Description
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.- 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
Table of Contents
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
