Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer

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Abstract
  • 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.
TitleBayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer
CreatorLewis, Allison L.
Cho, H.
Storey, K. M.
PublisherJournal of Clinical Medicine
Academic DepartmentMathematics
DivisionNatural Sciences
OrganizationLafayette College
Date IssuedOctober 5, 2020
Date Available2020-10-12
TypeArticle
LanguageEnglish
Keywordradiotherapy treatment
bayesian experimental design
scanning protocol
mutual information
tumor growth models
Bibliographic CitationHo, C., A. L. Lewis, and K. M. Storey. 2020 Oct 5. "Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer." Journal of Clinical Medicine 9: 3208.
Standard IdentifierDOI 10.3390/jcm9103208
Handle 10385/p5547s34w
Permalinkhttp://hdl.handle.net/10385/p5547s34w
Rights Statement Creative Commons - Attribution Creative Commons - Attribution
Rights HoldersLewis, Allison L.
Cho, H.
Storey, K. M.
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