MACHINE LEARNING IN RADIATION ONCOLOGY. THEORY AND APPLICATIONS

1. What Is Machine Learning?
2. Computational Learning Theory
3. Machine Learning Methodology
4. Performance Evaluation in Machine Learning
5. Informatics in Radiation Oncology
6. Application of Machine Learning for Multicenter Learning
7. Computerized Detection of Lesions in Diagnostic Images
8. Classification of Malignant and Benign Tumors
9. Image-Guided Radiotherapy with Machine Learning
10. Knowledge-Based Treatment Planning
11. Artificial Neural Networks to Emulate and Compensate Breathing Motion During Radiation Therapy
12. Image-Based Motion Correction
13. Detection and Prediction of Radiotherapy Errors
14. Treatment Planning Validation
15. Treatment Delivery Validation
16. Bioinformatics of Treatment Response
17. Modelling of Normal Tissue Complication Probabilities (NTCP): Review of Application of Machine Learning in Predicting NTCP
18. Modeling of Tumor Control Probability (TCP)

This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Features
• Provides a complete overview of the role of machine learning in radiation oncology and medical physics
• Covers the use of machine learning in quality assurance, computer-aided detection, image-guided radiotherapy, respiratory motion management, and outcome prediction
• Presents important relevant background information