1st Edition

Radiomics and Radiogenomics Technical Basis and Clinical Applications

Edited By Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin Copyright 2019
    484 Pages
    by Chapman & Hall

    484 Pages 83 Color & 25 B/W Illustrations
    by Chapman & Hall

    484 Pages 83 Color & 25 B/W Illustrations
    by Chapman & Hall

    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

    Part I: Introduction

    1. Principles and rationale of radiomics and radiogenomics

    Sandy Napel

    Part II: Technical Basis

    2. Imaging informatics: an overview

    Assaf Hoogi, Daniel Rubin

    3. Quantitative imaging using CT

    Lin Lu, Lawrence H. Schwartz, Binsheng Zhao

    4. Quantitative PET/CT for radiomics

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

    5. Common techniques of quantitative MRI

    David Hormuth II, Jack Virostko, Ashley Stokes, Adrienne Dula, Anna G. Sorace, Jennifer G. Whisenant, Jared Weis, C. Chad Quarles, Michael I. Miga, Thomas E. Yankeelov

    6. Tumor segmentation

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

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

    Bruna Victorasso Jardim-Perassi, Gary Martinez, Robert Gillies

    8. Feature extraction and qualification

    Lise Wei, Issam El Naqa

    9. Predictive modeling, machine learning, and statistical issues

    Panagiotis Korfiatis, Timothy L. Kline, Zeynettin Akkus, Kenneth Philbrick, Bradley J. Erikson

    10. Radiogenomics: rationale and methods

    Olivier Gevaert

    11. Resources and datasets for radiomics

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

    Part III: Clinical Applications

    12. Roles of radiomics and radiogenomics in clinical practice

    Tianyue Niu, Xiaoli Sun, Pengfei Yang, Guohong Cao, Khin K. Tha, Hiroki Shirato, Kathleen Horst, Lei Xing

    13. Brain cancer

    William D. Dunn Jr, Rivka Colen

    14. Breast cancer

    Hui Li, Maryellen L. Giger

    15. Lung cancer

    Dong Di, Jie Tian, Shuo Wang

    16. The essence of R in head and neck cancer

    Hesham Elhalawani, Arvind Rao, Clifton D. Fuller

    17. Gastrointestinal cancers

    Zaiyi Liu

    18. Radiomics in genitourinary cancers: prostate cancer

    Satish Viswanath, Anant Madabhushi

    19. Radiomics analysis for gynecologic cancers

    Harini Veeraraghavan

    20. Applications of imaging genomics beyond oncology

    Xiaohui Yao, Jingwen Yan, Li Shen

    Part IV: Future Outlook

    21. Quantitative imaging to guide mechanism based modeling of cancer

    David A. Hormouth II, Matthew T. McKenna, Thomas E. Yankeelov

    22. Looking Ahead: Opportunities and Challenges in Radiomics and Radiogenomics



    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.