Handbook of Machine Learning in Oncology
Foundations and Advanced Techniques
- 1st Edition - June 1, 2027
- Latest edition
- Authors: Sanjay Saxena, Jasjit S. Suri
- Language: English
Machine learning (ML) is transforming oncology by introducing innovative methodologies for early detection, diagnosis, treatment planning, and predicting patient outcomes.… Read more
Description
Description
Machine learning (ML) is transforming oncology by introducing innovative methodologies for early detection, diagnosis, treatment planning, and predicting patient outcomes. Handbook of Machine Learning in Oncology: Foundations and Advanced Techniques provides a detailed guide for applying ML-based methods to cancer research and treatment. The first half of this comprehensive guide covers the basics of cancer biology and ML, including key concepts, algorithms, and data processing techniques. It presents the methodology for supervised and unsupervised learning, Deep Learning, radiomics, radiogenomics, and natural language processing, highlighting their importance in oncology. The second half of the book focuses on practical applications, offering detailed case studies on various cancers such as breast, lung, prostate, colorectal, brain, and pancreatic. This applied handbook discusses predictive modeling, diagnostic applications, treatment planning, and emerging technologies. The book offers a thorough and insightful exploration of how Machine Learning and Deep Learning are revolutionizing oncology, providing readers with the knowledge and tools to contribute to this rapidly evolving field.
Key features
Key features
- Includes coverage of foundational concepts of Machine Learning and Deep Learning, as they relate to modelling and AI algorithms for imaging, diagnosis and predictive modeling in oncology
- Provides in-depth technical coverage of machine learning techniques such as supervised and unsupervised learning, neural networks, reinforcement learning, support vector machines, and natural language processing
- Presents crucial technologies and software platforms, along with advanced techniques such as radiomics, radiogenomics, and omics approaches
- Provides in-depth technical coverage of diagnostic, prognostic, and classification, predictive, and treatment applications of Machine Learning across a range of cancer types and organ systems
Readership
Readership
Graduate students and researchers in the fields of bioinformatics, machine learning, biomedical engineering, and computer science, as well as researchers and academics interested in integrating machine learning into cancer research
Table of contents
Table of contents
Section 1: Introduction and Basics
1. Introduction to Machine Learning and Oncology
1.1. Overview of Cancer Biology
1.2. Historical Perspective on Cancer Treatment
1.3. Introduction to Machine Learning
1.4. Importance of Machine Learning in Oncology
1.5. Key Objectives and Structure of the Book
1.6. Overview of Machine Learning Algorithms
1.7. Integration of Machine Learning in Oncology Workflows
1.8. Current Challenges in Oncology and How ML Can Help
1.9. Case Studies Highlighting ML Impact in Oncology
1.10. Future Prospects of Machine Learning in Cancer Research
2. Fundamentals of Machine Learning
2.1. Introduction to Supervised Learning
2.2. Introduction to Unsupervised Learning
2.3. Introduction to Reinforcement Learning
2.4. Common Algorithms in Supervised Learning (SVM, Decision Trees, Neural Networks)
2.5. Common Algorithms in Unsupervised Learning (Clustering, Dimensionality Reduction)
2.6. Evaluation Metrics for ML Models
2.7. Model Validation Techniques
2.8. Feature Engineering and Selection
2.9. Overfitting and Underfitting
2.10. Practical Considerations in ML Model Development
3. Neuroscience Data Types
3.1. Types of Cancer Data: Genomic, Imaging, Clinical
3.2. Genomic Data: Collection, Annotation, and Processing
3.3. Imaging Data: MRI, CT, PET, and X-rays
