Predicted SAR/temperature changes induced by phase-amplitude steering are minimally affected by uncertainties in tissue properties: a basis for robust on-line adaptive hyperthermia treatment planning

BACKGROUND

Reliability of absolute specific absorption rate (SAR)/temperature levels predicted by treatment planning is strongly affected by tissue parameter uncertainties. Therefore, regular re-optimization to suppress hot spots can accidentally induce new hot spots elsewhere. Adaptive planning methods to avoid this problem re-optimize with respect to the current predicted 3D-distribution. This strategy is robust if reliability of predicted SAR/temperature changes (i.e., increases/decreases) after phase-amplitude adjustments is minimally affected by parameter uncertainties; this work evaluated this robustness.

METHODS

We validated the basic concept in an inhomogeneous phantom, followed by a patient model. Uncertainties in electrical conductivity, permittivity and perfusion were mimicked by simulations using 100 random parameter samples from normal distributions. Reliability of predicted SAR/temperature increase/decrease after phase-amplitude adjustments was evaluated. Next, correlations between measured and simulated SAR and SAR changes were determined for phase settings evaluated at the treatment start for a treatment series. Finally, practical use in an adaptive workflow was illustrated.

RESULTS

Local SAR/temperature increases/decreases after phase-amplitude adjustments can be predicted accurately. For the phantom, the measured 28.5% SAR decrease was predicted accurately(28.5 ± 0.7%). In the patient model, predicted SAR/temperature changes were typically accurate within a few percent. For the treatment series, correlations between measured and simulated (relative) SAR changes were much better(R2=0.70-0.82) than for absolute SAR levels(R2=0.29). Predictions of steering effects during treatment corresponded qualitatively with measurements/observations.

CONCLUSION

Predictions of SAR/temperature increases/decreases induced by phase-amplitude steering are hardly affected by tissue parameter uncertainties. On-line adaptive planning based on predicted changes is thus robust to effectively support clinical steering strategies.

Optimization of microwave hyperthermia system for focused breast cancer treatment: A study using realistic digital breast phantoms

BACKGROUND

Microwave breast hyperthermia is a noninvasive treatment method for breast cancer that utilizes microwave energy (ME) sources to raise tissue temperatures above 42∘ C $^{\circ }{\rm C}$ , inducing tumor cell necrosis. The efficiency of ME deposition depends on the electric field magnitude and tissue conductivity, with antenna phase and amplitude adjustments used to maximize the electric field magnitude within tumors. Achieving precise ME focusing in the complex and heterogeneous breast tissue is challenging and can lead to unwanted hot spots in normal tissue. This study presents a novel method for optimizing ME focusing on the center of target tumors, using a simplified calculation of antenna phases, heuristic optimization for antenna amplitudes, and realistic breast phantoms for performance evaluation.

PURPOSE

In this work, we propose an approach to optimize the microwave hyperthermia system, employing phase and amplitude modulation techniques to concentrate the electric field at the center of a malignant tumor within a breast medium. The approach uses line sources arranged in a circular pattern around realistic breast models. The method begins by determining the phase, followed by adjusting the amplitudes of each source in order to maximize the total electric field at the tumor's center. The goal is to maximize the electric field at the tumor center while minimizing the optimization cost and complexity.

METHODS

Simulations are performed at 4 GHz frequency using two different types of digital breast phantoms (fatty and dense breasts) as test beds. The algorithm is tested by using three quantities; that is, the electric field distribution, the power density distribution, and the temperature distribution inside the whole breast region. The electric field and power density are calculated using an in-house method of moments (MoM) algorithm, while the temperature distributions are obtained with computer simulation technology (CST) software. To further evaluate the method with quantitative measures of success, thermal indices are calculated for each phantom and method.

RESULTS

The specific absorbtion rate (SAR) results and corresponding temperature distributions for each breast type and optimization demonstrate that effective focusing is achieved in both cases. However, the combined phase-amplitude optimization provides more precise focusing by eliminating hot spots. Among thermal indices, the TC75 and T90 values obtained from the phase-amplitude combined optimization for both breast types outperform the results found in the literature. The T50 values obtained using the combined optimization are above 42C ∘ ${\rm C}^\circ$ .

