Machine 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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Foreword from the editors - p. xiii 8. Section I: Fundamentals and overview - p. xv 9. Chapter 1. Fundamentals of machine learning - p. 3 10. Chapter 2. Artificial intelligence, machine learning, and bioethics in clinical medicine - p. 29 11. Chapter 3. Machine learning applications in cancer genomics - p. 41 12. Chapter 4. Radiomics: “unlocking the potential of medical images for precision radiation oncology” - p. 73 13. Chapter 5. Deep learning for medical image segmentation - p. 107 14. Chapter 6. Natural language processing in oncology - p. 137 15. Chapter 7. Evaluating machine learning models: From development to clinical deployment - p. 163 16. Section II: Research applications - p. 191 17. Chapter 8. Germline genomics in radiotherapy - p. 197 18. Chapter 9. Tumor genomics in radiotherapy - p. 217 19. Chapter 10. Radiotherapy outcome prediction with medical imaging - p. 239 20. Chapter 11. Causal inference for oncology - p. 317 21. Chapter 12. Machine learning in quality assurance and treatment delivery - p. 337 22. Section III: Clinical applications and future developments - p. 350 23. Chapter 13. Case study: Deep learning in radiotherapy auto segmentation - p. 355 24. Chapter 14. Case study: adaptive radiotherapy in the clinic - p. 365 25. Chapter 15. Case study: Handling small datasets – Transfer learning for medical images - p. 381 26. Chapter 16. Case study: Lymph node malignancy classification for head and neck cancer radiation therapy - p. 401 27. Chapter 17. Training the current and next generation in machine learning and artificial intelligence applications in radiation oncology - p. 425 28. Chapter 18. Governance issues and commercialization - p. 445 29. Index - p. 455
Machine 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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Foreword from the editors - p. xiii 8. Section I: Fundamentals and overview - p. xv 9. Chapter 1. Fundamentals of machine learning - p. 3 10. Chapter 2. Artificial intelligence, machine learning, and bioethics in clinical medicine - p. 29 11. Chapter 3. Machine learning applications in cancer genomics - p. 41 12. Chapter 4. Radiomics: “unlocking the potential of medical images for precision radiation oncology” - p. 73 13. Chapter 5. Deep learning for medical image segmentation - p. 107 14. Chapter 6. Natural language processing in oncology - p. 137 15. Chapter 7. Evaluating machine learning models: From development to clinical deployment - p. 163 16. Section II: Research applications - p. 191 17. Chapter 8. Germline genomics in radiotherapy - p. 197 18. Chapter 9. Tumor genomics in radiotherapy - p. 217 19. Chapter 10. Radiotherapy outcome prediction with medical imaging - p. 239 20. Chapter 11. Causal inference for oncology - p. 317 21. Chapter 12. Machine learning in quality assurance and treatment delivery - p. 337 22. Section III: Clinical applications and future developments - p. 350 23. Chapter 13. Case study: Deep learning in radiotherapy auto segmentation - p. 355 24. Chapter 14. Case study: adaptive radiotherapy in the clinic - p. 365 25. Chapter 15. Case study: Handling small datasets – Transfer learning for medical images - p. 381 26. Chapter 16. Case study: Lymph node malignancy classification for head and neck cancer radiation therapy - p. 401 27. Chapter 17. Training the current and next generation in machine learning and artificial intelligence applications in radiation oncology - p. 425 28. Chapter 18. Governance issues and commercialization - p. 445 29. Index - p. 455
