Includes bibliographical references (pages 301-364) and index.

Clinical data in outcome models / Michael T. Milano -- Imaging data (radiomics) / Issam El Naqa -- Dosimetric data / Issam El Naqa and Randall K. Ten Haken -- Pre-clinical radiobiological insights to inform modelling of radiotherapy outcome / Peter van Luijk and Robert P. Coppes -- Radiogenomics / Issam El Naqa, Sarah L. Kerns, James Coates, Yi Luo, Corey Speers, Randall K. Ten Haken, Catharine M.L. West, and Barry S. Rosenstein -- Analytical models / Vitali Moiseenko, Jimm Grimm, James D. Murphy, David J. Carlson, and Issam El Naqa -- Conventional modeling / Tiziana Rancati and Claudio Fiorino -- Machine learning modeling / Sarah Gulliford -- Multiscale modeling: Monte Carlo / Werner Friedland and Pavel Kundrát -- Multi-scale modeling approaches: application in chemo- and immuno-therapies / Issam El Naqa -- Outcome modeling in treatment planning / X. Sharon Qi, Mariana Guerrero, and X. Allen Li -- Utility / Matthew J. Schipper -- Outcome modeling in particle therapy / J. Schuemann, A.L. McNamara, and C. Grassberger -- Modeling response to oncological surgery / J. Jesús Naveja, Leonardo Zapata-Fonseca, and Flavio F. Contreras-Torres -- Developing personalized treatment recommendations using SMARTs / Elizabeth F. Krakow and Erica E.M. Moodie.

"This book discusses outcome modeling in cancer using analytical and data-driven approaches, both top-down using big data approaches and bottom-up using multi-scale methods. It provides in-depth mathematical background as well as intuitive physical and biological perspectives. Applications in radiation oncology, chemotherapy, surgery and immunotherapy are covered, as well as - in the case of multimodality treatments - span assessment, prediction, prognosis, decision support and clinical trials design. This is in addition to statistical validation of performance and evaluating applicability. Software tools accompany the book, illustrating how to solve various modeling exercises"-- Provided by publisher.