The Role of Blood Perfusion in the Thermal Interaction Between Magnetic Nanoparticles and Cancerous Tumors: A Computational Study

Abstract

In this study, the role of blood perfusion in modulating the thermal response of tumors during magnetic nanoparticle hyperthermia was investigated through computational modeling. The thermal dissipation of 15 nm magnetite nanoparticles was estimated using micromagnetic simulations of their hysteresis loops under a magnetic field of 20 mT and a frequency of 100 kHz. These calculations provided precise energy loss parameters, serving as inputs to simulate the temperature distribution in a tumor embedded within healthy tissue. Temperature-dependent blood perfusion rates, derived from experimental models, were integrated to differentiate the vascular dynamics in normal and cancerous tissues. The simulations were conducted using a bioheat transfer model on a 2D axisymmetric tumor geometry with magnetite nanoparticles dispersed uniformly in the tumor volume. Results showed that tumor tissues exhibit limited blood perfusion enhancement under hyperthermic conditions compared to healthy tissues, leading to localized heat retention favorable for therapeutic purposes. The computational framework validated these findings by achieving therapeutic tumor temperatures (41–45 °C) without significant overheating of surrounding healthy tissues, highlighting the critical interplay between perfusion and energy dissipation. These results demonstrate the efficacy of combining nanoparticle modeling with temperature-dependent perfusion for optimizing magnetic nanoparticle-based hyperthermia protocols.

1. Introduction

During the clinical application of magnetic nanoparticle hyperthermia (MNH), it is desirable to have complete knowledge of the temperature distribution in order to achieve the therapeutic tumor temperature range (41–45 °C) [1]. Invasive temperature measurements can provide information on the tissue temperature at discrete points only when the number of probes that can be implanted is restricted by patient tolerance and practical aspects, and thus the tissue temperature knowledge is limited [2]. Therefore, a mathematical model for calculating tissue temperature during the treatment could prove valuable in complementing the invasive temperature measurements. The difference between applied (cause) and heat-generated (effect) fields, experimentally seen in vitro and in vivo, is overwhelming, and so is the heat distribution [3]. Bridging theory and experiment in these cases requires new efforts in terms of heat measurement techniques and simulation approaches. Once a match between the latter two has been achieved, there will be real chances of the routine use of MNH as a treatment adapted to the specific requirements of each cancer case. One of the main shortcomings of many in vitro models is the disregard of both tumor and body physiological conditions. For the sake of simplicity, biological media are usually considered to be a blood-flooded matrix composed of cells and interstitial space in physical models of hyperthermia [4,5].

Specific heat, thermal, and electrical conductivities, mass density, as well as the dielectric constant of the involved biological media (tumor and healthy tissue) must also be included in models and simulations. In the case of deep-seated tumors, the perturbing effects of bones (with low dielectric constant and thermal conductivity) are usually neglected due to the added complexity and their relatively limited influence on the heat transfer [6,7]. Another important element is vascularization. The heat transfer process greatly depends on blood perfusion, which is different for tumors and normal tissue. Moreover, bifurcations in vessels have an impact on the cooling effect of blood [8]. Finally, the heat sources, i.e., MNPs, must be included so as to complete the basic tumor model of MNH. These may exhibit size distribution, which influences their magnetic properties and, therefore, the heat generation process, and spatial distribution, which determines the formation of “hot spots” and the uniformity of heat deposition in the tissue where MNPs are infused [9,10].

Heat transfer in living tissues is a complicated process because it involves a combination of thermal conduction in tissues, convection and perfusion of blood, and metabolic heat production [11,12,13]. Over the years, several mathematical models have been developed to describe heat transfer within living biological tissues. These models have been widely used in the analysis of hyperthermia in cancer treatment, laser surgery, cryosurgery, cryopreservation, thermal comfort, and many other applications. The most widely used bioheat model was introduced by Pennes in 1948 [14]. Pennes proposed a new simplified bioheat model to describe the effect of blood perfusion and metabolic heat generation on heat transfer within a living tissue. Since the publication of Pennes’ landmark paper, the bioheat transfer equation (BHTE) of his model has been widely used by many researchers for the analysis of bioheat transfer phenomena. For any MNP construct, thermal modeling is critical for understanding the heating efficacy at cellular level. The BHTE can be used to predict temperature profiles during local hyperthermia [15,16]. Despite the development of more accurate temperature prediction models, the BHTE can still be applied to almost every case of thermal modeling [16,17,18,19,20]. When applying the BHTE, the specified magnetic field strength, frequency, background temperature, estimated average perfusion, MNP concentration, and distribution in the target tissue yield an energy term which is then used to estimate the power absorption and temperature distribution within the target region [21,22,23].

