Machine Learning in Radiation Oncology: A Guide for Clinicians

Machine Learning in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. The book addresses the existing void in a resource to educate practicing clinicians on how machine learning can be used to improve clinical and patient-centered outcomes. Sections cover the fundamental concepts of machine learning and radiation oncology, detail techniques applied in genomics, discuss translational opportunities, such as in radiogenomics and autosegmentation, present current clinical applications in clinical decision-making, cover how to integrate AI into workflow and use cases, and elaborate on cross-collaborations within industry.The book is a valuable resource for oncologists, radiologists and members of the biomedical field who want to learn more about machine learning and its present and potential uses in 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 INDICE: Section 1: FUNDAMENTAL CONCEPTS 1. Overview of machine learning and radiation oncology 2. Machine Learning techniques in genomics (shallow learning) 3. Bayesian machine learning/deep learning 4. Computational Genomics Section 2: TRANSLATIONAL OPPORTUNITIES 5. Germline Radiogenomics 6. Tumor Radiogenomics: PORTOS, GARD/RSI, Bayesian Networks 7. Quantitative imaging with genomics for radiation oncology 8. Autosegmentation Section 3: CURRENT CLINICAL APPLICATIONS 9. Integrating ML into clinical decision making 10. Machine learning classification algorithms for outcome prediction in radiotherapy 11. Clinical integration of AI into workflow 12. Standardization/Use Cases/Data Sharing/Privacy 13. Cross-collaborations with Industry

• ISBN: 978-0-12-822000-9
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/09/2022
• Nº Volúmenes: 1
• Idioma: Inglés