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With new advancements in technology, it is now possible to collect data for avariety of different metrics describing tumor growth, including tumor volume,composition, and vascularity, among others. For any proposed model of tumorgrowth 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 behelpful to infer such patient-specific parameters both accurately and early, sothat treatment protocols can be adjusted mid-course for maximum efficacy.However, taking measurements can be costly and invasive, limiting clinicians toa sparse collection schedule. As such, the determination of optimal times andmetrics for which to collect data in order to best inform proper treatmentprotocols could be of great assistance to clinicians. In this investigation, weemploy a Bayesian information-theoretic calibration protocol for experimentaldesign in order to identify the optimal times at which to collect data forinforming treatment parameters. Within this procedure, data collection timesare chosen sequentially to maximize the reduction in parameter uncertainty witheach added measurement, ensuring that a budget of $n$ high-fidelityexperimental measurements results in maximum information gain about thelow-fidelity model parameter values. In addition to investigating the optimaltemporal pattern for data collection, we also develop a framework for decidingwhich metrics should be utilized at each data collection point. We illustratethis framework with a variety of toy examples, each utilizing a radiotherapytreatment regimen. For each scenario, we analyze the dependence of thepredictive power of the low-fidelity model upon the measurement budget.

Bayesian information-theoretic calibration of patient-specific  radiotherapy sensitivity parameters for informing effective scanning  protocols in cancer