Blood perfusion can be the dominant form of energy removal when considering heating processes. It assumes that the blood enters the control volume at some arterial temperature and then comes to equilibrium at tissue temperature. Thus, as the blood leaves the control volume, it carries away the energy and therefore acts as an energy sink in MPH treatment. In many works, blood perfusion is considered as a constant value that is characteristic of the tissue [11,24,25,26]. In reality, blood perfusion is not static; it varies with temperature and time. Tumor microvasculature exhibits unique thermal responses compared to healthy tissues, with an initial hyperperfusion that may decline under prolonged hyperthermic conditions due to vascular collapse or functional impairments [27,28,29]. Incorporating such complexities is crucial for accurately simulating the thermal interaction between magnetic nanoparticles and tumors. Here, we address this gap by integrating temperature- and time-dependent blood perfusion models into a computational framework for MNH simulations.

This study estimated the thermal dissipation of 15 nm magnetite nanoparticles subjected to an alternating magnetic field of 20 mT and 100 kHz using micromagnetic simulations, providing the energy loss parameters necessary for the analysis. Power dissipation was integrated into a bioheat transfer model, and the finite element method (FEM) was employed to solve the heat transfer equations and compute spatial and temporal temperature distributions within a spherical tumor surrounded by healthy tissue. The blood perfusion rates in both tumor and healthy tissues were modeled as dynamic functions of temperature and time, capturing the physiological variability inherent in the process. Prior to the application of MPH in patients, safety and effectiveness must be ensured with the generation of an accurate and reliable treatment plan. Consequently, the need for a numerical model that considers variables such as the tissue density, specific heat and thermal conductivity, metabolic heat generation, and blood perfusion rate is of high importance. Our model takes into account all the suitable boundary conditions and, more importantly, should be utilized within the framework of BHTE tailored to varied perfusion. Accurate treatment planning can allow for the generation of a temperature spatiotemporal distribution which can provide better visualization of the heat transfer present in the tumor region than single-point thermometry. Thermal modeling that is in agreement with physical temperature measurements can complement focal thermal monitoring. By incorporating temperature-dependent and time-dependent blood perfusion rates into the FEM framework, this work delivers a more accurate representation of thermal dynamics during MNH, offering critical insights into optimizing this therapy for effective and safe clinical applications.

2. Materials and Methods

To determine the hysteresis losses of magnetic nanoparticles (MNPs), a detailed micromagnetic numerical model was developed with OOMMF software [30], assuming single-domain, spheroidal particles with identical material composition. The particles interact with one another via magnetostatic dipole–dipole interactions, which were incorporated into the simulation framework. The model simulates the behaviour of an ensemble of MNPs by numerically integrating the Landau–Lifshitz–Gilbert (LLG) [31] equation to calculate their magnetic response under an alternating magnetic field. The energy losses $Q$ were evaluated by calculating the hysteresis loop area and multiplying it by the applied magnetic field frequency. For these simulations, the Fe₃O₄ MNPs were characterized by the following magnetic parameters: a damping coefficient alpha = 0.02, a saturation magnetization of bulk magnetite Ms = 480 kA/m, and a first-order cubic magnetocrystalline anisotropy constant K = −13.5 kJ/m³. Those values were chosen based on the study [32] which investigates a system of MNPs with the same properties, including size, shape, material composition, and anisotropy. This ensures that the selected value is representative of the specific MNPs used in our work. The volume concentration of MNPs in the simulated environment was set to $\mathsf{\varphi }$ = 1%, and the anisotropy axes of individual particles were randomly oriented to mimic realistic spatial distributions. This is an acceptable concentration value usually employed both clinically [33,34,35] and in silico [36]. Simulations were performed at an initial temperature of 300 K to ensure a thermally consistent representation of the system. Field amplitude and frequency were chosen to simulate realistic clinical conditions [9,37] and were set to 20 mT and 100 kHz, respectively.

