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Full Description
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.
Features:
Covers top-down approaches applying statistical, machine learning, and big data analytics and bottom-up approaches using first principles and multi-scale techniques, including numerical simulations based on Monte Carlo and automata techniques
Provides an overview of the available software tools and resources for outcome model development and evaluation, and includes hands-on detailed examples throughout
Presents a diverse selection of the common applications of outcome modeling in a wide variety of areas: treatment planning in radiotherapy, chemotherapy and immunotherapy, utility-based and biomarker applications, particle therapy modeling, oncological surgery, and the design of adaptive and SMART clinical trials
Contents
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.
