Main 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.

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