The finite element model (FEM) utilized in this study comprises a spherical tumor with a radius of 5 mm embedded within a healthy brain tissue region with a radius of 25 mm. This geometric configuration was adopted from the works of K. Maier-Hauff et al. [38,39]. The dimensions align with the realistic anatomical parameters observed in glioblastoma multiforme cases, enhancing the applicability of the model for clinically relevant scenarios. This configuration enables an accurate representation of heat distribution within the tumor and surrounding healthy tissue during magnetic nanoparticle hyperthermia. In order to save computational time and space but simultaneously obtain accurate results for the realistic 3D space, we designed our geometry in a 2D (r,z) axisymmetric model as depicted in Figure 1. The use of a 2D axisymmetric model is a well-established approach in heat transfer studies, as it significantly reduces computational costs while maintaining the key characteristics of heat distribution [40,41]. This approach is particularly valid when considering radially symmetric tumor geometries and homogeneous heating conditions.

The heat transfer simulation was performed using COMSOL 3.5a Multiphysics software [42]. The time-dependent “Bioheat Τransfer” module is utilized to simulate the in vivo MNH scheme studied here. The governing equation reads:

$${{\mathsf{\rho }}_{\mathrm{t}}\mathrm{c}}_{\mathrm{t}}{\displaystyle \frac{\mathsf{\partial }\mathrm{T}}{\mathsf{\partial }\mathrm{t}}}+\nabla ·\left(-{\mathrm{k}}_{\mathrm{t}}\nabla \mathrm{T}\right)={\mathrm{c}}_{\mathrm{b}}{\mathsf{\rho }}_{\mathrm{b}}{\mathsf{\omega }}_{\mathrm{b}}\left({\mathrm{T}}_{\mathrm{b}}-\mathrm{T}\right)+{\mathrm{Q}}_{\mathrm{m}\mathrm{e}\mathrm{t}}+{\mathrm{Q}}_{\mathrm{e}\mathrm{x}\mathrm{t}}$$

(1)

where ρ_t, c_t, T, and k_t are density, specific heat, temperature, and thermal conductivity of the tissue, respectively. In addition, T_b is the temperature of arterial blood, Q_met is the metabolic heat generation in W/m³, and ρ_b, c_b, and ω_b are density, specific heat, and perfusion rate of blood, respectively. Finally, Q_ext is the energy deposition rate in W/m³ which is generated by an external heating source (in our case, the MNPs subjected to AC field) and is related to the MNPs’ hysteresis losses $\mathrm{Q}$ through:

$${\mathrm{Q}}_{\mathrm{e}\mathrm{x}\mathrm{t}}=\mathsf{\varphi }\mathrm{Q}$$

(2)

Metabolic heat generation Q_met is usually very small compared to the external power deposition term Q_ext. It should be noted that metabolic heat generation is assumed to be homogeneously distributed throughout the tissue. The first term on the left part of Equation (1) describes the storage of thermal energy while the first term on the right is related to heat diffusion. The term ρ_bc_bω_b (T_b − T), which accounts for the effects of blood perfusion, can be the dominant form of energy removal when considering heating processes. It assumes that the blood enters the control volume at some arterial temperature, T_b, and then reaches equilibrium at tissue temperature. Thus, as the blood leaves the control volume, it carries away the energy and therefore acts as an energy sink in MPH treatment. Power deposition due to eddy currents can be neglected due to the low amplitude and frequency of the applied alternating magnetic field (20 mT/100 kHz).

The thermophysical and other properties of the tumor and healthy tissue used for simulation are summarized in

Table 1. Thermophysical properties of healthy tissue and tumors taken from [43,44].

Tissue	Thermal Conductivity (W/mK) kt	Density (kg/m3) ρt	Specific Heat (J/kgK) ct	Viscosity (Pas) n	Metabolic Heat Rate (W/m3) Qmet
Healthy tissue	0.64	1040	3693	0.1	6374.5
Tumor	0.57	1000	3500	0.1	31,872.5

. The properties of blood are summarized in

Table 2. Thermophysical properties of blood taken from [32].