3.4. Clinical Data: Electronic Health Records, Pathology Reports
3.5. Data Collection Methods
3.6. Data Annotation Techniques
3.7. Data Cleaning and Normalization
3.8. Feature Selection and Extraction
3.9. Data Integration from Multiple Sources
3.10. Challenges and Best Practices in Data Management
Section 2: Supervised and Unsupervised Learning
4. Supervised Learning Techniques
4.1. Introduction to Classification Algorithms
4.2. Support Vector Machines (SVM) in Oncology
4.3. Decision Trees and Random Forests
4.4. Neural Networks and Deep Learning for Cancer Diagnosis
4.5. Regression Models for Predictive Analysis
4.6. Case Studies: Predictive Modelling for Cancer Outcomes
4.7. Model Evaluation and Performance Metrics
4.8. Cross-Validation Techniques
4.9. Feature Importance and Model Interpretability
4.10. Practical Applications in Cancer Research
5. Unsupervised Learning Techniques
5.1. Introduction to Clustering Methods
5.2. K-means Clustering in Cancer Research
5.3. Hierarchical Clustering Techniques
5.4. Dimensionality Reduction Techniques (PCA, t-SNE)
5.5. Patient Stratification Using Clustering
5.6. Biomarker Discovery through Unsupervised Learning
5.7. Case Studies: Applications in Oncology
5.8. Evaluating Unsupervised Learning Models
5.9. Practical Considerations and Challenges
5.10. Future Directions in Unsupervised Learning
6. Deep Learning Fundamentals
6.1. Introduction to Neural Networks
6.2. Convolutional Neural Networks (CNNs) for Imaging Data
6.3. Recurrent Neural Networks (RNNs) for Genomic Data Analysis
6.4. Autoencoders and Their Applications in Oncology
6.5. Transfer Learning and Pre-trained Models
6.6. Case Studies: Deep Learning in Cancer Research
6.7. Training Deep Learning Models: Best Practices
6.8. Model Evaluation and Performance Metrics
6.9. Overcoming Challenges in Deep Learning
6.10. Future Trends in Deep Learning for Oncology
Section 3: Advanced Techniques and Applications
7. Radiomics and Radiogenomics
7.1. Introduction to Radiomics
7.2. Feature Extraction from Medical Images
7.3. Linking Imaging Features with Genomic Data
7.4. Clinical Applications of Radiomics
7.5. Case Studies in Radiogenomics
7.6. Workflow for Radiomic Analysis
7.7. Machine Learning Models in Radiomics
7.8. Evaluating Radiomics Models
7.9. Challenges and Solutions in Radiomics
7.10. Future Directions in Radiogenomics
8. Natural Language Processing in Cancer Research
8.1. Introduction to Natural Language Processing (NLP)
8.2. Text Mining Techniques for Clinical Notes
8.3. Analyzing Research Publications Using NLP
8.4. Automated Diagnosis through NLP
8.5. Literature Review Automation
8.6. Case Studies: NLP Applications in Oncology
8.7. Challenges in NLP for Medical Data
8.8. Best Practices for NLP Model Development
8.9. Evaluating NLP Models
8.10. Future Trends in NLP for Cancer Research
9. Integrative Omics Approaches
9.1. Introduction to Omics Data
9.2. Genomic Data Integration
9.3. Transcriptomic Data Analysis
9.4. Proteomic Data Integration
9.5. Metabolomic Data Analysis
9.6. Multi-Omics Data Integration Techniques
9.7. Case Studies in Precision Oncology
9.8. Machine Learning Models for Multi-Omics Data
9.9. Challenges in Integrative Omics
9.10. Future Directions in Omics Research
Section 4: Ethical Considerations and Future Directions
10. Ethical and Practical Considerations
10.1. Data Privacy and Security in ML Models
10.2. Ethical Issues in Machine Learning for Oncology
10.3. Bias and Fairness in Machine Learning Models
10.4. Regulatory and Compliance Issues
10.5. Ensuring Patient Consent and Data Anonymization
10.6. Addressing Disparities in Cancer Research
10.7. Case Studies on Ethical Considerations
10.8. Best Practices for Ethical Machine Learning
10.9. Legal Considerations in ML for Healthcare
10.10. Future Directions in Ethical Machine Learning
11. Future Directions in Machine Learning for Cancer Research
11.1. Emerging Trends in Machine Learning
11.2. Integration of AI with Other Technologies (IoT, Blockchain)
