AI-RO-Book-Project
Scope of Book/Brief Description:
Chartering the future of Radiation Oncology Services with use Artifical Intelligence and its implications. This book is envisioned to serve as a comprehensive resource for scientists, physicians, and engineers interested in developing the next generation Radiation Oncology technologies by taking advantage of artificial intelligence tools.
Expected Publish Date:
September 2022
Publisher:
World Scientific Publishing Executive Editor: Christopher B. Davis Desk Editor: Cheryl Heng Man Ting
Co-Editors:
Seong K. Mun, PhD Sonja Dieterich, PhD Professor of Physics and Director of AIC Professor of Medical Physics, Residency Co-Director Virginia Tech University of California, Davis munsk@vt.edu sdieterich@ucdavis.edu
Current List of Chapter Topics and Contributors:
The technology and devices for radiation oncology:
o Sonja Dieterich, PhD, University of California, Davis o Agam Sharda, Varian Medical Systems
Natural language processing for radiation oncology:
o Julian Hong, MD, University of Calif San Francisco o Lisa Ni, MD, University of Calif San Francisco o Christina Phuong, MD, University of Calif San Francisco
Science and tools for radiomics:
o Christopher Wardell, PhD, University Arkansas
AI lessons learned from radiology:
o Seong K. Mun, PhD, Virginia Tech o Kenneth H. Wong, PhD, Virginia Tech
Artificial intelligence or image segmentation in radiation oncology:
o Quan Chen, PhD, University of Kentucky o Xue Feng, PhD, Carina Medical LLC
Artificial intelligence in radiation therapy treatment planning:
o Xiaofeng Zhu, PhD, INOVA Health o Dandan Zheng, PhD, Univ. of Nebraska Medical Center
Open access data to enable AI applications in radiation therapy
o Fred Prior, PhD, University Arkansas o William Bennett, PhD, University of Arkansas
Automating treatment planning:
o Leigh Conroy, PhD, Princess Margaret Cancer Centre o Tom Purdie, PhD, Princess Margaret Cancer Centre
Machine learning and bioinformatics methods for PRISM radiotherapy toxicity outcomes variations:
o Joe Deasy, PhD, Memorial Slone Kettering Cancer Center
An approach to evaluating AI and AI systems/solutions in healthcare:
o Gretchen Purcell Jackson, MD, PhD, IBM, o Radiation Oncologist (TBD)
Error propagation and statistical modeling predictive analytics:
o Srijan Sengupta, PhD, North Carolina State University o Ed Kline, RadPhysics, Inc.
AI innovation in radiation oncology:
o Andrew Wilson and Company, Elekta Corp.
Knowledge-based treatment planning for radiation therapy:
o Dalong Pang, PhD, Georgetown University
Artificial intelligence and ethics:
o Megan Hyun, PhD, University of Nebraska o Alexander Hyun, PhD, Minerva Scools at KGI
For any questions regarding the development of the book, please contact Shijir at sbayarsa@vt.edu