1st Edition

Radiomics and Radiogenomics
Technical Basis and Clinical Applications

ISBN 9780815375852
Published June 28, 2019 by Chapman and Hall/CRC
420 Pages 83 Color & 25 B/W Illustrations

USD $249.95

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Book Description

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.


  • 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

Table of Contents

Part I: Introduction


1 Principles and rationale of radiomics and radiogenomics

Sandy Napel


2 Imaging informatics: An overview

Assaf Hoogi and Daniel L. Rubin

3 Quantitative imaging using CT

Lin Lu, Lawrence H. Schwartz, and Binsheng Zhao

4 Quantitative PET/CT for radiomics

Stephen R. Bowen, Paul E. Kinahan, George A. Sandison, and Matthew J. Nyflot

5 Quantitative imaging using MRI

David A. Hormuth II, Jack Virostko, Ashley Stokes, Adrienne Dula, Anna G.

Sorace, Jennifer G. Whisenant, Jared Weis, C. Chad Quarles, Michael I. Miga, and

Thomas E. Yankeelov

6 Tumor segmentation

Spyridon Bakas, Rhea Chitalia, Despina Kontos, Yong Fan, and Christos Davatzikos

7 Habitat imaging of tumor evolution by magnetic resonance imaging (MRI)

Bruna Victorasso Jardim-Perassi, Gary Martinez, and Robert Gillies

8 Feature extraction and qualification

Lise Wei and Issam El Naqa

9 Predictive modeling, machine learning, and statistical issues

Panagiotis Korfiatis, Timothy L. Kline, Zeynettin Akkus, Kenneth Philbrick,

and Bradley J. Erickson

10 Radiogenomics: Rationale and methods

Olivier Gevaert

11 Resources and datasets for radiomics

Ken Chang, Andrew Beers, James Brown, and Jayashree Kalpathy-Cramer


12 Pathways to radiomics-aided clinical decision-making for precision medicine

Tianyue Niu, Xiaoli Sun, Pengfei Yang, Guohong Cao,

Khin K. Tha, Hiroki Shirato, Kathleen Horst, and Lei Xing

13 Brain cancer

William D. Dunn Jr. and Rivka R. Colen

14 Breast cancer

Hui Li and Maryellen L. Giger

15 Radiomics for lung cancer

Di Dong and Jie Tian

16 The essence of R in head and neck cancer: Role of radiomics and

radiogenomics from a radiation oncology perspective

Hesham Elhalawani, Arvind Rao, and Clifton D. Fuller

17 Gastrointestinal cancers

Zaiyi Liu

18 Radiomics in genitourinary cancers: Prostate cancer

Satish Viswanath and Anant Madabhushi

19 Radiomics analysis for gynecologic cancers

Harini Veeraraghavan

20 Applications of imaging genomics beyond oncology

Xiaohui Yao, Jingwen Yan, and Li Shen


21 Quantitative imaging to guide mechanism-based modeling of cancer

David A. Hormuth II, Matthew T. Mckenna, and Thomas E. Yankeelov

22 Looking ahead: Opportunities and challenges in radiomics and radiogenomics

Ruijiang Li, Yan Wu, Michael Gensheimer, Masoud Badiei Khuzani, and Lei Xing

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Ruijiang Li, PhD, is an Assistant Professor and ABR-certified medical physicist in the Department of Radiation Oncology at Stanford University School of Medicine. He is also an affiliated faculty member of the Integrative Biomedical Imaging Informatics at Stanford (IBIIS), a departmental section within Radiology. He has a broad background and training in medical imaging, with specific expertise in quantitative image analysis and machine learning as well as their applications in radiology and radiation oncology. He has received many nationally recognized awards, including the NIH Pathway to Independence (K99/R00) Award, ASTRO Clinical/Basic Science Research Award, ASTRO Basic/Translational Science Award, etc.

Dr. Lei Xing is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. He also holds affiliate faculty positions in Department of Electrical engineering, Medical Informatics, Bio-X and Molecular Imaging Program at Stanford. Dr. Xing’s research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, imaging informatics and analysis, and applications of molecular imaging in radiation oncology. Dr. Xing is an author on more than 280 peer reviewed publications, a co-inventor on many issued and pending patents, and a co-investigator or principal investigator on numerous NIH, DOD, ACS and corporate grants. He is a fellow of AAPM (American Association of Physicists in Medicine) and AIMBE (American Institute for Medical and Biological Engineering).

Dr. Sandy Napel is Professor of Radiology, and Professor of Medicine and Electrical Engineering (by courtesy) at Stanford University. His primary interests are in developing diagnostic and therapy-planning applications and strategies for the acquisition, visualization, and quantitation of multi-dimensional medical imaging data. He is the co-director of the Radiology 3D and Quantitative Imaging Lab, and co-Director of IBIIS (Integrative Biomedical Imaging Informatics at Stanford).

Daniel L. Rubin, MD, MS, is Associate Professor of Radiology and Medicine (Biomedical Informatics Research) at Stanford University. He is Principal Investigator of two centers in the National Cancer Institute's Quantitative Imaging Network (QIN), Chair of the QIN Executive Committee, Chair of the Informatics Committee of the ECOG-ACRIN cooperative group, and past Chair of the RadLex Steering Committee of the Radiological Society of North America. His NIH-funded research program focuses on quantitative imaging and integrating imaging data with clinical and molecular data to discover imaging phenotypes that can predict the underlying biology, define disease subtypes, and personalize treatment. He is a Fellow of the American College of Medical Informatics and haspublished over 160 scientific publications in biomedical imaging informatics and radiology.