11.3. Advances in Computational Techniques
11.4. Machine Learning in Precision Medicine
11.5. Case Studies of Innovative Applications
11.6. Future Research Opportunities and Challenges
11.7. Collaborative Research and Data Sharing
11.8. Innovations in Cancer Treatment Planning
11.9. The Role of AI in Early Cancer Detection
11.10. The Future of Machine Learning in Oncology
Section 5: Predictive Modelling and Diagnostic Applications
12. Predictive Modelling for Cancer Diagnosis and Prognosis
12.1. Building Predictive Models
12.2. Evaluating Predictive Models
12.3. Case Studies: Predicting Cancer Onset
12.4. Case Studies: Classifying Cancer Stages
12.5. Applications: Risk Assessment
12.6. Applications: Early Detection
12.7. Challenges in Predictive Modelling
12.8. Best Practices for Model Development
12.9. Model Validation Techniques
12.10. Future Directions in Predictive Modelling
13. Diagnostic Applications of Machine Learning
13.1. Imaging-Based Diagnosis: MRI, CT, X-rays
13.2. Molecular and Genetic Diagnosis
13.3. Tumor Classification Techniques
13.4. Metastasis Detection
13.5. Case Studies: Diagnostic Applications
13.6. Challenges in Diagnostic ML Applications
13.7. Best Practices for Diagnostic Model Development
13.8. Integrating ML into Clinical Workflows
13.9. Evaluating Diagnostic Models
13.10. Future Trends in Diagnostic Applications
14. Survival Analysis and Prognostic Models
14.1. Techniques for Survival Analysis
14.2. Predicting Patient Outcomes
14.3. Case Studies: Survival Prediction for Different Cancer Types
14.4. Challenges in Survival Analysis
14.5. Best Practices for Prognostic Model Development
14.6. Evaluating Prognostic Models
14.7. Integrating Prognostic Models into Clinical Practice
14.8. Case Studies: Prognostic Applications
14.9. Future Directions in Survival Analysis
14.10. Advances in Prognostic Modelling Techniques
Section 6: Treatment Planning and Response Prediction
15. Personalized Treatment Recommendations
15.1. Building Treatment Planning Models
15.2. Predicting Treatment Outcomes
15.3. Predicting Treatment Toxicity
15.4. Case Studies: Chemotherapy
15.5. Case Studies: Radiation Therapy
15.6. Challenges in Treatment Planning
15.7. Best Practices for Personalized Treatment
15.8. Integrating ML into Treatment Planning
15.9. Evaluating Treatment Planning Models
15.10. Future Directions in Personalized Treatment
16. Breast Cancer: Diagnosis, Classification, and Predictive Models for Treatment Response
16.1. Diagnosis Using Imaging and Genomic Data
16.2. Classification Techniques for Breast Cancer
16.3. Predictive Models for Treatment Response
16.4. Case Studies: Early Detection
16.5. Case Studies: Survival Prediction
16.6. Challenges in Breast Cancer Modelling
16.7. Best Practices for Breast Cancer Research
16.8. Integrating ML in Breast Cancer Treatment
16.9. Evaluating Breast Cancer Models
16.10. Future Directions in Breast Cancer Research
17. Lung Cancer: Machine Learning in Imaging and Personalized Treatment
17.1. ML in CT and PET Imaging
17.2. Molecular Subtypes and Personalized Treatment
17.3. Tumor Segmentation Techniques
17.4. Outcome Prediction Models
17.5. Case Studies: Lung Cancer Applications
17.6. Challenges in Lung Cancer Modelling
17.7. Best Practices for Lung Cancer Research
17.8. Integrating ML in Lung Cancer Treatment
17.9. Evaluating Lung Cancer Models
17.10. Future Directions in Lung Cancer Research
18. Prostate Cancer: MRI-based Diagnosis and Predictive Models for Treatment Planning
18.1. MRI-based Diagnosis Techniques
18.2. Risk Stratification Models
18.3. Predictive Models for Treatment Planning
18.4. Case Studies: Active Surveillance
18.5. Case Studies: Prognosis
18.6. Challenges in Prostate Cancer Modelling
18.7. Best Practices for Prostate Cancer Research
18.8. Integrating ML in Prostate Cancer Treatment
18.9. Evaluating Prostate Cancer Models
18.10. Future Directions in Prostate Cancer Research
19. Colorectal Cancer: Imaging Analysis and Integrative Genomic Approaches