	Density (kg/m3) ρb	Specific Heat (J/kgK) cb	Temperature (°C) Tb
Blood	1060	3894	37

.

The simulations also adopt the temperature-dependent blood perfusion rate model introduced by Lang et al. [45], for both the tumor, ω_b,tumor, and the healthy tissue (ω_b,tissue) in (1/s). Those blood perfusion rates depend on the temperature in the following way:

$$\begin{gathered} {\mathsf{\omega }}_{\mathrm{b},\mathrm{t}\mathrm{u}\mathrm{m}\mathrm{o}\mathrm{r}}=\left\{\begin{array}{c}0.000833 \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{T}<37 °\mathrm{C} \\ 0.000833-{\displaystyle \frac{{\left(\mathrm{T}-37\right)}^{4.8}}{5438000}} \mathrm{f}\mathrm{o}\mathrm{r} 37 °\mathrm{C}\le \mathrm{T}\le 42 °\mathrm{C} |\\ 0.000416 \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{T}>42 °\mathrm{C} \end{array}\right. \\ {\mathsf{\omega }}_{\mathrm{b},\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}=\left\{\begin{array}{c}0.00045+0.00355 \mathrm{e}\mathrm{x}\mathrm{p}\left(-{\displaystyle \frac{{\left(\mathrm{T}-45\right)}^2}{12}}\right) \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{T}\le 45 °\mathrm{C}\\ 0.004 \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{T}>45 °\mathrm{C}\end{array}\right. \end{gathered}$$

(3)

Under the “Initial Values” node, the normal body temperature of 37 °C (310.15 K) was set throughout the entire geometry. A general “Heat Source” Q_ext was added in the Tumor–MNPs composite domain, assuming that all the MNPs are uniformly dispersed within the tumor region, with a volume fraction of $\mathsf{\varphi }$. A “Temperature” node of constant temperature (Dirichlet boundary condition type) was selected to describe the conditions on the outer boundary of the healthy tissue. Assuming that a sufficient volume of healthy tissue spans between the heat source and the boundary, the metabolism and perfusion mechanisms should counterbalance the effect of the source, thus the temperature at the boundary should be approximately equal to the normal body temperature of 37 °C. Treatment time was equal to 3600 s. At tumor boundaries, we set the continuity boundary condition. This boundary condition describes the energy transfer between the boundaries of the tumor and the external environment, which is the healthy tissue. The general relation that describes this is the following:

$$\mathbf{n}\cdot ({\mathrm{k}}_1\mathrm{\nabla }{ \mathrm{T}}_1-{\mathrm{k}}_2\mathrm{\nabla }{ \mathrm{T}}_2)=0$$

(4)

where n is the vector perpendicular to the boundary surface. This condition specifies that the heat flux in the normal direction is continuous across the boundary between material 1 (tumor) and material 2 (healthy tissue). The axial symmetry boundary condition is applied along the z-axis of the model. This condition is used in boundaries in order to reduce the overall size of the model and save system resources like CPU time and space. The boundaries of the model are depicted in Figure 2a with different colored arrows.

Since the model contains rotational symmetry, the computational domain can be represented in 2D using cylindrical coordinates. Because of the computation time saved by modeling in 2D, we can select a fine mesh that will provide extremely accurate results. An extra-fine mesh was used throughout the entire geometry as shown in Figure 2b. It consisted of 11,813 triangular domain elements and 586 boundary elements. The time-dependent study of 3600 s is carried out for a time step of 0.1 s and a relative tolerance of 0.001. A direct time-dependent solver is used throughout the simulations that included 23,886 degrees of freedom (DOFs). All the boundary conditions used are presented in

Table 3. Simulating conditions used for each boundary.

Arrows Color Index in Figure 2a	Boundary Condition
red	Axial symmetry
blue	Continuity (Energy Transfer)
black	Dirichlet (constant temperature at 37 °C)

.

