclusion
References
9: Ultrasonography in Image-Guided Radiotherapy: Current Status and Future Challenges
9.1 Ultrasound-Guided Radiotherapy
9.2 Ultrasound Imaging Technology
9.2.1 Basic Principles of Ultrasound Imaging
9.2.2 Attenuation of Ultrasound
9.2.3 Speed of Sound
9.2.4 Ultrasound Transducers
9.2.5 Image Formation and Different Types of Imaging
9.3 Ultrasound in the Radiotherapy Workflow
9.4 Inter-Fractional and Intra-Fractional Organ Motion Estimation Using Ultrasound
9.5 Automatic Image Processing Applications
9.6 Future Directions
References
10: Means for Target Volume Delineation and Stabilisation: Fiducial Markers, Balloons and Others
10.1 Introduction
10.2 Image Guidance
10.2.1 Cone-Beam Computed Tomography
10.2.2 Ultrasound
10.2.3 MRI
10.3 Medical Devices
10.3.1 Radio-Opaque Fiducial Markers
10.3.2 Electromagnetic Transponders
10.3.3 Endo-Rectal Devices
10.3.4 Endo-Rectal Balloons
10.3.5 Endo-Rectal Displacement Device
10.3.6 Implanted Rectum Spacers
10.4 Summary
References
11: Artificial Intelligence in Radiation Oncology: A Rapidly Evolving Picture
11.1 Introduction
11.2 What Is AI?
11.3 Why Is Deep Learning Important for Radiation Oncology?
11.4 Application Areas for Deep Learning in Radiation Oncology
11.4.1 Image Segmentation for Treatment Planning
11.4.2 Computed Tomography and Cone-Beam Computed Tomography Segmentation
11.4.3 Magnetic Resonance Imaging Segmentation for MR-Guided Radiation Therapy
11.5 Toward AI-Guided Radiotherapy
11.6 Quality Assurance
11.7 Obstacles for AI and Deep Learning
11.7.1 Data Size Limitations
11.7.2 Generalizability on Unseen Data and Concept Drift
11.8 Other Areas in RO Likely to be Impacted by AI
11.9 Conclusion
References
Part III: Outcome Evaluation
12: Multi-Modality Imaging for Prediction of Tumor Control Following Radiotherapy
12.1 Introduction
12.2 Technical Requirements for the Use of Quantitative Imaging Biomarkers to Predict Radiotherapy Outcome
12.2.1 Availability
12.2.2 Technical Characteristics of Imaging Systems
12.2.3 Measurement Precision and Accuracy
12.2.4 Standardized Acquisition and Analysis
12.3 Quantitative Imaging Biomarkers for TC Prediction Following RT
12.3.1 Magnetic Resonance Imaging
12.3.2 Positron Emission Tomography
12.3.3 Combined PET/MR
12.3.4 Radiomics
12.4 Radiotherapy Interventions Based on QIB Prediction Models
References
13: Modelling for Radiation Treatment Outcome
13.1 Introduction
13.2 Basic Modelling Principles
13.2.1 Data
13.2.2 Data Analysis Strategy
13.2.3 Data Pre-Processing
13.2.4 Feature Selection
13.2.5 Model Training
13.2.6 Model Evaluation and Interpretation
13.2.7 Model Application
13.3 Introduction to TCP and NTCP Models
13.3.1 Poisson Model of Tumour Control Probability
13.3.2 Modelling of Normal Tissue Complication Probability
13.3.3 Application: Biological Treatment Plan Optimisation and Evaluation
13.4 Case 1: Patient Selection for Proton-Beam Therapy: The Model-Based Approach
13.4.1 Principles of the Model-Based Approach
13.4.1.1 Phase α: Model-Based Selection
13.4.1.2 Phase β: Model-Based Clinical Evaluation
13.4.2 Application: Proton-Beam Therapy for Head and Neck Cancer
13.5 Case 2: Radiomics
13.5.1 The Radiomics Workflow
13.5.2 Application: Radiomics for Adaptive Treatment
13.6 Summary and Outlook
References