19.1. Endoscopic Imaging Techniques
19.2. Genomic Data Integration
19.3. Predictive Models for Early Detection
19.4. Recurrence Prediction Techniques
19.5. Case Studies: Colorectal Cancer Applications
19.6. Challenges in Colorectal Cancer Modelling
19.7. Best Practices for Colorectal Cancer Research
19.8. Integrating ML in Colorectal Cancer Treatment
19.9. Evaluating Colorectal Cancer Models
19.10. Future Directions in Colorectal Cancer Research
Section 7: Advanced Cancer Applications
20. Brain Cancer: MRI and fMRI Analysis, Genomic and Radiomic Data Integration
20.1. MRI Analysis Techniques
20.2. fMRI Analysis Techniques
20.3. Genomic Data Integration for Brain Cancer
20.4. Radiomic Data Integration for Brain Cancer
20.5. Case Studies: Glioma Classification
20.6. Case Studies: Survival Analysis
20.7. Challenges in Brain Cancer Modelling
20.8. Best Practices for Brain Cancer Research
20.9. Integrating ML in Brain Cancer Treatment
20.10. Future Directions in Brain Cancer Research
21. Pancreatic Cancer: Early Detection, Diagnosis, and Predictive Models
21.1. Early Detection Techniques
21.2. Imaging Data Analysis
21.3. Predictive Models for Surgical Outcomes
21.4. Prognostic Biomarkers Identification
21.5. Case Studies: Pancreatic Cancer Applications
21.6. Challenges in Pancreatic Cancer Modelling
21.7. Best Practices for Pancreatic Cancer Research
21.8. Integrating ML in Pancreatic Cancer Treatment
21.9. Evaluating Pancreatic Cancer Models
21.10. Future Directions in Pancreatic Cancer Research
22. Applications in Less Common Cancers
22.1. ML Applications in Rare Cancers
22.2. Predictive Models for Rare Cancers
22.3. Diagnostic Tools for Less Common Cancers
22.4. Case Studies: Advances in Rare Cancers
22.5. Challenges in Rare Cancer Modelling
22.6. Best Practices for Rare Cancer Research
22.7. Integrating ML in Rare Cancer Treatment
22.8. Evaluating Models for Less Common Cancers
22.9. Future Directions in Rare Cancer Research
22.10. Innovations in Rare Cancer Applications
23. Brain-Computer Interfaces in Oncology
23.1. Overview of Brain-Computer Interfaces (BCIs)
23.2. ML Techniques for BCIs
23.3. Case Studies: Communication Aids for Cancer Patients
23.4. Challenges in BCI Development for Oncology
23.5. Best Practices for BCI Research
23.6. Integrating ML in BCI Systems
23.7. Evaluating BCI Models
23.8. Future Directions in BCI Research
23.9. Innovations in BCI Applications for Oncology
23.10. Case Studies: Advances in BCI for Cancer Care
Section 8: Clinical Trials and Future Innovations
24. AI in Cancer Care Delivery
24.1. Workflow Automation in Oncology
24.2. Virtual Assistants in Cancer Care
24.3. Decision Support Systems
24.4. Implementing AI in Clinical Settings
24.5. Case Studies: AI in Cancer Care
24.6. Challenges in AI Implementation
24.7. Best Practices for AI in Oncology
24.8. Evaluating AI Systems in Clinical Use
24.9. Future Directions in AI for Cancer Care
24.10. Innovations in AI-driven Cancer Care
25. Clinical Trials and Translational Research
25.1. Machine Learning in Clinical Trial Design
25.2. Patient Recruitment and Monitoring
25.3. Case Studies of Successful Implementations
25.4. Challenges in ML for Clinical Trials
25.5. Best Practices for ML in Translational Research
25.6. Evaluating ML Models in Clinical Trials
25.7. Integrating ML into Clinical Trial Workflows
25.8. Future Directions in ML for Clinical Trials
25.9. Innovations in Translational Research
25.10. Case Studies: Advances in Clinical Trials
26. Future Directions and Innovations
26.1. Advances in Computational Techniques
26.2. Integration with Emerging Technologies (VR/AR, Robotics)
26.3. Future Research Challenges and Opportunities
26.4. Innovations in Cancer Research Methodologies
26.5. Collaborative Research and Data Sharing
26.6. Case Studies of Innovative Applications
26.7. Future Trends in Machine Learning for Oncology
26.8. The Role of AI in Future Cancer Research
26.9. Innovations in Cancer Treatment Planning
