Machine and Deep Learning in Oncology, Medical Physics and Radiology

Foreword to the First Edition
Preface to the First Edition
Preface to the Second Edition
Contents
Part I: Introduction to Machine and Deep Learning Principles
	1: What Are Machine and Deep Learning?
		1.1	 Overview
		1.2	 Background
		1.3	 Machine Learning Definition
		1.4	 Deep Learning Definition
		1.5	 Learning from Data
		1.6	 Overview of Machine and Deep Learning Approaches
		1.7	 Quantifying the Data and Learning Objectives
		1.8	 Application in Biomedicine
		1.9	 Applications in Radiology and Oncology
		1.10	 Ethical Challenges in the Application of Machine Learning
		1.11	 Steps to Machine Learning Heaven
		1.12	 Conclusions
		References
	2: Computational Learning Theory
		2.1	 Introduction
		2.2	 Computational Modeling Versus Statistics
		2.3	 Learning Capacity
		2.4	 PAC Learning
		2.5	 VC Dimension
		2.6	 Learning with Deep Learning
		2.7	 Model Complexity Analysis in Practice
			2.7.1	 Model Order Based on Information Theory
			2.7.2	 Model Order Based on Resampling Methods
		2.8	 Conclusions
		References
	3: Conventional Machine Learning Methods
		3.1	 Introduction
		3.2	 Unsupervised Learning
			3.2.1	 Linear Principal Component Analysis
			3.2.2	 Kernel Principal Component Analysis
			3.2.3	 Factor Analysis (FA)
			3.2.4	 Clustering
		3.3	 Supervised Learning
			3.3.1	 Logistic Regression
			3.3.2	 Feed-Forward Neural Networks (FFNN)
			3.3.3	 General Regression Neural Networks (GRNN)
			3.3.4	 Kernel-Based Methods
			3.3.5	 Decision Trees and Random Forests
			3.3.6	 Bayesian Network
			3.3.7	 Naive Bayes
		3.4	 Reinforcement Learning
			3.4.1	 Reinforcement Learning for Adaptive Liver Cancer Treatment
		References
	4: Overview of Deep Machine Learning Methods
		4.1	 Introduction
		4.2	 The Vanilla Neural Network
			4.2.1	 Training a Neural Network
			4.2.2	 Hyperparameters Associated with Training
			4.2.3	 What Makes a Neural Network Deep?
			4.2.4	 Example: Neural Network for Binary Classification
		4.3	 Autoencoders
		4.4	 Convolutional Neural Networks (CNNs)
			4.4.1	 Convolutions
			4.4.2	 Pooling
		4.5	 Recurrent Neural Networks
			4.5.1	 Long Short-Term Memory (LSTM)
			4.5.2	 Gated Recurrent Units (GRUs)
		4.6	 Generative Adversarial Networks (GANs)
			4.6.1	 Vanilla GANs
			4.6.2	 Common GAN Variants: DCGAN, WGAN
		4.7	 Deep Reinforcement Learning (DRL)
		4.8	 Current Challenges and Future Directions
		4.9	 Conclusion
		References
	5: Quantum Computing for Machine Learning
		5.1	 Introduction
		5.2	 Postulates of Quantum Mechanics
		5.3	 Quantum Hardware
			5.3.1	 Quantum Annealers
			5.3.2	 Universal Quantum Computers
		5.4	 Common Quantum Computing Algorithms
			5.4.1	 Grover’s Algorithm
			5.4.2	 Quantum Phase Estimation
			5.4.3	 Shor’s Algorithm
			5.4.4	 Quantum Machine Learning
				5.4.4.1	 Quantum Support Vector Machines
				5.4.4.2	 Quantum Principal Component Analysis
				5.4.4.3	 Quantum Bayesian Network
				5.4.4.4	 Quantum Neural Network and Deep Learning
				5.4.4.5	 Quantum Reinforcement Learning
		5.5	 Application of Quantum Computing in Medical Physics
			5.5.1	 Optimization and Planning
			5.5.2	 Outcome Modeling/Decision Making
		5.6	 Conclusion
		References
	6: Performance Evaluation
		6.1	 Standard Evaluation Methods for Machine Learning Systems
			6.1.1	 Choosing an Appropriate Performance Measure
