ABSTRACT
Outcome modeling plays a pivotal role in oncology. This includes understanding response to different therapeutic cancer agents, personalization of treatment and designing of future clinical trials. Although application of outcome models has accompanied oncology practices since its inception, it also evolved from simple hand calculations of doses based on experiences and simplified understanding of cancer behavior into more advanced computer simulation models driven by tremendous growth in patient-specific data and an acute desire to have more accurate predictions of response. This chapter reviews basic definitions used in computational modeling and main data resources, and provides an overview of the increasing role of outcome models in the field of oncology.
