An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models

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Abstract
  • The use of mathematical models to make predictions about tumor growth and response to treatment has become increasingly prevalent in the clinical setting. The level of complexity within these models ranges broadly, and the calibration of more complex models requires detailed clinical data. This raises questions about the type and quantity of data that should be collected and when, in order to maximize the information gain about the model behavior while still minimizing the total amount of data used and the time until a model can be calibrated accurately. To address these questions, we propose a Bayesian information-theoretic procedure, using an adaptive score function to determine the optimal data collection times and measurement types. The novel score function introduced in this work eliminates the need for a penalization parameter used in a previous study, while yielding model predictions that are superior to those obtained using two potential pre-determined data collection protocols for two different prostate cancer model scenarios: one in which we fit a simple ODE system to synthetic data generated from a cellular automaton model using radiotherapy as the imposed treatment, and a second scenario in which a more complex ODE system is fit to clinical patient data for patients undergoing intermittent androgen suppression therapy. We also conduct a robust analysis of the calibration results, using both error and uncertainty metrics in combination to determine when additional data acquisition may be terminated.
TitleAn adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models
CreatorStorey, Kathleen M.
Zittle, A. C.
Lewis, Allison L.
Cho, H.
PublisherMathematical Biosciences and Engineering
Academic DepartmentMathematics
DivisionNatural Sciences
OrganizationLafayette College
Date IssuedSeptember 19, 2023
Date Available2023-09-27
TypeArticle
LanguageEnglish
Keywordprostate cancer models
sequential experimental design
Bayesian inference
personalized medicine
data-driven mathematical oncology
Bibliographic CitationCho, H., et al. (2023 Sept 19) "An adaptive information-theoretic experimental design procedure for high-to-low fidelity calibration of prostate cancer models." Mathematical Biosciences and Engineering 20 (10): 17986–18017.
Standard IdentifierHandle 10385/sf268660s
DOI 10.3934/mbe.2023799
Permalinkhttp://hdl.handle.net/10385/sf268660s
Rights Statement Creative Commons - Attribution Creative Commons - Attribution
Rights HoldersZittle, A. C.
Storey, Kathleen M.
Lewis, Allison L.
Cho, H.
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File Lewis-MathematicalBiosciencesandEngineering-vol20-2023.pdf Uploaded 2023-09-27 Public Download
File Lewis-MathematicalBiosciencesandEngineering-vol20-2023.pdf Uploaded 2023-09-27 Public Download

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