26.10. Future Prospects for ML in Oncology
27. Conclusion and Final Comments
27.1. Summary of Key Applications
27.2. Impact of Machine Learning on Cancer Research and Treatment
27.3. Encouragement for Continued Innovation and Research
27.4. Final Thoughts and Insights
27.5. Resources for Further Learning
27.6. Future Prospects for Researchers and Practitioners
27.7. Addressing Ongoing Challenges in Oncology
27.8. The Role of Interdisciplinary Research
27.9. Building a Collaborative Research Environment
27.10. Closing Remarks and Vision for the Future
1. Introduction to Machine Learning and Oncology
1.1. Overview of Cancer Biology
1.2. Historical Perspective on Cancer Treatment
1.3. Introduction to Machine Learning
1.4. Importance of Machine Learning in Oncology
1.5. Key Objectives and Structure of the Book
1.6. Overview of Machine Learning Algorithms
1.7. Integration of Machine Learning in Oncology Workflows
1.8. Current Challenges in Oncology and How ML Can Help
1.9. Case Studies Highlighting ML Impact in Oncology
1.10. Future Prospects of Machine Learning in Cancer Research
2. Fundamentals of Machine Learning
2.1. Introduction to Supervised Learning
2.2. Introduction to Unsupervised Learning
2.3. Introduction to Reinforcement Learning
2.4. Common Algorithms in Supervised Learning (SVM, Decision Trees, Neural Networks)
2.5. Common Algorithms in Unsupervised Learning (Clustering, Dimensionality Reduction)
2.6. Evaluation Metrics for ML Models
2.7. Model Validation Techniques
2.8. Feature Engineering and Selection
2.9. Overfitting and Underfitting
2.10. Practical Considerations in ML Model Development
3. Neuroscience Data Types
3.1. Types of Cancer Data: Genomic, Imaging, Clinical
3.2. Genomic Data: Collection, Annotation, and Processing
3.3. Imaging Data: MRI, CT, PET, and X-rays
3.4. Clinical Data: Electronic Health Records, Pathology Reports
3.5. Data Collection Methods
3.6. Data Annotation Techniques
3.7. Data Cleaning and Normalization
3.8. Feature Selection and Extraction
3.9. Data Integration from Multiple Sources
3.10. Challenges and Best Practices in Data Management
Section 2: Supervised and Unsupervised Learning
4. Supervised Learning Techniques
4.1. Introduction to Classification Algorithms
4.2. Support Vector Machines (SVM) in Oncology
4.3. Decision Trees and Random Forests
4.4. Neural Networks and Deep Learning for Cancer Diagnosis
4.5. Regression Models for Predictive Analysis
4.6. Case Studies: Predictive Modelling for Cancer Outcomes
4.7. Model Evaluation and Performance Metrics
4.8. Cross-Validation Techniques
4.9. Feature Importance and Model Interpretability
4.10. Practical Applications in Cancer Research
5. Unsupervised Learning Techniques
5.1. Introduction to Clustering Methods
5.2. K-means Clustering in Cancer Research
5.3. Hierarchical Clustering Techniques
5.4. Dimensionality Reduction Techniques (PCA, t-SNE)
5.5. Patient Stratification Using Clustering
5.6. Biomarker Discovery through Unsupervised Learning
5.7. Case Studies: Applications in Oncology
5.8. Evaluating Unsupervised Learning Models
5.9. Practical Considerations and Challenges
5.10. Future Directions in Unsupervised Learning
6. Deep Learning Fundamentals
6.1. Introduction to Neural Networks
6.2. Convolutional Neural Networks (CNNs) for Imaging Data
6.3. Recurrent Neural Networks (RNNs) for Genomic Data Analysis
6.4. Autoencoders and Their Applications in Oncology
6.5. Transfer Learning and Pre-trained Models
6.6. Case Studies: Deep Learning in Cancer Research
6.7. Training Deep Learning Models: Best Practices
6.8. Model Evaluation and Performance Metrics
6.9. Overcoming Challenges in Deep Learning
6.10. Future Trends in Deep Learning for Oncology
Section 3: Advanced Techniques and Applications
7. Radiomics and Radiogenomics
7.1. Introduction to Radiomics
7.2. Feature Extraction from Medical Images
7.3. Linking Imaging Features with Genomic Data
7.4. Clinical Applications of Radiomics
7.5. Case Studies in Radiogenomics
