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Hyperthermia is a minimally invasive auxiliary cancer treatment that induces tumor damage and cell death by elevating tissue temperatures. The Arrhenius model is commonly used to evaluate thermal damage in biological tissues. However, variability in key Arrhenius parameters, such as frequency factor (A) and activation energy ( \(E_a\) ), can compromise the therapy planning. This study quantifies how these uncertain inputs might affect the simulation results. So, we consider a three-dimensional breast tissue model governed by Pennes’ bioheat equation, considering different values of A and \(E_a\) found in the literature. Moreover, this study performs the uncertainty quantification analysis via Monte Carlo simulations using a GPU-accelerated implementation. Our results show that uncertainty in A contributes minimally to the damage integral \(\Omega _A\) . In contrast, variability in \(E_a\) broadens the \(95\%\) confidence interval for the critical threshold ( \(\Omega _A \ge 4\) ), extending the required treatment time from approximately 15 to 35 min. These remarks highlight the necessity of precise \(E_a\) estimation to ensure reliable hyperthermia protocols.

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Uncertainty Quantification of Thermal Damage in Hyperthermia as a Cancer Therapy

  • 会议论文

  • 发表日期：

    2025年

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  • Gustavo Coelho Martins,
  • Gustavo Resende Fatigate,
  • Marcelo Lobosco,
  • Ruy Freitas Reis

摘要

Hyperthermia is a minimally invasive auxiliary cancer treatment that induces tumor damage and cell death by elevating tissue temperatures. The Arrhenius model is commonly used to evaluate thermal damage in biological tissues. However, variability in key Arrhenius parameters, such as frequency factor (A) and activation energy ( \(E_a\) ), can compromise the therapy planning. This study quantifies how these uncertain inputs might affect the simulation results. So, we consider a three-dimensional breast tissue model governed by Pennes’ bioheat equation, considering different values of A and \(E_a\) found in the literature. Moreover, this study performs the uncertainty quantification analysis via Monte Carlo simulations using a GPU-accelerated implementation. Our results show that uncertainty in A contributes minimally to the damage integral \(\Omega _A\) . In contrast, variability in \(E_a\) broadens the \(95\%\) confidence interval for the critical threshold ( \(\Omega _A \ge 4\) ), extending the required treatment time from approximately 15 to 35 min. These remarks highlight the necessity of precise \(E_a\) estimation to ensure reliable hyperthermia protocols.