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Thermoembolization presents a unique treatment alternative for patients diagnosed with hepatocellular carcinoma. The approach delivers a reagent that undergoes an exothermic chemical reaction and combines the benefits of embolic as well as thermal- and chemical-ablative therapy modalities. The target tissue and vascular bed are subjected to simultaneous hyperthermia, ischemia, and chemical denaturation in a single procedure. To guide optimal delivery, we developed a mathematical model for understanding the competing diffusive and convective effects observed in thermoembolization delivery protocols. A mixture theory formulation was used to mathematically model thermoembolization as chemically reacting transport of an electrophile, dichloroacetyl chloride (DCACl), within porous living tissue. Mass and energy transport of each relevant constituent are considered. Specifically, DCACl is injected into the vessels and exothermically reacts with water in the blood or tissue to form dichloroacetic acid and hydrochloric acid. Neutralization reactions are assumed instantaneous in this approach. We validated the mathematical model predictions of temperature using MR thermometry of the thermoembolization procedure performed in ex vivo kidney. Mathematical modeling predictions of tissue death were highly dependent on the vascular geometry, injection pressure, and intrinsic amount of exothermic energy released from the chemical species, and were able to recapitulate the temperature distributions observed in MR thermometry. These efforts present a first step toward formalizing a mathematical model for thermoembolization and are promising for providing insight for delivery protocol optimization. While our approach captured the observed experimental temperature measurements, larger-scale experimental validation is needed to prioritize additional model complexity and fidelity. [ABSTRACT FROM AUTHOR]

Post-treatment imaging is the principal method for evaluating thermal lesions following image-guided thermal ablation procedures. While real-time temperature feedback using magnetic resonance temperature imaging (MRTI) is a complementary tool that can be used to optimise lesion size throughout the procedure, a thermal dose model is needed to convert temperature-time histories to estimates of thermal damage. However, existing models rely on empirical parameters derived from laboratory experiments that are not direct indicators of post-treatment radiologic appearance. In this work, we investigate a technique that uses perioperative MR data to find novel thermal dose model parameters that are tailored to the appearance of the thermal lesion on post-treatment contrast-enhanced imaging. Perioperative MR data were analysed for five patients receiving magnetic resonance-guided laser-induced thermal therapy (MRgLITT) for brain metastases. The characteristic enhancing ring was manually segmented on post-treatment T1-weighted imaging and registered into the MRTI geometry. Post-treatment appearance was modelled using a coupled Arrhenius-logistic model and non-linear optimisation techniques were used to find the maximum-likelihood kinetic parameters and dose thresholds that characterise the inner and outer boundary of the enhancing ring. The parameter values and thresholds were consistent with previous investigations, while the average difference between the predicted and segmented boundaries was on the order of one pixel (1 mm). The areas predicted using the optimised model parameters were also within 1 mm of those predicted by clinically utilised dose models. This technique makes clinically acquired data available for investigating new thermal dose model parameters driven by clinically relevant endpoints. [ABSTRACT FROM AUTHOR]

Purpose:Neurosurgical laser ablation is experiencing a renaissance. Computational tools for ablation planning aim to further improve the intervention. Here, global optimisation and inverse problems are demonstrated to train a model that predicts maximum laser ablation extent. Methods:A closed-form steady state model is trained on and then subsequently compared toN = 20 retrospective clinical MR thermometry datasets. Dice similarity coefficient (DSC) is calculated to provide a measure of region overlap between the 57 °C isotherms of the thermometry data and the model-predicted ablation regions; 57 °C is a tissue death surrogate at thermal steady state. A global optimisation scheme samples the dominant model parameter sensitivities, blood perfusion (ω) and optical parameter (μeff) values, throughout a parameter space totalling 11 440 value-pairs. This represents a lookup table ofμeff–ωpairs with the corresponding DSC value for each patient dataset. Theμeff–ωpair with the maximum DSC calibrates the model parameters, maximising predictive value for each patient. Finally, leave-one-out cross-validation with global optimisation information trains the model on the entire clinical dataset, and compares against the model naïvely using literature values forωandμeff. Results:When using naïve literature values, the model’s mean DSC is 0.67 whereas the calibrated model produces 0.82 during cross-validation, an improvement of 0.15 in overlap with the patient data. The 95% confidence interval of the mean difference is 0.083–0.23 (p < 0.001). Conclusions:During cross-validation, the calibrated model is superior to the naïve model as measured by DSC, with +22% mean prediction accuracy. Calibration empowers a relatively simple model to become more predictive. [ABSTRACT FROM PUBLISHER]

Purpose: Optically activated nanoparticle-mediated heating for thermal therapy applications is an area of intense research. The ability to characterise the spatio-temporal heating potential of these particles for use in modelling under various exposure conditions can aid in the exploration of new approaches for therapy as well as more quantitative prospective approaches to treatment planning. The purpose of this research was to investigate an inverse solution to the heat equation using magnetic resonance temperature imaging (MRTI) feedback, for providing optical characterisation of two types of nanoparticles (gold-silica nanoshells and gold nanorods). Methods: The optical absorption of homogeneous nanoparticle-agar mixtures was measured during exposure to an 808 nm laser using real-time MRTI. A coupled finite element solution of heat transfer was registered with the data and used to solve the inverse problem. The L2 norm of the difference between the temperature increase in the model and MRTI was minimised using a pattern search algorithm by varying the absorption coefficient of the mixture. Results: Absorption fractions were within 10% of literature values for similar nanoparticles. Comparison of temporal and spatial profiles demonstrated good qualitative agreement between the model and the MRTI. The weighted root mean square error was <1.5 σMRTI and the average Dice similarity coefficient for ΔT = 5 °C isotherms was >0.9 over the measured time interval. Conclusion: This research demonstrates the feasibility of using an indirect method for making minimally invasive estimates of nanoparticle absorption that might be expanded to analyse a variety of geometries and particles of interest. [ABSTRACT FROM AUTHOR]