7.6. Workflow for Radiomic Analysis
7.7. Machine Learning Models in Radiomics
7.8. Evaluating Radiomics Models
7.9. Challenges and Solutions in Radiomics
7.10. Future Directions in Radiogenomics
8. Natural Language Processing in Cancer Research
8.1. Introduction to Natural Language Processing (NLP)
8.2. Text Mining Techniques for Clinical Notes
8.3. Analyzing Research Publications Using NLP
8.4. Automated Diagnosis through NLP
8.5. Literature Review Automation
8.6. Case Studies: NLP Applications in Oncology
8.7. Challenges in NLP for Medical Data
8.8. Best Practices for NLP Model Development
8.9. Evaluating NLP Models
8.10. Future Trends in NLP for Cancer Research
9. Integrative Omics Approaches
9.1. Introduction to Omics Data
9.2. Genomic Data Integration
9.3. Transcriptomic Data Analysis
9.4. Proteomic Data Integration
9.5. Metabolomic Data Analysis
9.6. Multi-Omics Data Integration Techniques
9.7. Case Studies in Precision Oncology
9.8. Machine Learning Models for Multi-Omics Data
9.9. Challenges in Integrative Omics
9.10. Future Directions in Omics Research
Section 4: Ethical Considerations and Future Directions
10. Ethical and Practical Considerations
10.1. Data Privacy and Security in ML Models
10.2. Ethical Issues in Machine Learning for Oncology
10.3. Bias and Fairness in Machine Learning Models
10.4. Regulatory and Compliance Issues
10.5. Ensuring Patient Consent and Data Anonymization
10.6. Addressing Disparities in Cancer Research
10.7. Case Studies on Ethical Considerations
10.8. Best Practices for Ethical Machine Learning
10.9. Legal Considerations in ML for Healthcare
10.10. Future Directions in Ethical Machine Learning
11. Future Directions in Machine Learning for Cancer Research
11.1. Emerging Trends in Machine Learning
11.2. Integration of AI with Other Technologies (IoT, Blockchain)
11.3. Advances in Computational Techniques
11.4. Machine Learning in Precision Medicine
11.5. Case Studies of Innovative Applications
11.6. Future Research Opportunities and Challenges
11.7. Collaborative Research and Data Sharing
11.8. Innovations in Cancer Treatment Planning
11.9. The Role of AI in Early Cancer Detection
11.10. The Future of Machine Learning in Oncology
Section 5: Predictive Modelling and Diagnostic Applications
12. Predictive Modelling for Cancer Diagnosis and Prognosis
12.1. Building Predictive Models
12.2. Evaluating Predictive Models
12.3. Case Studies: Predicting Cancer Onset
12.4. Case Studies: Classifying Cancer Stages
12.5. Applications: Risk Assessment
12.6. Applications: Early Detection
12.7. Challenges in Predictive Modelling
12.8. Best Practices for Model Development
12.9. Model Validation Techniques
12.10. Future Directions in Predictive Modelling
13. Diagnostic Applications of Machine Learning
13.1. Imaging-Based Diagnosis: MRI, CT, X-rays
13.2. Molecular and Genetic Diagnosis
13.3. Tumor Classification Techniques
13.4. Metastasis Detection
13.5. Case Studies: Diagnostic Applications
13.6. Challenges in Diagnostic ML Applications
13.7. Best Practices for Diagnostic Model Development
13.8. Integrating ML into Clinical Workflows
13.9. Evaluating Diagnostic Models
13.10. Future Trends in Diagnostic Applications
14. Survival Analysis and Prognostic Models
14.1. Techniques for Survival Analysis
14.2. Predicting Patient Outcomes
14.3. Case Studies: Survival Prediction for Different Cancer Types
14.4. Challenges in Survival Analysis
14.5. Best Practices for Prognostic Model Development
14.6. Evaluating Prognostic Models
14.7. Integrating Prognostic Models into Clinical Practice
14.8. Case Studies: Prognostic Applications
14.9. Future Directions in Survival Analysis
14.10. Advances in Prognostic Modelling Techniques
Section 6: Treatment Planning and Response Prediction
15. Personalized Treatment Recommendations
15.1. Building Treatment Planning Models
15.2. Predicting Treatment Outcomes
15.3. Predicting Treatment Toxicity
15.4. Case Studies: Chemotherapy
