Clinical magnetic hyperthermia requires integrated magnetic particle imaging

Abstract

Magnetic nanomaterials that respond to clinical magnetic devices have significant potential as cancer nanotheranostics. The complexities of their physics, however, introduce challenges for these applications. Hyperthermia is a heat-based cancer therapy that improves treatment outcomes and patient survival when controlled energy delivery is combined with accurate thermometry. To date, few technologies have achieved the needed evolution for the demands of the clinic. Magnetic fluid hyperthermia (MFH) offers this potential, but to be successful it requires particle-imaging technology that provides real-time thermometry. Presently, the only technology having the potential to meet these requirements is magnetic particle imaging (MPI), for which a proof-of-principle demonstration with MFH has been achieved. Successful clinical translation and adoption of integrated MPI/MFH technology will depend on successful resolution of the technological challenges discussed. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Keywords: Cancer; magnetic fluid hyperthermia; magnetic nanoparticles; magnetic particle imaging; theranostics.

FIGURE 1

Magnetic iron oxide nanoparticles (MIONPs) exhibit unique behavior that can be exploited for hyperthermia. (a) A collection of freely suspended and non‐interacting single‐domain, spherical magnets are magnetically isotropic at T = 310.15 K (37°C) with zero measured moment (M = 0) with no magnetic field (H = 0) (far left). However, moments of the nanomagnets (white arrows) will align with the direction of an external field (H > 0), yielding a net magnetization (M > 0) (middle left). When the external field is removed, nanomagnet moments relax to isotropic condition, M → 0, with a characteristic time scale τ, that is determined by inherent properties of the nanomagnets and experimental conditions. This is called superparamagnetism and occurs through a combination of internal moment reversal (Néel relaxation, blue arrows) and whole particle rotations (Brownian relaxation, red arrows) (middle right). Application of a magnetic field in the reverse direction aligns moments with the field (far right). (b) When a collection of freely suspended and non‐interacting MIONPs is exposed to an external field that changes direction at a time scale slower than τ, MIONP moments align instantaneously with the field, yielding a magnetization that follows a reversible path mimicking superparamagnetism (left). With increasing magnetic field frequency f, or decreasing temperature T, individual moments lag causing the magnetization to trace an irreversible path, or hysteresis that signifies a transition out of the superparamagnetic regime. Magnetic hysteresis dissipates heat, proportional to the area of the hysteresis loop, with total heat produced by repeated and rapid cycling in an alternating magnetic field (AMF). (c) For clinical magnetic fluid hyperthermia (MFH), MIONPs must generate sufficient heat in an AMF within the clinical limits of f and H (dashed vertical line). Feridex®, a magnetic resonance imaging (MRI) MIONP contrast agent displays superparamagnetism (i.e., SPM MIONP, or SPION) at the test conditions, yielding negligible heating. The other MIONPs are not superparamagnets and generate heat at the test conditions, but display differing hysteresis responses to H. Magnetic moments of crystallites or particles can influence relaxation behavior of neighbors, influencing response of the system to external magnetic fields. This leads to collective behavior that can be used to tailor heating properties of magnetic nanoparticles for medical applications (see text). Data previously published in Bordelon et al. (2011) and Dennis et al. (2015) were used in the figure

FIGURE 2

Block diagram of imaging‐guided MFH clinical workflow that relies on an integrated MPI‐MFH‐MNT device. Future imaging‐guided MFH would mimic clinical workflow used in modern radiation oncology to treat solid tumors with ionizing radiation. Separately, the device can provide diagnostic imaging with the same MIONPs used for therapy, and once disease is confirmed, anatomical imaging (CT and/or MRI) can be coregistered for treatment planning