3. Results

3.1. Micromagnetic Simulation

The hysteresis loop, depicted in Figure 3, reflects the reduction in magnetization due to the nanoscale size of the particles compared to the bulk material [46]. Furthermore, the low field of 20 mT cannot cause a completely saturated magnetization of the system of interacting nanoparticles since the characteristic field of the cubic anisotropy is as high as 2K/M_s = 60 mT, which is much higher than the coercive field obtained here (≈8 mT). By integrating the data of Figure 3, we found that hysteresis loop area equals to 3.6 J/kg_MNPs. By multiplying this value to the driving frequency value, we found Q_ext $=360 \mathrm{W}/{\mathrm{g}}_{\mathrm{M}\mathrm{N}\mathrm{P}\mathrm{s}}$.

3.2. Blood Perfusion

After using COMSOL 3.5a to numerically solve the time-dependent Equation (1), we obtained the distributions of variables such as blood perfusion, temperature, and MNPs’ volumetric power dissipation in order to appraise the validity of our method. First, we obtained the blood perfusion temporal distribution for the healthy tissue and tumor which is depicted in Figure 4a and Figure 4b, respectively. Note that the blood perfusion rate distributions presented in Figure 4 correspond to average values across the boundaries of both the tumor and healthy tissue. This approach accounts for spatial variations in temperature and provides a representative measure of the perfusion response in each region.

It can be seen that healthy tissue is characterized by a normal vascular system, which supplies the tissue with more blood as the temperature rises in order to extract heat. The vascular system of the tumor is hypertrophic and consequently, at normal temperature, it provides greater blood flow. On the other hand, at higher temperatures, the vascular system of tumor fails to increase the blood flow and therefore reduces it. Thus, it is easier to heat the tumor. This property is beneficial for the whole process of the in vivo MNH.

3.3. Heat Transfer

Then, we estimated temperature distribution within the geometry. The heating source of the tumor are the MNPs, with their losses given by Equation (2). Since blood acts as a heat sink because of perfusion, an efficient amount of energy is required to attain an MNH-appropriate temperature increase, which is illustrated in Figure S1 after t = 3600 s of MNH treatment. Temperature as a function of the radial distance is depicted in Figure 5, together with the distributions obtained for different values of blood perfusion found in the literature. Figure 5 highlights the significant role of blood perfusion in determining the achievable temperatures within the tumor and surrounding healthy tissue. The curves indicate that higher perfusion rates lead to enhanced heat dissipation, resulting in lower steady-state temperatures. Conversely, lower perfusion rates contribute to increased tumor heating. Our model incorporating variable perfusion ω_b = ω_b(T,t) predicts a more realistic temperature distribution, as it accounts for physiological responses to hyperthermia treatment. The results demonstrate that assuming a fixed perfusion rate may underestimate the temperature profile, affecting treatment predictions. This variability emphasizes the need for considering a range of perfusion values, as biological properties are subject to individual differences.

A contour temperature plot within the tumor volume is presented in Figure S2a. We see that the entire volume of the tumor enters the MNH window limits, i.e., between 41 °C and 45 °C, whereas tumor boundaries are above the MNH threshold. On the other hand, the temperature within the healthy tissue did not increase significantly, as illustrated in the contour temperature plot of Figure S2b. The maximum temperature reached in healthy tissue volume is 41 °C, while at the center of the tumor, the temperature reached the value of 45.6 °C. This temperature increase in healthy tissue (ΔΤ = 4 °C) does not affect cellular mechanisms, as already been demonstrated in the literature [47].

To validate our method, temperature values attained both in the center and at the boundaries of the tumor were compared to the corresponding experimental temperature values obtained in a clinical studies [38,39] under the same particles (material, size, concentration) and field (amplitude and frequency) conditions. There, the authors performed heating treatment to patients suffering from glioblastoma multiforme by intracranial injection of the magnetic fluid MFL AS (MagForce Nanotechnologies AG, Berlin, Germany [48]), which is subject to European medical device regulations [35] and consisted of aminosilane-coated magnetite nanoparticles (15 nm core size) dispersed in water at the same concentration (1%) used here. Temperature values attained both in the center and at the boundaries of the tumor are compared to the corresponding experimental temperature values obtained in [29,30] to evaluate our method. The results are presented in

Table 4. Comparison between experimental (Tmeasured) and numerical (Tcalculated) temperature values estimated intratumorally and at tumor boundaries.

Position in Tumor	Tmeasured [29,30] (°C)	Tcalculated (°C)	Deviation (%)
center	44.6	45.6	2.2
boundaries	42	41.5	1.2

where a good agreement between experimentally measured and numerically calculated values is revealed.