				6.1.1.1	 Common Metrics Used in all Machine Learning Applications
				6.1.1.2	 Metrics Used Specifically in Medical Machine Learning Applications
				6.1.1.3	 Common Metrics Used in Computer Imaging Applications
			6.1.2	 Choosing an Appropriate Sampling Method
			6.1.3	 Choosing an Appropriate Statistical Testing Strategy
				6.1.3.1	 In the Context of a Single Classifier
				6.1.3.2	 In the Context of Several Classifiers
		6.2	 Standard Practice in Medical Imaging and Oncology
			6.2.1	 Review of the Current Practice in Medical Imaging and Oncology
			6.2.2	 Areas where Improvements Could Be Made
			6.2.3	 Lessons from the Past
		References
	7: Software Tools for Machine and Deep Learning
		7.1	 Introduction
		7.2	 Python-Based Machine Learning Library
			7.2.1	 Pip and Conda
			7.2.2	 NumPy and SciPy
			7.2.3	 Dedicated Machine Learning Libraries
				7.2.3.1	 Scikit-Learn
				7.2.3.2	 Shogun
				7.2.3.3	 mlpy
				7.2.3.4	 PyMVPA
				7.2.3.5	 MDP
				7.2.3.6	 PyBrain
			7.2.4	 Deep Learning
				7.2.4.1	 Theano
				7.2.4.2	 Chainer
				7.2.4.3	 TensorFlow
				7.2.4.4	 PyTorch
				7.2.4.5	 Caffe
				7.2.4.6	 MXNet
			7.2.5	 Examples
			7.2.6	 Benchmark
		7.3	 Weka
		7.4	 R
		7.5	 Matlab
		7.6	 Cloud-Based Platforms
			7.6.1	 AWS Deep Learning AMIs and SageMaker
			7.6.2	 Google Colab
			7.6.3	 Azure Machine Learning Studio
			7.6.4	 IBM Watson Machine Learning Studio
		7.7	 Conclusions
		References
	8: Privacy-Preserving Federated Data Analysis: Data Sharing, Protection, and Bioethics in Healthcare
		8.1	 Introduction
			8.1.1	 Data Landscape
				8.1.1.1	 Structured Data and Unstructured Data
				8.1.1.2	 Horizontally Partitioned Data and Vertically Partitioned Data
		8.2	 Prerequisites
			8.2.1	 Data Extraction
				8.2.1.1	 ETL Tooling and Data Warehousing
				8.2.1.2	 Image Biomarker Extraction
			8.2.2	 Data Representation and FAIR Data Principles
				8.2.2.1	 Relational Databases and Ontologies
				8.2.2.2	 Semantic Web, RDF, and Linked Data
					Resource Description Framework
					Unique Resource Identifiers and Linked Data
						Querying Using SPARQL
				8.2.2.3	 HL7 FHIR and REST-APIs
			8.2.3	 Network Infrastructure
				8.2.3.1	 Institutional Infrastructure
					Traditional ETL and DWH
					FAIR Data Store
					Traditional ETL and DWH with a FAIR Store
						Traditional ETL and DWH with a Virtual FAIR Store
						Virtual FAIR Store per Institute
						Virtual FAIR Store per Source and Institute
				8.2.3.2	 Machine Learning Infrastructure
					Centralized Machine Learning Infrastructure
					Distributed Machine Learning Infrastructure: The Personal Health Train
			8.2.4	 Centralized and Distributed Machine Learning Algorithms
				8.2.4.1	 Centralized Machine Learning
				8.2.4.2	 Distributed Machine Learning
					Horizontal Distributed (Federated) Machine Learning
					Vertical Distributed (Federated) Learning
			8.2.5	 Bioethics and Data Protection
				8.2.5.1	 Bioethics and Data Protection: Individuals
				8.2.5.2	 Bioethics and Data Protection: Data Entity
					Pseudonymization
					Data Obfuscation
					Data Perturbation
				8.2.5.3	 Bioethics and Data Protection: Society
			8.2.6	 Applications and Initiatives
				8.2.6.1	 Datashield
				8.2.6.2	 I2B2
				8.2.6.3	 VATE
				8.2.6.4	 PCORnet
				8.2.6.5	 FAIRHealth
				8.2.6.6	 Personal Health Train Initiatives
					EuroCAT
					20 K Challenge
			8.2.7	 Summary
		References
