  With new advancements in technology, it is now possible to collect data for a
variety of different metrics describing tumor growth, including tumor volume,
composition, and vascularity, among others. For any proposed model of tumor
growth and treatment, we observe large variability among individual patients'
parameter values, particularly those relating to treatment response; thus,
exploiting the use of these various metrics for model calibration can be
helpful to infer such patient-specific parameters both accurately and early, so
that treatment protocols can be adjusted mid-course for maximum efficacy.
However, taking measurements can be costly and invasive, limiting clinicians to
a sparse collection schedule. As such, the determination of optimal times and
metrics for which to collect data in order to best inform proper treatment
protocols could be of great assistance to clinicians. In this investigation, we
employ a Bayesian information-theoretic calibration protocol for experimental
design in order to identify the optimal times at which to collect data for
informing treatment parameters. Within this procedure, data collection times
are chosen sequentially to maximize the reduction in parameter uncertainty with
each added measurement, ensuring that a budget of $n$ high-fidelity
experimental measurements results in maximum information gain about the
low-fidelity model parameter values. In addition to investigating the optimal
temporal pattern for data collection, we also develop a framework for deciding
which metrics should be utilized at each data collection point. We illustrate
this framework with a variety of toy examples, each utilizing a radiotherapy
treatment regimen. For each scenario, we analyze the dependence of the
predictive power of the low-fidelity model upon the measurement budget.