15.5. Case Studies: Radiation Therapy
15.6. Challenges in Treatment Planning
15.7. Best Practices for Personalized Treatment
15.8. Integrating ML into Treatment Planning
15.9. Evaluating Treatment Planning Models
15.10. Future Directions in Personalized Treatment
16. Breast Cancer: Diagnosis, Classification, and Predictive Models for Treatment Response
16.1. Diagnosis Using Imaging and Genomic Data
16.2. Classification Techniques for Breast Cancer
16.3. Predictive Models for Treatment Response
16.4. Case Studies: Early Detection
16.5. Case Studies: Survival Prediction
16.6. Challenges in Breast Cancer Modelling
16.7. Best Practices for Breast Cancer Research
16.8. Integrating ML in Breast Cancer Treatment
16.9. Evaluating Breast Cancer Models
16.10. Future Directions in Breast Cancer Research
17. Lung Cancer: Machine Learning in Imaging and Personalized Treatment
17.1. ML in CT and PET Imaging
17.2. Molecular Subtypes and Personalized Treatment
17.3. Tumor Segmentation Techniques
17.4. Outcome Prediction Models
17.5. Case Studies: Lung Cancer Applications
17.6. Challenges in Lung Cancer Modelling
17.7. Best Practices for Lung Cancer Research
17.8. Integrating ML in Lung Cancer Treatment
17.9. Evaluating Lung Cancer Models
17.10. Future Directions in Lung Cancer Research
18. Prostate Cancer: MRI-based Diagnosis and Predictive Models for Treatment Planning
18.1. MRI-based Diagnosis Techniques
18.2. Risk Stratification Models
18.3. Predictive Models for Treatment Planning
18.4. Case Studies: Active Surveillance
18.5. Case Studies: Prognosis
18.6. Challenges in Prostate Cancer Modelling
18.7. Best Practices for Prostate Cancer Research
18.8. Integrating ML in Prostate Cancer Treatment
18.9. Evaluating Prostate Cancer Models
18.10. Future Directions in Prostate Cancer Research
19. Colorectal Cancer: Imaging Analysis and Integrative Genomic Approaches
19.1. Endoscopic Imaging Techniques
19.2. Genomic Data Integration
19.3. Predictive Models for Early Detection
19.4. Recurrence Prediction Techniques
19.5. Case Studies: Colorectal Cancer Applications
19.6. Challenges in Colorectal Cancer Modelling
19.7. Best Practices for Colorectal Cancer Research
19.8. Integrating ML in Colorectal Cancer Treatment
19.9. Evaluating Colorectal Cancer Models
19.10. Future Directions in Colorectal Cancer Research
Section 7: Advanced Cancer Applications
20. Brain Cancer: MRI and fMRI Analysis, Genomic and Radiomic Data Integration
20.1. MRI Analysis Techniques
20.2. fMRI Analysis Techniques
20.3. Genomic Data Integration for Brain Cancer
20.4. Radiomic Data Integration for Brain Cancer
20.5. Case Studies: Glioma Classification
20.6. Case Studies: Survival Analysis
20.7. Challenges in Brain Cancer Modelling
20.8. Best Practices for Brain Cancer Research
20.9. Integrating ML in Brain Cancer Treatment
20.10. Future Directions in Brain Cancer Research
21. Pancreatic Cancer: Early Detection, Diagnosis, and Predictive Models
21.1. Early Detection Techniques
21.2. Imaging Data Analysis
21.3. Predictive Models for Surgical Outcomes
21.4. Prognostic Biomarkers Identification
21.5. Case Studies: Pancreatic Cancer Applications
21.6. Challenges in Pancreatic Cancer Modelling
21.7. Best Practices for Pancreatic Cancer Research
21.8. Integrating ML in Pancreatic Cancer Treatment
21.9. Evaluating Pancreatic Cancer Models
21.10. Future Directions in Pancreatic Cancer Research
22. Applications in Less Common Cancers
22.1. ML Applications in Rare Cancers
22.2. Predictive Models for Rare Cancers
22.3. Diagnostic Tools for Less Common Cancers
22.4. Case Studies: Advances in Rare Cancers
22.5. Challenges in Rare Cancer Modelling
22.6. Best Practices for Rare Cancer Research
22.7. Integrating ML in Rare Cancer Treatment
22.8. Evaluating Models for Less Common Cancers
22.9. Future Directions in Rare Cancer Research
22.10. Innovations in Rare Cancer Applications
23. Brain-Computer Interfaces in Oncology
23.1. Overview of Brain-Computer Interfaces (BCIs)