Part II: Machine Learning for Medical Image Analysis in Radiology and Oncology
	9: Computerized Detection of Lesions in Diagnostic Images with Early Deep Learning Models
		9.1	 Introduction
		9.2	 Overview of Architecture of a CADe Scheme
		9.3	 Machine Learning (ML) in CADe
			9.3.1	 Feature-Based (Segmented-Object-Based) ML (Classifiers)
			9.3.2	 Early Deep Learning Models
				9.3.2.1	 Overview
				9.3.2.2	 Difference Between Deep Learning and Feature-Based ML (Classifiers)
				9.3.2.3	 Early Deep Learning Model: Massive-Training Artificial Neural Network (MTANN)
		9.4	 CADe in Thoracic Imaging
			9.4.1	 Thoracic Imaging for Lung Cancer Detection
			9.4.2	 CADe of Lung Nodules in Thoracic CT
				9.4.2.1	 Overview
				9.4.2.2	 Illustration of a CADe Scheme
			9.4.3	 CADe of Lung Nodules in CXR
		9.5	 CADe in Colonic Imaging
			9.5.1	 Colonic Imaging for Colorectal Cancer Detection
			9.5.2	 Overview of CADe of Polyps in CTC
		9.6	 Summary
		References
	10: Classification of Malignant and Benign Tumors
		10.1	 Introduction
		10.2	 Overview of Classification Framework
			10.2.1	 Perception Modeling
			10.2.2	 Feature Extraction for Tumor Quantification
			10.2.3	 Design of Decision Function Using Machine Learning
			10.2.4	 Deep Learning Methods
			10.2.5	 CADx Classifier Training and Performance Evaluation
		10.3	 Application Examples in Mammography
			10.3.1	 Mammography
			10.3.2	 Detection of Clustered Microcalcifications in Mammograms
			10.3.3	 Computer-Aided Diagnosis (CADx) of Microcalcification Lesions in Mammograms
			10.3.4	 Adaptive CADx Boosted with Content-Based Image Retrieval (CBIR)
		10.4	 MDS as a Visualization Tool of Example Lesions
			10.4.1	 Multidimensional Scaling (MDS) Technique
			10.4.2	 Exploring Similar MC Lesions with MDS
		10.5	 Issues and Recommendations
		10.6	 Conclusions
		References
	11: Auto-contouring for Image-Guidance and Treatment Planning
		11.1	 Introduction
		11.2	 Traditional Auto-Segmentation Techniques
			11.2.1	 First-Generation Auto-Segmentation Techniques
			11.2.2	 Second-Generation Auto-Segmentation Techniques
			11.2.3	 Third-Generation Auto-Segmentation Techniques
		11.3	 Deep Learning-Based Auto-Segmentation
			11.3.1	 Convolutional Neural Networks and Fully Convolutional Networks
			11.3.2	 Popular Deep Learning Auto-Segmentation Architectures
		11.4	 Image Segmentation Packages and Publicly Available Datasets
			11.4.1	 Open-Source Image Segmentation Packages
			11.4.2	 Publicly Available Datasets
			11.4.3	 Commercial Systems
		11.5	 Auto-Segmentation Software Commissioning and Quality Assurance
			11.5.1	 Auto-Segmentation Evaluation
			11.5.2	 Patient-Specific Evaluations
			11.5.3	 Commissioning and QA
			11.5.4	 Current Limitations to Auto-Segmentation Algorithm Development and Implementation
		11.6	 Overview of State-of-the-Art Results in Medical Image Auto-Segmentation
			11.6.1	 Normal Tissues
				11.6.1.1	 Craniospinal
				11.6.1.2	 Head and Neck
				11.6.1.3	 Thoracic
				11.6.1.4	 Pelvis and Abdomen
			11.6.2	 Tumors and Clinical Target Volumes
				11.6.2.1	 Tumors
				11.6.2.2	 Clinical Target Volumes
		11.7	 Conclusion
		References
Part III: Machine Learning for Radiation Oncology Workflow
	12: Machine Learning Applications in Quality Assurance of Radiation Delivery
		12.1	 Introduction
		12.2	 Overview of the Use of Machine Learning in Quality Assurance and Treatment Delivery
