Machine Learning in Radiation Oncology

Theory and Applications

Issam El Naqa, Ruijiang Li, Martin J. Murphy (Herausgeber)

Buch | Softcover

XIV, 336 Seiten

2016 | 1. Softcover reprint of the original 1st ed. 2015
Springer International Publishing (Verlag)
978-3-319-35464-4 (ISBN)

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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.

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).

Erscheinungsdatum	21.10.2016
Zusatzinfo	XIV, 336 p. 127 illus., 67 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Medizin / Pharmazie ► Medizinische Fachgebiete ► Onkologie
	Medizinische Fachgebiete ► Radiologie / Bildgebende Verfahren ► Radiologie
	Naturwissenschaften ► Chemie ► Physikalische Chemie
	Naturwissenschaften ► Physik / Astronomie ► Angewandte Physik
Schlagworte	machine learning • Medical and Radiation Physics • medical physics • Medicine • Medicine: general issues • Nuclear chemistry, photochemistry and radiation • Outcome Modelling • radiation oncology • radiation physics • Radiaton Oncology • Radiology • radiotherapy • Treatment Planning
ISBN-10	3-319-35464-7 / 3319354647
ISBN-13	978-3-319-35464-4 / 9783319354644
Zustand	Neuware