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
Trade Review
"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
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
