Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians (Paperback)

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