BookIssam El Naqa, Ruijiang Li, Martin J. Murphy, editors.
Summary: 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.
Contents:
Introduction: What is Machine Learning
Computational Learning Theory
Overview of Supervised Learning Methods
Overview of Unsupervised Learning Methods
Performance Evaluation
Variety of Applications in Radiation Oncology
Machine Learning for Quality Assurance: Quality Assurance as a Learning Problem
Detection of Radiotherapy Errors Using Unsupervised Learning
Prediction of Radiotherapy Errors Using Supervised Learning
Machine Learning for Computer-Aided Detection: Detection of Cancer Lesions from Imaging
Classification of Malignant and Benign Tumours
Machine Learning for Treatment Planning and Delivery
Image-guided Radiotherapy with Machine Learning: IMRT Optimization Using Machine Learning
Treatment Assessment Tools
Machine Learning for Motion Management: Prediction of Respiratory Motion
Motion-Correction Using Learning Methods
Machine Learning Application in 4D-CT
Machine Learning Application in Dynamic Delivery
Machine Learning for Outcomes Modeling: Bioinformatics of Treatment Response
Modelling of Norma Tissue Complication Probabilities (NTCP)
Modelling of Tumour Control Probability (TCP).