368 pages | 101 B/W Illus.
This book explores outcome modeling in cancer from a data-centric perspective to enable a better understanding of complex treatment response, to guide the design of advanced clinical trials, and to aid personalized patient care and improve their quality of life. It contains coverage of the relevant data sources available for model construction (panomics), ranging from clinical or preclinical resources to basic patient and treatment characteristics, medical imaging (radiomics), and molecular biological markers such as those involved in genomics, proteomics and metabolomics. It also includes discussions on the varying methodologies for predictive model building with analytical and data-driven approaches.
This book is primarily intended to act as a tutorial for newcomers to the field of outcome modeling, as it includes in-depth how-to recipes on modeling artistry while providing sufficient instruction on how such models can approximate the physical and biological realities of clinical treatment. The book will also be of value to seasoned practitioners as a reference on the varying aspects of outcome modeling and their current applications.
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