Time evolution of temperature at the position where the curvature of their radial distribution changes (2.5 mm from the center of the tumor) and at tumor boundaries (5 mm from the center of the tumor) are depicted in Figure 6.

The results illustrated in Figure 6 show a significant and rapid rise in temperature within the intratumoral region, which stabilizes after approximately 15 min, reaching a steady state. This elevated and sustained temperature is crucial for inducing thermal damage to tumor cells. Conversely, the tumor boundaries exhibit a slower rate of temperature increase and reach a lower steady-state temperature compared to the intratumoral region. This gradient in temperature distribution can be attributed to the heat dissipation effects of surrounding healthy tissues, which have a more robust vascular response and blood perfusion rate. The distinct temperature profile highlights the efficiency of MNH in selectively heating the tumor core while sparing adjacent healthy tissue. Such a differentiation in temperature dynamics between the tumor center and its periphery underscores the importance of the tumor’s impaired vascular system, which limits heat dissipation.

Finally, we estimated the Cumulative Equivalent Minutes at 43 °C (CEM43) to quantify the thermal dose delivered during the treatment based on the expression [49,50]

$$\mathrm{C}\mathrm{E}\mathrm{M}43={\int }_{\mathrm{t}=0}^{{\mathrm{t}}_{\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{l}}}{\mathrm{R}}^{\left(43-\mathrm{T}\left(\mathrm{t}\right)\right)}\mathrm{d}\mathrm{t}$$

(5)

where R is a temperature-dependent factor: R = 0.5R = 0.5 for T ≥ 43 °C and R = 0.25 for T < 43 °C. From temperature profiles in Figure 6, we found that the intratumoral region reaches CEM43 = 60 min within 16 min while the tumor boundary requires 29 min to reach the same dose. These characteristics ensure a therapeutically effective thermal dose to the tumor while minimizing potential thermal damage to the surrounding tissue. These results confirm that the applied heating strategy provides an effective thermal dose in accordance with established hyperthermia treatment standards.

4. Discussion

This study investigates the thermal interaction between magnetic nanoparticles (MNPs) and a cancerous tumor using computational models to evaluate the impact of temperature- and time-dependent blood perfusion rates. The findings illustrate the complex interplay between nanoparticle heat dissipation, tissue perfusion, and tumor physiology, providing critical insights into the optimization of magnetic nanoparticle hyperthermia (MNH) for clinical cancer treatment. The results confirm that tumor tissues can be selectively heated during MNH due to their impaired vascular response, which contrasts with the robust blood perfusion system in healthy tissues. The tumor’s hypertrophic vascular structure initially exhibits elevated blood perfusion compared to normal tissue. However, its inability to maintain this increase under hyperthermic conditions results in reduced heat dissipation, allowing for sustained temperature rise in the tumor core. These findings are consistent with previous studies [3,11,51,52] that highlight the vulnerability of tumor vasculature to thermal stress, which enhances the therapeutic efficacy of hyperthermia treatments.

Micromagnetic simulations of 15 nm magnetite nanoparticles were used to accurately determine their thermal dissipation, incorporating realistic material properties such as saturation magnetization, damping coefficient, and anisotropy. In the temperature range of room temperature (ΔΤ ≈ 20 K), the heating efficiency of MNPs remains largely unaffected, as the Curie temperature of magnetite (≈850 K) is significantly higher, ensuring stable magnetic properties and negligible variations in the specific loss power (SLP) during hyperthermia treatment. The power dissipation derived from these simulations matches well with earlier studies using similar nanoparticle systems [53,54,55,56,57]. The adoption of temperature- and time-dependent blood perfusion models in finite element simulations further advances the state-of-the-art by capturing dynamic physiological responses, addressing a critical limitation in previous literature that relied on static perfusion rates. In conventional models, blood perfusion is treated as a fixed parameter characteristic of specific tissue types, disregarding its physiological response to temperature changes. However, in reality, perfusion varies with temperature, particularly in tumor tissues where vascular regulation is impaired. The assumption of a constant perfusion rate leads to an underestimation of the tumor’s temperature rise during hyperthermia. This could result in an inaccurate assessment of treatment efficacy, as the thermal dose delivered to the tumor would appear lower than what actually occurred. In contrast, our model accounts for the reduction in tumor perfusion at higher temperatures, better reflecting the real physiological response and predicting a more localized and sustained heating effect. The implications of this discrepancy are critical for hyperthermia treatment planning. Underestimating the temperature increase could lead to suboptimal treatment protocols, potentially reducing therapeutic effectiveness. Conversely, an overestimation of heat dissipation in healthy tissues could cause unnecessary concerns regarding thermal side effects. Our findings highlight that incorporating a variable perfusion model is essential for improving the accuracy of MNH simulations, ensuring a more reliable prediction of the thermal response and enhancing the precision of clinical hyperthermia applications. Furthermore, the inclusion of parametric analysis strengthens the study by providing a broader perspective on treatment effectiveness under different physiological conditions.

