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.

• Part I: Introduction 1. Principles and rationale of radiomics and radiogenomics Sandy NapelPart II: Technical Basis2. Imaging informatics: an overviewAssaf Hoogi, Daniel Rubin3. Quantitative imaging using CTLin Lu, Lawrence H. Schwartz, Binsheng Zhao4. Quantitative PET/CT for radiomicsStephen R. Bowen, Paul E. Kinahan, George A. Sandison, Matthew J. Nyflot5. Common techniques of quantitative MRIDavid Hormuth II, Jack Virostko, Ashley Stokes, Adrienne Dula, Anna G. Sorace, Jennifer G. Whisenant, Jared Weis, C. Chad Quarles, Michael I. Miga, Thomas E. Yankeelov6. Tumor segmentationSpyridon Bakas, Rhea Chitalia, Despina Kontos, Yong Fan, Christos Davatzikos7. Habitat imaging of tumor evolution by magnetic resonance imaging (MRI) Bruna Victorasso Jardim-Perassi, Gary Martinez, Robert Gillies8. Feature extraction and qualificationLise Wei, Issam El Naqa9. Predictive modeling, machine learning, and statistical issuesPanagiotis Korfiatis, Timothy L. Kline, Zeynettin Akkus, Kenneth Philbrick, Bradley J. Erikson10. Radiogenomics: rationale and methodsOlivier Gevaert11. Resources and datasets for radiomicsKen Chang, Andrew Beers, James Brown, Jayashree Kalpathy-CramerPart III: Clinical Applications12. Roles of radiomics and radiogenomics in clinical practiceTianyue Niu, Xiaoli Sun, Pengfei Yang, Guohong Cao, Khin K. Tha, Hiroki Shirato, Kathleen Horst, Lei Xing13. Brain cancerWilliam D. Dunn Jr, Rivka Colen14. Breast cancerHui Li, Maryellen L. Giger15. Lung cancerDong Di, Jie Tian, Shuo Wang16. The essence of R in head and neck cancerHesham Elhalawani, Arvind Rao, Clifton D. Fuller17. Gastrointestinal cancersZaiyi Liu18. Radiomics in genitourinary cancers: prostate cancerSatish Viswanath, Anant Madabhushi19. Radiomics analysis for gynecologic cancersHarini Veeraraghavan20. Applications of imaging genomics beyond oncologyXiaohui Yao, Jingwen Yan, Li ShenPart IV: Future Outlook21. Quantitative imaging to guide mechanism based modeling of cancerDavid A. Hormouth II, Matthew T. McKenna, Thomas E. Yankeelov22. Looking Ahead: Opportunities and Challenges in Radiomics and Radiogenomics<

"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