COMPARATIVE ANALYSIS OF HEAT TRANSFER MODELS IN MAGNETIC HYPERTHERMIA: BENCHMARKING AGAINST EXPERIMENTAL DATA

Magnetic hyperthermia, a promising cancer treatment technique, utilizes magnetic nanoparticles to generate localized heating in tissues. Accurate modeling of heat transfer is essential for predicting thermal responses and optimizing treatment protocols. While comparisons of heat transfer models have been conducted for other types of hyperthermia, no comprehensive evaluation has been made specifically for magnetic hyperthermia using nanoparticles. In this study, we address this gap by comparing the performance of three heat transfer models-local thermal equilibrium (LTE), Pennes, and local thermal nonequilibrium (LTNE)-in simulating the thermal dynamics of magnetic hyperthermia. The models were tested against experimental data from vivo studies with varying nanoparticle concentrations and magnetic field intensities. The LTNE model, which accounts for LTNE between solid and fluid phases in tissue, consistently outperformed the LTE and Pennes models. Statistical analysis revealed that the LTNE model exhibited the lowest mean percentage difference (7.04%) and mean absolute temperature difference (2.74°C) compared to experimental data. In contrast, the LTE and Pennes models showed larger deviations, with mean percentage differences of 13.01% and 12.67%, respectively. These findings highlight the LTNE model's superior ability to predict temperature distributions and tissue damage in magnetic hyperthermia, making it a more reliable tool for simulation. This study underscores the importance of model selection for accurate prediction and optimization in nanoparticle-based hyperthermia treatments.

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Publication Details

Journal
Computational Thermal Sciences An International Journal
Published
2025-01-01
DOI
https://doi.org/10.1615/computthermalscien.2025057348
Citations
2
Primary Topic
Characterization and Applications of Magnetic Nanoparticles
Type
article
Field-Weighted Citation Impact
1.35
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article

COMPARATIVE ANALYSIS OF HEAT TRANSFER MODELS IN MAGNETIC HYPERTHERMIA: BENCHMARKING AGAINST EXPERIMENTAL DATA

Giuseppe Peter Vanoli, Assunta Andreozzi, Marcello Iasiello, A. Cafarchio
2 citations
Computational Thermal Sciences An International Journal
Characterization and Applications of Magnetic Nanoparticles
1.35
article

COMPARATIVE ANALYSIS OF HEAT TRANSFER MODELS IN MAGNETIC HYPERTHERMIA: BENCHMARKING AGAINST EXPERIMENTAL DATA

Giuseppe Peter Vanoli, Assunta Andreozzi, Marcello Iasiello, A. Cafarchio
article en
2 citations

Abstract

Magnetic hyperthermia, a promising cancer treatment technique, utilizes magnetic nanoparticles to generate localized heating in tissues. Accurate modeling of heat transfer is essential for predicting thermal responses and optimizing treatment protocols. While comparisons of heat transfer models have been conducted for other types of hyperthermia, no comprehensive evaluation has been made specifically for magnetic hyperthermia using nanoparticles. In this study, we address this gap by comparing the performance of three heat transfer models-local thermal equilibrium (LTE), Pennes, and local thermal nonequilibrium (LTNE)-in simulating the thermal dynamics of magnetic hyperthermia. The models were tested against experimental data from vivo studies with varying nanoparticle concentrations and magnetic field intensities. The LTNE model, which accounts for LTNE between solid and fluid phases in tissue, consistently outperformed the LTE and Pennes models. Statistical analysis revealed that the LTNE model exhibited the lowest mean percentage difference (7.04%) and mean absolute temperature difference (2.74°C) compared to experimental data. In contrast, the LTE and Pennes models showed larger deviations, with mean percentage differences of 13.01% and 12.67%, respectively. These findings highlight the LTNE model's superior ability to predict temperature distributions and tissue damage in magnetic hyperthermia, making it a more reliable tool for simulation. This study underscores the importance of model selection for accurate prediction and optimization in nanoparticle-based hyperthermia treatments.

Computational Thermal Sciences An International JournalVol. 17(4)
University of Molise (IT), University of Naples Federico II (IT)
Openalex Percentile: Top 18%
Characterization and Applications of Magnetic Nanoparticles
1.35
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