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Machine Learning Applications in Radiation Therapy

Machine Learning Applications in Radiation Therapy
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Author(s): Hao H. Zhang (University of Maryland School of Medicine, USA), Robert R. Meyer (University of Wisconsin-Madison, USA), Leyuan Shi (University of Wisconsin-Madison, USA)and Warren D. D’Souza (University of Maryland School of Medicine, USA)
Copyright: 2012
Pages: 26
Source title: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques
Source Author(s)/Editor(s): Siddhivinayak Kulkarni (University of Ballarat, Australia)
DOI: 10.4018/978-1-4666-1833-6.ch005

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Abstract

Cancer is one of the most complex diseases and one of the most effective treatments, radiation therapy, is also a complicated process. Informatics is becoming a critical tool for clinicians and scientists for improvements to the treatment and a better understanding of the disease. Computational techniques such as Machine Learning have been increasingly used in radiation therapy. As complex as cancer is, this book chapter shows that a machine learning technique has the ability to provide physicians information for better diagnostic, to obtain tumor location for more accurate treatment delivery, and to predict radiotherapy response so that personalized treatment can be developed.

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