CONCLUSIONS

This study presents an optimization method for focusing ME within breast tissue, performed in two steps: first phase optimization, followed by amplitude optimization. The electric field calculations are performed using both the MoM and Finite Difference Time Domain methods. The technique is numerically tested on two realistic breast models, with thermal indices calculated for each phantom and optimization process. Results show T90 values exceeding 40∘ C $^\circ{\rm C}$ and T50 values above 42∘ C $^\circ{\rm C}$ . While the study employs a 2D applicator, it provides a strong foundation for future development in 3D applications.

Anti-Tumor Strategies of Photothermal Therapy Combined with Other Therapies Using Nanoplatforms

Conventional cancer treatments often have complications and serious side effects, with limited improvements in 5-year survival and quality of life. Photothermal therapy (PTT) employs materials that convert light to heat when exposed to near-infrared light to raise the temperature of the tumor site to directly ablate tumor cells, induce immunogenic cell death, and improve the tumor microenvironment. This therapy has several benefits, including minimal invasiveness, high efficacy, reduced side effects, and robust targeting capabilities. Beyond just photothermal conversion materials, nanoplatforms significantly contribute to PTT by supplying effective photothermal conversion materials and bolstering tumor targeting to amplify anti-tumor effects. However, the anti-tumor effects of PTT alone are ultimately limited and often need to be combined with other therapies. This narrative review describes the recent progress of PTT combined with chemotherapy, radiotherapy, photodynamic therapy, immunotherapy, gene therapy, gas therapy, chemodynamic therapy, photoacoustic imaging, starvation therapy, and multimodal therapy. Studies have shown that combining PTT with other treatments can improve efficacy, reduce side effects, and overcome drug resistance. Despite the encouraging results, challenges such as optimizing treatment protocols, addressing tumor heterogeneity, and overcoming biological barriers remain. This paper highlights the potential for personalized, multimodal approaches to improve cancer treatment outcomes.

Robust stochastic optimisation strategies for locoregional hyperthermia treatment planning using polynomial chaos expansion

Objective.Conventional temperature optimization in hyperthermia treatment planning aims to maximize tumour temperature (e.g.T90; the temperature reached in at least 90% of the tumour) while enforcing hard constraints on normal tissue temperature (max(Ttissue) ⩽45 °C). This method generally incorrectly assumes that tissue/perfusion properties are known, typically relying on average values from the literature. To enhance the reliability of temperature optimization in clinical applications, we developed new robust optimization strategies to reduce the impact of tissue/perfusion property uncertainties.Approach.Within the software package Plan2Heat, temperature calculations during optimization apply efficient superposition of precomputed distributions, represented by a temperature matrix (T-matrix). We extended this method using stochastic polynomial chaos expansion models to compute an averageT-matrix (Tavg) and a covariance matrixCto account for uncertainties in tissue/perfusion properties. Three new strategies were implemented usingTavgandCduring optimization: (1)Tavg90 maximization, hard constraint on max(Ttissue), (2)Tavg90 maximization, hard constraint on max(Ttissue) variation, and (3) combinedTavg90 maximization and variation minimization, hard constraint on max(Ttissue). Conventional and new optimization strategies were tested in a cervical cancer patient. 100 test cases were generated, randomly sampling tissue-property probability distributions. TumourT90 and hot spots (max(Ttissue) >45 °C) were evaluated for each sample.Main Results.Conventional optimization had 28 samples without hot spots, with a medianT90 of 39.7 °C. For strategies (1), (2) and (3), the number of samples without hot spots was increased to 33, 41 and 36, respectively. MedianT90 was reduced lightly, by ∼0.1 °C-0.3 °C, for strategies (1-3). Tissue volumes exceeding 45 °C and variation in max(Ttissue) were less for the novel strategies.Significance.Optimization strategies that account for tissue-property uncertainties demonstrated fewer, and reduced in volume, normal tissue hot spots, with only a marginal reduction in tumourT90. This implies a potential clinical utility in reducing the need for, or the impact of, device setting adjustments during hyperthermia treatment.

Thermoradiotherapy Optimization Strategies Accounting for Hyperthermia Delivery Uncertainties

PURPOSE

The combined effect of hyperthermia and radiation therapy can be quantified by an enhanced equivalent radiation dose (EQDRT). Uncertainties in hyperthermia treatment planning and adjustments during treatment can impact achieved EQDRT. We developed and compared strategies for EQDRT optimization of radiation therapy plans, focusing on robustness against common adjustments.