Purpose: A generalised polynomial chaos (gPC) method is used to incorporate constitutive parameter uncertainties within the Pennes representation of bioheat transfer phenomena. The stochastic temperature predictions of the mathematical model are critically evaluated against MR thermometry data for planning MR-guided laser-induced thermal therapies (MRgLITT). Methods: The Pennes bioheat transfer model coupled with a diffusion theory approximation of laser tissue interaction was implemented as the underlying deterministic kernel. A probabilistic sensitivity study was used to identify parameters that provide the most variance in temperature output. Confidence intervals of the temperature predictions are compared to MR temperature imaging (MRTI) obtained during phantom and in vivo canine ( n = 4) MRgLITT experiments. The gPC predictions were quantitatively compared to MRTI data using probabilistic linear and temporal profiles as well as 2-D 60 °C isotherms. Results: Optical parameters provided the highest variance in the model output (peak standard deviation: anisotropy 3.51 °C, absorption 2.94 °C, scattering 1.84 °C, conductivity 1.43 °C, and perfusion 0.94 °C). Further, within the statistical sense considered, a non-linear model of the temperature and damage-dependent perfusion, absorption, and scattering is captured within the confidence intervals of the linear gPC method. Multivariate stochastic model predictions using parameters with the dominant sensitivities show good agreement with experimental MRTI data. Conclusions: Given parameter uncertainties and mathematical modelling approximations of the Pennes bioheat model, the statistical framework demonstrates conservative estimates of the therapeutic heating and has potential for use as a computational prediction tool for thermal therapy planning. [ABSTRACT FROM AUTHOR]

Purpose: Sub-lethal temperature elevations in the tumour incurred during laser cancer therapy can induce heat shock protein (HSP) expression leading to enhanced tumour survival and recurrence. Nanoshells utilised in combination with laser therapy can potentially enable selective heat deposition, greater thermal injury, and diminished HSP expression in the tumour. The study objective was to measure the distribution of temperature and HSP expression in prostate tumours in response to laser therapy alone or with nanoshells to determine if these combinatorial therapies can minimise HSP expression. Methods: PC3 cells were inoculated in the backs of CB17-Prkd c SCID/J mice and treated with external laser irradiation (wavelength of 810 nm, irradiance of 5 W/cm2, spot size of 5 mm, and heating duration of 3 min) alone or in combination with gold nanoshells (diameter of 55 nm and outer gold shell thickness of 10 nm) introduced into the tumour 24 h prior to laser treatment. Magnetic resonance temperature imaging was used to measure the distribution of temperature elevation in the tumours during laser treatment. Tumours were sectioned 16 h following laser treatment, stained for Hsp27 and Hsp70, imaged with a confocal microscope, and HSP expression levels were quantified as a function of depth in the tumours. Results: Maximum temperature elevations at the tumour surface were 28°C for laser treatment only and 50°C for laser heating in combination with gold nanoshells. Laser therapy alone caused significant induction of HSP expression in the first few millimeters of the tumour depth, whereas decreasing HSP expression occurred with greater tumour depth. Tumours treated with laser and nanoshells experienced substantial temperatures (73-78°C) at the tumour surface and temperatures greater than 53°C in the first few millimeters which eliminated HSP expression. Conclusion: Inclusion of nanoshells in laser therapy can provide a mechanism for enhancing heat deposition capable of eliminating HSP expression within a larger tumour region compared to laser heating alone. [ABSTRACT FROM AUTHOR]

Minimally invasive thermal therapy using high-power diode lasers is an active area of clinical research. Gold nanoshells (AuNS) can be tuned to absorb light in the range used for laser ablation and may facilitate more conformal tumor heating and sparing of normal tissue via enhanced tumor specific heating. This concept was investigated in a xenograft model of prostate cancer (PC-3) using MR temperature imaging (MRTI) in a 1.5T scanner to characterize the spatiotemporal temperature distribution resulting from nanoparticle mediated heating. Tumors with and without intravenously injected AuNS were exposed to an external laser tuned to 808 nm for 180 sec at 4 W/cm2 under real-time monitoring with proton resonance frequency shift based MRTI. Microscopy indicated that these nanoparticles (140-150 nm) accumulated passively in the tumor and remained close to the tumor microvasculature. MRTI measured a statistically significant ( p < 0.001) increase in maximum temperature in the tumor cortex (mean == 21 ± 7°C) in ++AuNS tumors versus control tumors. Analysis of the temperature maps helped demonstrate that the overall distribution of temperature within ++AuNS tumors was demonstrably higher versus control, and resulted in damage visible on histopathology. This research demonstrates that passive uptake of intravenously injected AuNS in PC-3 xenografts converts the tumor vasculature into a potent heating source for nanoparticle mediated ablation at power levels which do not generate significant damage in normal tissue. When used in conjunction with MRTI, this has implications for development and validation of more conformal delivery of therapy for interstitial laser ablations. [ABSTRACT FROM AUTHOR]