Machine Learning and Artificial Intelligence in Radiation Oncology
A Guide for Clinicians
- 1st Edition - December 2, 2023
- Imprint: Academic Press
- Editors: Barry S. Rosenstein, Tim Rattay, John Kang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 0 0 0 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 2 0 0 1 - 6
Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinic… Read more
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Request a sales quoteMachine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology.
- Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic
- Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations
- Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic
Oncologists, clinicians, radiologists, cancer researchers
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Foreword from the editors
- Section I. Fundamentals and overview
- Chapter 1. Fundamentals of machine learning
- Chapter 2. Artificial intelligence, machine learning, and bioethics in clinical medicine
- Chapter 3. Machine learning applications in cancer genomics
- Chapter 4. Radiomics: “unlocking the potential of medical images for precision radiation oncology”
- Chapter 5. Deep learning for medical image segmentation
- Chapter 6. Natural language processing in oncology
- Chapter 7. Evaluating machine learning models: From development to clinical deployment
- Section II. Research applications
- Chapter 8. Germline genomics in radiotherapy
- Chapter 9. Tumor genomics in radiotherapy
- Chapter 10. Radiotherapy outcome prediction with medical imaging
- Chapter 11. Causal inference for oncology
- Chapter 12. Machine learning in quality assurance and treatment delivery
- Section III. Clinical applications and future developments
- Chapter 13. Case study: Deep learning in radiotherapy auto segmentation
- Chapter 14. Case study: adaptive radiotherapy in the clinic
- Chapter 15. Case study: Handling small datasets – Transfer learning for medical images
- Chapter 16. Case study: Lymph node malignancy classification for head and neck cancer radiation therapy
- Chapter 17. Training the current and next generation in machine learning and artificial intelligence applications in radiation oncology
- Chapter 18. Governance issues and commercialization
- Index
- Edition: 1
- Published: December 2, 2023
- Imprint: Academic Press
- No. of pages: 478
- Language: English
- Paperback ISBN: 9780128220009
- eBook ISBN: 9780128220016
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Barry S. Rosenstein
Dr. Rosenstein is a Professor of Radiation Oncology and a Professor of Genetics & Genomic Sciences at the Icahn School of Medicine at Mount Sinai. The focus of Dr. Rosenstein’s research program for the past 25 years has been the identification of genetic/genomic markers associated with the development of adverse effects resulting from radiotherapy. In this context, he was one of the first investigators to hypothesize that possession of single nucleotide polymorphisms in certain genes may render some cancer patients more susceptible to injuries resulting from radiotherapy. Dr. Rosenstein established and co-led for 14 years the Radiogenomics Consortium (RGC), representing an international consortium currently with 240 members in 33 countries across 135 institutions. Through his efforts, Dr. Rosenstein, has been in the forefront of research in the use of big data in radiation oncology and has collaborated with investigators possessing expertise in bioinformatics and statistics to employ machine learning-based modeling approaches in radiogenomic studies.
Affiliations and expertise
Professor of Radiation Oncology, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USATR
Tim Rattay
Dr. Rattay is Associate Professor in Breast Surgery at the University of Leicester and Consultant Breast Surgeon at University Hospitals of Leicester, UK. He has been working in the field of radiobiology and radiogenomics of the normal tissues for over ten years. His research is specifically focused on the effect of breast radiotherapy on surgical and patient-reported outcomes. This includes Big Data and machine learning (ML) approaches and he is also interested in applying qualitative research methodology to explore breast cancer survivors’ views and experience of treatment and personalised medicine. Dr. Rattay works in a multi-disciplinary research team with nurses, clinical psychologists, geneticists, radiographers and medical physicists, and he has established collaborations with ML experts both nationally and internationally.
Affiliations and expertise
Associate Professor in Breast Surgery at the University of Leicester and Consultant Breast Surgeon at University Hospitals of Leicester, UKJK
John Kang
Dr. Kang has over 15 years of experience in developing and applying novel computational methods to complex, biomedical data. He is an assistant professor and biomedical informatics lead in the Dept. of Radiation Oncology at the University of Washington. His research focus is on machine learning in oncology with a specific focus on natural language processing and topic modeling and his operations focus is on using informatics to improve patient care and decrease physician burden. Dr. Kang has been invited to speak on AI in oncology at several national and international conferences and workshops.
Affiliations and expertise
Assistant Professor and Biomedical Informatics Lead, Dept. of Radiation Oncology, University of WashingtonRead Machine Learning and Artificial Intelligence in Radiation Oncology on ScienceDirect