Table of Contents

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, Yan Wu, Michael Gensheimer, Masoud Badiei Khuzani, Lei Xing

About the Author

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.

Reviews

"Despite an abundance of research papers and some review articles, there have not been many comprehensive books devoted to these special audiences. Two first‐edition books published in 2019 by the Taylor and Francis Group, Radiomics and Radiogenomics (edited by Ruijiang Li, Lei Xing, Sandy Napel, and Daniel L. Rubin) and Big Data in Radiation Oncology (edited by Jun Deng and Lei Xing), have opportunely filled this void, and provided a comprehensive review as well as valuable insights on these key new advances. .... From these two books, readers can gain a fundamental understanding of radiomic feature definition and computation, processing steps (such as voxel resampling, MRI field bias correction and normalization, and other data harmonization), and processing parameters (such as fixed bin size vs fixed bin number and voxel neighborhood size). Readers can also develop a deeper appreciation of proper data management in modeling from both texts. As such, the technical knowledge from the books can assist researchers in optimizing their own study design."
-Dandan Zheng, in the Journal of Applied Clinical Medical Physics, July 2020