			12.2.1	 Automated Chart Review
			12.2.2	 Machine Learning Applied to Delivery Systems
			12.2.3	 Machine Learning Applied to IMRT QA
		12.3	 Future Directions
		References
	13: Knowledge-Based Treatment Planning
		13.1	 Introduction
		13.2	 Anatomical Feature-Based KBP Model
			13.2.1	 Distance to Target Histogram
			13.2.2	 Model Training and Validation
		13.3	 A Robust Ensemble Model with Outlier Filtering Mechanism
			13.3.1	 An Ensemble KBP Model
			13.3.2	 Outlier Filtering
				13.3.2.1	 Anatomical Outliers and Dosimetric Outliers
				13.3.2.2	 Prediction Performance Measure
				13.3.2.3	 Model-Based Case Filtering Method
			13.3.3	 Retrospective Validation
		13.4	 A KBP Model for Multiple-PTV Plans
			13.4.1	 Generalized Distance to Target Histogram
			13.4.2	 Modeling with a gDTH-Based Similarity Metric
			13.4.3	 Data Augmentation
			13.4.4	 Training and Validation
		13.5	 Head and Neck Trade-off KBP Model
			13.5.1	 Plan Trade-off Modeling
			13.5.2	 Trade-off Simulation and Validation
		13.6	 A Complete Workflow for KBP Planning of Whole Breast Radiation Therapy
			13.6.1	 Digitally Reconstructed Radiograph (DRR)-Based Energy Selection
			13.6.2	 Anatomy-Driven Fluence Estimation
			13.6.3	 Patient-Specific Fluence Fine-Tuning
			13.6.4	 Planning Validation
				13.6.4.1	 Data Selection
				13.6.4.2	 Model Training and Validation
				13.6.4.3	 Plan Quality Comparison
				13.6.4.4	 Plan Efficiency
		13.7	 Beam Bouquet Knowledge Model for Lung IMRT Planning
			13.7.1	 Dissimilarity Metric between Two Beam Bouquets
			13.7.2	 Establishing the Standardized Beam Bouquets
			13.7.3	 Validation with Clinical Cases
		13.8	 Summary
		References
	14: Intelligent Respiratory Motion Management for Radiation Therapy Treatment
		14.1	 The Problem of Respiratory Movement During Radiotherapy
		14.2	 Dynamic Compensation Strategies during Delivery
		14.3	 Using an Artificial Neural Network (ANN) to Model and Predict Breathing Motion
		14.4	 Basic Neural Network Architecture for Correlation and Prediction
			14.4.1	 The Single Neuron, or Linear Filter
			14.4.2	 The Basic Feed-Forward Artificial Neural Network for Prediction
			14.4.3	 Training the Feed-Forward Network
			14.4.4	 The Recurrent Network
		14.5	 Performance of Basic Neural Networks to Predict Tumor Motion
			14.5.1	 Breathing Prediction Examples for a Simple Feed-Forward Network
		14.6	 Advanced Neural Network Architectures
			14.6.1	 Quadratic Neural Unit
			14.6.2	 Using a Kalman filter to Predict/Correct as Part of the Training Loop
			14.6.3	 A Network with Multiple Breathing Signal Inputs
			14.6.4	 Deep Learning Neural Networks for Prediction
		14.7	 Support Vector Regression (SVR) as an Alternative to Neural Networks for Breathing Prediction
		14.8	 Probabilistic Neural Networks
		14.9	 Summary
		References
Part IV: Machine Learning for Outcomes Modeling and Decision Support
	15: Prediction of Oncology Treatment Outcomes
		15.1	 Introduction
		15.2	 Outcome Modeling in Radiotherapy
		15.3	 Data Resources
			15.3.1	 Clinical Data
			15.3.2	 Dosimetric Data
			15.3.3	 Radiomics (Imaging Features)
			15.3.4	 Biological Markers
		15.4	 Database Technologies for Machine Learning in Oncology
		15.5	 Pan- Vs. P-OMICs
			15.5.1	 Spurious Relationship
			15.5.2	 Echo Chamber Effect
			15.5.3	 Yule–Simpson Paradox
			15.5.4	 Ghost Analytics
		15.6	 Modeling Methods
