A Guide to Outcome Modeling In Radiotherapy and Oncology
Listening to the Data
Herausgeber: El Naqa, Issam
A Guide to Outcome Modeling In Radiotherapy and Oncology
Listening to the Data
Herausgeber: El Naqa, Issam
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This book serves as a tutorial for newcomers to the field of outcome modeling; including in-depth how-to recipes on modeling artistry and providing instructions on how such models can approximate the physical and biological realities of clinical treatment.
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This book serves as a tutorial for newcomers to the field of outcome modeling; including in-depth how-to recipes on modeling artistry and providing instructions on how such models can approximate the physical and biological realities of clinical treatment.
Produktdetails
- Produktdetails
- Verlag: CRC Press
- Seitenzahl: 368
- Erscheinungstermin: 30. Juni 2020
- Englisch
- Abmessung: 254mm x 175mm x 23mm
- Gewicht: 676g
- ISBN-13: 9780367572082
- ISBN-10: 0367572087
- Artikelnr.: 66597058
- Verlag: CRC Press
- Seitenzahl: 368
- Erscheinungstermin: 30. Juni 2020
- Englisch
- Abmessung: 254mm x 175mm x 23mm
- Gewicht: 676g
- ISBN-13: 9780367572082
- ISBN-10: 0367572087
- Artikelnr.: 66597058
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.
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.
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.
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.