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

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• 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.

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• 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.

Select Guide Rating

• 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.