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Physiological random processes in precision cancer therapy.

Nick Henscheid1,2, Eric Clarkson1,2,3,4, Kyle J Myers5

  • 1Center for Gamma-Ray Imaging, University of Arizona, Tucson, AZ, United States of America.

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This study introduces a mathematical framework using random process theory to model complex cancer physiology. This approach enables precise prediction of individual patient treatment responses for improved precision cancer therapy.

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Area of Science:

  • Mathematical Oncology
  • Cancer Physiology Modeling
  • Stochastic Processes in Biology

Background:

  • Cancer growth and therapy response are influenced by complex, heterogeneous, and dynamic physiological processes.
  • These processes exhibit spatial and genetic heterogeneity, temporal evolution, and inherent unpredictability.
  • Existing models often struggle to capture the full complexity of these dynamic systems.

Purpose of the Study:

  • To present a unified mathematical framework for modeling cancer physiology using the theory of random processes.
  • To demonstrate how this framework can rigorously analyze tumor heterogeneity and dynamic evolution.
  • To enable prediction of therapeutic outcomes for precision cancer therapy.

Main Methods:

  • Modeling physiological processes as random functions of position and time within a tumor.
  • Utilizing the infinite-dimensional characteristic functional to define joint statistics of these functions.
  • Applying maximum-likelihood estimation with Emission Computed Tomography (ECT) data for parameter estimation.

Main Results:

  • Successfully applied the random process theory to models of drug delivery and tumor response.
  • Estimated patient-specific physiological parameters using ECT data.
  • Demonstrated the capability to predict the probability of tumor control for individual patients.

Conclusions:

  • The theory of random processes offers a unified and mathematically rigorous approach to modeling complex cancer physiology.
  • This methodology facilitates personalized treatment strategies by predicting individual patient responses.
  • The framework holds significant potential for advancing precision cancer therapy and improving patient outcomes.