FIGURE 3

MIONP intratumor distribution is unpredictable and heterogeneous, requiring direct imaging for MFH treatment planning. (a) Prussian blue‐stained human prostate cancer (PC3 and LAPC4) tumor tissue xenografts grown in male nude mice (single sections) were used as models. Tumor volume was 0.15 ± 0.02 cm³ and 5 mg Fe/cm³ of tumor was directly injected into tumors (reprinted with permission from Attaluri et al., 2015). (b) Histology sections were digitized and images processed in MATLAB. (c) Fe recovered from tumors using ICP MS shows that the total concentration in tumors also varies. (d) Computed 2D temperature distributions using digitized MIONP distribution shows MFH with amplitude‐modulated control reduces temperature heterogeneity when compared to constant power MFH. Images derived from MIONP distributions shown in (a) and (b). In LAPC4, MIONPs show relatively uniform distributions; whereas, in PC3 the MIONPs were concentrated along the major axis. Power modulation ameliorated the effects of MIONP heterogeneity by reducing the area of hot zone near high concentrations of MIONPs and reduced the temperature at the tumor‐tissue boundary. T _max and T _min are the maximum and minimum temperatures inside the tumor, respectively. T ₁₀ and T ₉₀ are the temperatures achieved by at least 10% and 90% of the tumor area, respectively, with the dimensionless heterogeneity coefficient indicating the temperature heterogeneity relative to T ₉₀, or temperature uniformity. Note that for the concentrated MIONPs in the PC3 tumor model, modulated power did not enhance temperature uniformity, but it reduced the area of ablation in the tumor, and reduced damage to normal tissue. Precise onboard imaging of MIONPs for each tumor with 3D distribution will improve MFH for precision therapy. Reprinted with permission from Kandala et al. (2019)

FIGURE 4

Pulsed AMF during MFH provides an effective means to manage/reduce adverse effects of off‐target induced‐eddy current heating in tissues. (a) Pulsing power and reducing duty cycle (ratio of working time to total time, where 100% is continuous power) enables physiological thermoregulatory cooling to effectively dissipate heat during power OFF cycles. Shown are simulated temperature distributions for 100%, 50%, 33.3%, and 25% duty cycles in a human scale computational model after 20 min of MIONP treatment. MIONPs are in the center of the circular diagram. (b) Simulated temperature rise for 100%, 50%, 33.3%, and 25% duty cycles in human scale computational models during MFH treatment demonstrate that by increasing treatment time, MIONP heating achieves similar temperatures in the tumor target. Note the change in the time scale on x‐axis. Reprinted with permission from Attaluri, Kandala, et al. (2020)

FIGURE 5

A proof‐of‐concept MPI integration with MFH demonstration. (a) Hardware setup for MPI scanner and image‐guided MPI/MFH. (Left) MPI scanner. To obtain an image, the sensitive field‐free‐region (FFR) is rastered through a volume. In this example, a field‐free‐line (FFL) geometry was used, and images obtained are similar to projection scintigraphy. (Top right) MFH with MPI. A separate higher‐frequency MPI/MFH scanner with the same FFL geometry was used for application of MFH. For image‐guided therapy, coordinates were matched between the two scanners such that the imaging information from the first scanner was used to define MFH therapeutic trajectories in the MPI/MFH system. The user was able to select a target from the MPI image, and the corresponding coordinates on the image was sent to the MPI/MFH heating scanner. The robot arm shifted the co‐registered animal bed to center the FFL of the heating scanner to the requested coordinates. To locally heat only the target, the FFL was held in place over the target while a higher frequency (354 kHz) AMF excitation field was applied. (b) Designing a heating “prescription” using MPI/MFH integrated scanner and a forward model to predict the temperature distribution from the MPI data of the MIONP biodistribution. To predict the localization effect of the MPI gradients, the gradient‐less SAR (or SLP) image with the known suppression effect of the MPI gradients (SAR filter) with FFR centered at the therapeutic target can be used. A prediction of the SAR/SLP dose after gradient localization, which can be integrated into the heat transfer modeling to calculate CEM43. To account for heat transfer, the previous image would be convolved with a temperature spatial point spread function (PSF). Reproduced with permission from Tay et al. (2018)