From the bioheat transfer simulations, it is evident that the MNH treatment achieves therapeutic temperatures (41–45 °C) in the tumor without overheating the surrounding healthy tissue. This selective heating highlights the potential of MNH in minimizing adverse side effects, which is a persistent challenge in other cancer treatment modalities like chemotherapy and external beam radiotherapy. The use of realistic anatomical dimensions for the tumor and brain geometry, based on the work of Maier-Hauff et al. [29,30], ensures clinical relevance and applicability of the results to glioblastoma multiforme treatment scenarios. The findings also endorse the working hypothesis that the differential thermal and vascular responses of tumors and healthy tissues can be exploited to achieve precise and localized heating of tumors during MNH. In our model, we assume that the heat generated by MNPs via hysteresis losses is uniformly distributed within the tumor volume. This assumption is based on the use of surfactants and other stabilizing agents used in the literature that help ensure a homogeneous dispersion of MNPs, thereby minimizing aggregation effects. Variations in MNP concentration may occur due to aggregation driven by strong dipolar interactions. These spatial inhomogeneities could lead to localized differences in heating efficiency. Our recent study [58] has demonstrated that MNP concentration significantly affects heating efficiency, as evidenced by specific loss power (SLP) estimations derived from micromagnetic simulations. In scenarios where aggregation occurs, the overall heating efficiency might be reduced, potentially leading to deviations in the predicted temperature profiles. Thus, several factors require further investigation. For example, nanoparticle distribution within the tumor and agglomeration effects could influence the overall heat generation, warranting more advanced modeling techniques. Similarly, the impacts of non-uniform tissue properties and variations in vascular density need to be explored to refine the predictive accuracy of computational models.

Future research should focus on validating these computational findings with experimental in vivo studies to establish robust correlations between modeling predictions and biological outcomes. Investigating the long-term effects of MNH treatment, including tumor recurrence and vascular recovery, would provide a more comprehensive understanding of its therapeutic potential. Furthermore, the combination of MNH with other treatment modalities, such as immunotherapy or targeted drug delivery, could open new avenues for enhancing therapeutic outcomes.

5. Conclusions

By employing the experimental conditions and MNP parameters that were used in a clinical application of MNH found in the literature, bioheat transfer modeling was carried out in order to visualize the temperature profile within the biological tissues (tumor and healthy tissue). Simulations revealed that temperature increase and heating rate within the tumor are maintained in ideal therapeutic levels (within moderate hyperthermia levels) without affecting thermally the healthy tissue. The results are also verified in the literature and validate our technique.

The modeling of heat generation and transfer process in MNH will increase the understanding of physical phenomena and allow a successful transition of this technology from bench to bedside. In addition, modeling through simulation can be used in the planning of the MPH treatment and it also serves as a new alternative method for temperature mapping due to the difficulty in real-time clinical temperature measurement during the treatment. Conclusively, the beneficial role of modeling implementation in an in vivo MPH scheme can be illustrated through the following aspects:

(i)

MPH treatment can be optimized with respect to the heat application geometry by maximizing the therapeutic effect while minimizing unwanted side effects.

(ii)

The outcome of the treatment can be evaluated based on model predictions.

(iii)

It can also be used for extensive parametric studies in order to characterize the stability of various treatment parameters and conditions.

(iv)

New treatment strategies can be proposed and evaluated.