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Radiomics and Radiogenomics: Technical Basis and Clinical
Applications provides a first summary of the overlapping fields of
radiomics and radiogenomics, showcasing how they are being used to
evaluate disease characteristics and correlate with treatment
response and patient prognosis. It explains the fundamental
principles, technical bases, and clinical applications with a focus
on oncology. The book's expert authors present computational
approaches for extracting imaging features that help to detect and
characterize disease tissues for improving diagnosis, prognosis,
and evaluation of therapy response. This book is intended for
audiences including imaging scientists, medical physicists, as well
as medical professionals and specialists such as diagnostic
radiologists, radiation oncologists, and medical oncologists.
Features Provides a first complete overview of the technical
underpinnings and clinical applications of radiomics and
radiogenomics Shows how they are improving diagnostic and
prognostic decisions with greater efficacy Discusses the image
informatics, quantitative imaging, feature extraction, predictive
modeling, software tools, and other key areas Covers applications
in oncology and beyond, covering all major disease sites in
separate chapters Includes an introduction to basic principles and
discussion of emerging research directions with a roadmap to
clinical translation
Radiomics and Radiogenomics: Technical Basis and Clinical
Applications provides a first summary of the overlapping fields of
radiomics and radiogenomics, showcasing how they are being used to
evaluate disease characteristics and correlate with treatment
response and patient prognosis. It explains the fundamental
principles, technical bases, and clinical applications with a focus
on oncology. The book's expert authors present computational
approaches for extracting imaging features that help to detect and
characterize disease tissues for improving diagnosis, prognosis,
and evaluation of therapy response. This book is intended for
audiences including imaging scientists, medical physicists, as well
as medical professionals and specialists such as diagnostic
radiologists, radiation oncologists, and medical oncologists.
Features Provides a first complete overview of the technical
underpinnings and clinical applications of radiomics and
radiogenomics Shows how they are improving diagnostic and
prognostic decisions with greater efficacy Discusses the image
informatics, quantitative imaging, feature extraction, predictive
modeling, software tools, and other key areas Covers applications
in oncology and beyond, covering all major disease sites in
separate chapters Includes an introduction to basic principles and
discussion of emerging research directions with a roadmap to
clinical translation
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.
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