  Current clinical decision-making in oncology relies on averages of large
patient populations to both assess tumor status and treatment outcomes.
However, cancers exhibit an inherent evolving heterogeneity that requires an
individual approach based on rigorous and precise predictions of cancer growth
and treatment response. To this end, we advocate the use of quantitative in
vivo imaging data to calibrate mathematical models for the personalized
forecasting of tumor development. In this chapter, we summarize the main data
types available from both common and emerging in vivo medical imaging
technologies, and how these data can be used to obtain patient-specific
parameters for common mathematical models of cancer. We then outline
computational methods designed to solve these models, thereby enabling their
use for producing personalized tumor forecasts in silico, which, ultimately,
can be used to not only predict response, but also optimize treatment. Finally,
we discuss the main barriers to making the above paradigm a clinical reality.
