Issam El Naqa is an Associate Professor of Radiation Oncology at the University of Michigan at Ann Arbor, USA.

• Section I: Multiple sources of data. Chapter 1: Introduction to data sources and outcome models. Chapter 2: Cinical data in outcome models. Chapter 3: Imaging data: Radiomics. Chapter 4: Dosimetric data. Chapter 5: Pre-Clinical Radiobiological insights to inform modelling of radiotherapy outcome. Chapter 6: Biological data: The use of omics in outcome models. Section II: Top-down Modeling Approaches. Chapter 7: Analytical and mechanistic modeling. Chapter 8: Data driven approaches I: using conventional statistical inference methods, including linear and logistic regression. Chapter 9: Data driven approaches II: Machine Learning. Section III: Bottom-up Modeling Approaches. Chapter 10: Stochastic multiscale modelling of biological effects induced by ionizing radiation. Chapter 11: Multiscale modeling approaches: Application in Chemo and immunotherapies. Section IV: Example Applications in Oncology. Chapter 12: Outcome Modeling in Treatment Planning. Chapter 13: A Utility Based Approach to Individualized and Adaptive Radiation Therapy. Chapter 14: Outcome modeling in Particle therapy. Chapter 15: Modeling response to oncological surgery. Chapter 16: Tools for the precision medicine era: Developing highly adaptive and personalized treatment recommendations using SMARTs.

Issam El Naqa is an Associate Professor of Radiation Oncology at the University of Michigan at Ann Arbor, USA.

• Section I: Multiple sources of data. Chapter 1: Introduction to data sources and outcome models. Chapter 2: Cinical data in outcome models. Chapter 3: Imaging data: Radiomics. Chapter 4: Dosimetric data. Chapter 5: Pre-Clinical Radiobiological insights to inform modelling of radiotherapy outcome. Chapter 6: Biological data: The use of omics in outcome models. Section II: Top-down Modeling Approaches. Chapter 7: Analytical and mechanistic modeling. Chapter 8: Data driven approaches I: using conventional statistical inference methods, including linear and logistic regression. Chapter 9: Data driven approaches II: Machine Learning. Section III: Bottom-up Modeling Approaches. Chapter 10: Stochastic multiscale modelling of biological effects induced by ionizing radiation. Chapter 11: Multiscale modeling approaches: Application in Chemo and immunotherapies. Section IV: Example Applications in Oncology. Chapter 12: Outcome Modeling in Treatment Planning. Chapter 13: A Utility Based Approach to Individualized and Adaptive Radiation Therapy. Chapter 14: Outcome modeling in Particle therapy. Chapter 15: Modeling response to oncological surgery. Chapter 16: Tools for the precision medicine era: Developing highly adaptive and personalized treatment recommendations using SMARTs.

Issam El Naqa is an Associate Professor of Radiation Oncology at the University of Michigan at Ann Arbor, USA.

• Section I: Multiple sources of data. Chapter 1: Introduction to data sources and outcome models. Chapter 2: Cinical data in outcome models. Chapter 3: Imaging data: Radiomics. Chapter 4: Dosimetric data. Chapter 5: Pre-Clinical Radiobiological insights to inform modelling of radiotherapy outcome. Chapter 6: Biological data: The use of omics in outcome models. Section II: Top-down Modeling Approaches. Chapter 7: Analytical and mechanistic modeling. Chapter 8: Data driven approaches I: using conventional statistical inference methods, including linear and logistic regression. Chapter 9: Data driven approaches II: Machine Learning. Section III: Bottom-up Modeling Approaches. Chapter 10: Stochastic multiscale modelling of biological effects induced by ionizing radiation. Chapter 11: Multiscale modeling approaches: Application in Chemo and immunotherapies. Section IV: Example Applications in Oncology. Chapter 12: Outcome Modeling in Treatment Planning. Chapter 13: A Utility Based Approach to Individualized and Adaptive Radiation Therapy. Chapter 14: Outcome modeling in Particle therapy. Chapter 15: Modeling response to oncological surgery. Chapter 16: Tools for the precision medicine era: Developing highly adaptive and personalized treatment recommendations using SMARTs.