23.2. ML Techniques for BCIs
23.3. Case Studies: Communication Aids for Cancer Patients
23.4. Challenges in BCI Development for Oncology
23.5. Best Practices for BCI Research
23.6. Integrating ML in BCI Systems
23.7. Evaluating BCI Models
23.8. Future Directions in BCI Research
23.9. Innovations in BCI Applications for Oncology
23.10. Case Studies: Advances in BCI for Cancer Care
Section 8: Clinical Trials and Future Innovations
24. AI in Cancer Care Delivery
24.1. Workflow Automation in Oncology
24.2. Virtual Assistants in Cancer Care
24.3. Decision Support Systems
24.4. Implementing AI in Clinical Settings
24.5. Case Studies: AI in Cancer Care
24.6. Challenges in AI Implementation
24.7. Best Practices for AI in Oncology
24.8. Evaluating AI Systems in Clinical Use
24.9. Future Directions in AI for Cancer Care
24.10. Innovations in AI-driven Cancer Care
25. Clinical Trials and Translational Research
25.1. Machine Learning in Clinical Trial Design
25.2. Patient Recruitment and Monitoring
25.3. Case Studies of Successful Implementations
25.4. Challenges in ML for Clinical Trials
25.5. Best Practices for ML in Translational Research
25.6. Evaluating ML Models in Clinical Trials
25.7. Integrating ML into Clinical Trial Workflows
25.8. Future Directions in ML for Clinical Trials
25.9. Innovations in Translational Research
25.10. Case Studies: Advances in Clinical Trials
26. Future Directions and Innovations
26.1. Advances in Computational Techniques
26.2. Integration with Emerging Technologies (VR/AR, Robotics)
26.3. Future Research Challenges and Opportunities
26.4. Innovations in Cancer Research Methodologies
26.5. Collaborative Research and Data Sharing
26.6. Case Studies of Innovative Applications
26.7. Future Trends in Machine Learning for Oncology
26.8. The Role of AI in Future Cancer Research
26.9. Innovations in Cancer Treatment Planning
26.10. Future Prospects for ML in Oncology
27. Conclusion and Final Comments
27.1. Summary of Key Applications
27.2. Impact of Machine Learning on Cancer Research and Treatment
27.3. Encouragement for Continued Innovation and Research
27.4. Final Thoughts and Insights
27.5. Resources for Further Learning
27.6. Future Prospects for Researchers and Practitioners
27.7. Addressing Ongoing Challenges in Oncology
27.8. The Role of Interdisciplinary Research
27.9. Building a Collaborative Research Environment
27.10. Closing Remarks and Vision for the Future
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 1, 2027
- Language: English
About the authors
About the authors
Sanjay Saxena
Author
Dr. Sanjay Saxena is an Assistant Professor in the Department of Computer Science and Engineering at IIIT Bhubaneswar, India. He obtained his Ph.D. from the Indian Institute of Technology (BHU), Varanasi, and completed his postdoctoral research at the Artificial Intelligence in Biomedical Imaging Lab, University of Pennsylvania, USA. Dr. Saxena's primary research area involves developing AI-based methods for brain cancer analysis, with a broader focus on Data Science, Machine Learning, Deep Learning, and the emerging fields of Radiomics and Radiogenomics. His tenure under Prof. Davatzikos at the University of Pennsylvania was instrumental in his significant learning about Radiomics/Radiogenomics. Dr. Saxena is a member of prestigious organisations such as IEEE, the Society of Neuro-Oncology, ACM, and the New York Academy of Science. He has presented his work at globally recognised universities such as like Imperial College London, Stony Brook University, and Vienna University of Technology. and has made substantial scholarly contributions, with several peer-reviewed Journals, conference publications, book chapters, and two books to his name, advancing the intersection of AI and human cancer research.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, IndiaJasjit S. Suri
Author
Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.
Affiliations and expertise
Chairman, AtheroPoint LLC, USA