			15.6.1	 Bottom-up Approaches for Modeling Oncology Response
			15.6.2	 Top-Down Approaches for Modeling Oncology Response
				15.6.2.1	 Logistic Regression
					A Logistic Outcome Modeling Example
				15.6.2.2	 Machine Learning Methods
					A Machine Learning Outcome Modeling Example
		15.7	 Software Tools for Outcome Modeling
		15.8	 Discussion
		15.9	 Future Research Directions
		15.10	 Conclusion
		References
	16: Radiomics and Radiogenomics
		16.1	 Introduction
		16.2	 Technical Basis of Radiomics
		16.3	 Key Findings and Clinical Applications
		16.4	 Emerging Paradigms: Deep Learning
		16.5	 Radiogenomics: Integrating Imaging with Genomics
		16.6	 Current Challenges and Potential Solutions
			16.6.1	 Standardization and Quantitative Imaging
			16.6.2	 Reproducibility and Need for Prospective Validation
			16.6.3	 Data and Software Sharing
		16.7	 Conclusion and Future Outlook
		References
	17: Modelling of Radiotherapy Response (TCP/NTCP)
		17.1	 Introduction
			17.1.1	 General Considerations
		17.2	 Tumour Control Probability
		17.3	 Machine Learning for TCP Modelling
		17.4	 Example 1: Dosimetric and Clinical Variables
			17.4.1	 Data Set
			17.4.2	 Data Exploration
			17.4.3	 Logistic Regression Modelling Example
			17.4.4	 Kernel-Based Modelling Example
			17.4.5	 Comparison with Other Known Models
		17.5	 Use of Imaging Features
		17.6	 Use of Biological Markers
		17.7	 NTCP Modelling
			17.7.1	 NTCP Models
			17.7.2	 Dosimetric Data Reduction-Summary Measure
		17.8	 Machine Learning Approaches to NTCP Modelling
			17.8.1	 Multivariable Logistic Regression
			17.8.2	 Feature Selection
		17.9	 Classical Machine Learning Approaches
			17.9.1	 Artificial Neural Networks
			17.9.2	 Support Vector Machines (SVM)
			17.9.3	 Unsupervised Learning SOM
			17.9.4	 Bayesian Networks
			17.9.5	 Decision Trees
			17.9.6	 Random Forests
			17.9.7	 Hybrid Models and Comparative Studies
		17.10	 Deep Learning
		17.11	 Radiomics and Dosiomics
		17.12	 Radiogenomics
		17.13	 Challenges Modelling Radiotherapy Response
		17.14	 Summary
		17.15	 Conclusions
		References
	18: Smart Adaptive Treatment Strategies
		18.1	 Introduction
		18.2	 Adaptive Treatment in Radiotherapy
		18.3	 What Knowledge Is Needed for ACT?
			18.3.1	 Clinical Data
			18.3.2	 Treatment Data
			18.3.3	 Imaging Data
			18.3.4	 Biological Data
		18.4	 How to Develop Outcome Models Using This Knowledge?
		18.5	 How to Optimize Adaptation?
			18.5.1	 Classical MDP/RL Learning
			18.5.2	 Deep MDP/RL Learning
		18.6	 ACT Example in Radiotherapy
		18.7	 Discussion and Recommendation
		18.8	 Conclusions
		References
	19: Artificial Intelligence in Clinical Trials
		19.1	 Introduction
			19.1.1	 Background on Clinical Trials in Oncology and Radiology
			19.1.2	 Clinical Trials as the Gold Standard for Clinical Practice
			19.1.3	 Why Do Clinical Trials Fail?
		19.2	 Types of Clinical Trial Design
			19.2.1	 Adaptive Clinical Trials
		19.3	 Artificial Intelligence and Clinical Trial Design
			19.3.1	 Need for Artificial Intelligence in Clinical Trial Design
			19.3.2	 The Multiple Roles of Artificial Intelligence (AI) in Clinical Trial Design
			19.3.3	 Challenges for Artificial Intelligence in Clinical Trial Design
			19.3.4	 Example Application of Artificial Intelligence in Trial Design (SMART)
		19.4	 Discussion and Recommendations
		19.5	 Conclusions
		References
Index