METHODS AND MATERIALS

Using Plan2Heat, we computed preplanning hyperthermia plans and treatment adjustment scenarios for 3 cervical cancer patients. We imported these scenarios into RayStation 12A for optimization with 4 different strategies: (1) conventional radiation therapy optimization prescribing 46 Gy to the planning target volume (PTV), (2) nominal EQDRT optimization using the preplanning scenario, targeting uniform 58 Gy in the gross tumor volume (GTV), keeping organs at risk doses as in plan 1, (3) robust EQDRT optimization, as plan 2 but adding adjusted scenarios for optimization, and (4) library of plans (4 plans) with strategy 2 criteria but optimizing on 1 adjusted scenario per plan. We calculated for each radiation therapy plan EQDRT distributions for preplanning and adjusted scenarios, evaluating each combination of GTV coverage and homogeneity objectives.

RESULTS

EQDRT95% increased from 49.9 to 50.9 Gy in strategy 1 to 56.1 to 57.4 Gy in strategy 2 with the preplanning scenario, improving homogeneity by ∼10%. Strategy 2 demonstrated the best overall robustness, with 62% of all GTV objectives within tolerance. Strategy 3 had a higher percentage of coverage objectives within tolerance than strategy 2 (68% vs 54%) but a lower percentage for uniformity (44% vs 71%). Strategy 4 showed a similar EQDRT95% and homogeneity for adjusted scenarios than strategy 2 for a preplanning scenario. D0.1% (radiation dose received by the 0.1% most irradiated volume) for organs at risk was increased by strategies 2 to 4 by up to ∼6 Gy.

CONCLUSIONS

EQDRT optimization enhances EQDRT levels and uniformity compared with conventional optimization. Better overall robustness is achieved by optimizing the preplanning hyperthermia plan. Robust optimization improves coverage but reduces homogeneity. A library of plans ensures coverage and uniformity when dealing with adjusted hyperthermia scenarios.

Accurate Temperature Reconstruction in Radiofrequency Ablation for Atherosclerotic Plaques Based on Inverse Heat Transfer Analysis

Radio frequency ablation has emerged as a widely accepted treatment for atherosclerotic plaques. However, monitoring the temperature field distribution in the blood vessel wall during this procedure presents challenges. This limitation increases the risk of endothelial cell damage and inflammatory responses, potentially leading to lumen restenosis. The aim of this study is to accurately reconstruct the transient temperature distribution by solving a stochastic heat transfer model with uncertain parameters using an inverse heat transfer algorithm and temperature measurement data. The nonlinear least squares optimization method, Levenberg-Marquardt (LM), was employed to solve the inverse heat transfer problem for parameter estimation. Then, to improve the convergence of the algorithm and reduce the computational resources, a method of parameter sensitivity analysis was proposed to select parameters mainly affecting the temperature field. Furthermore, the robustness and accuracy of the algorithm were verified by introducing random noise to the temperature measurements. Despite the high level of temperature measurement noise (ξ = 5%) and larger initial guess deviation, the parameter estimation results remained closely aligned with the actual values, with an overall ERMS consistently below 0.05. The absolute errors between the reconstruction temperature at the measurement points TC1, TC2, and TC3, and the actual temperature, remained within 0.33 °C, 2.4 °C, and 1.17 °C, respectively. The Levenberg-Marquardt algorithm employed in this study proficiently tackled the ill-posed issue of inversion process and obtained a strong consistency between the reconstructed temperature the actual temperature.

Magnetic Particle Imaging-Guided Thermal Simulations for Magnetic Particle Hyperthermia

Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after delivery. The objective of this study was to develop a clinically translatable protocol that incorporates MPI data into finite element calculations for simulating tissue temperatures during MPH. To verify the protocol, we conducted MPH experiments in tumor-bearing mouse cadavers. Five 8-10-week-old female BALB/c mice bearing subcutaneous 4T1 tumors were anesthetized and received intratumor injections of Synomag®-S90 nanoparticles. Immediately following injection, the mice were euthanized and imaged, and the tumors were heated with an AMF. We used the Mimics Innovation Suite to create a 3D mesh of the tumor from micro-computerized tomography data and spatial index MPI to generate a scaled heating function for the heat transfer calculations. The processed imaging data were incorporated into a finite element solver, COMSOL Multiphysics®. The upper and lower bounds of the simulated tumor temperatures for all five cadavers demonstrated agreement with the experimental temperature measurements, thus verifying the protocol. These results demonstrate the utility of MPI to guide predictive thermal calculations for MPH treatment planning.

Robust, planning-based targeted locoregional tumour heating in small animals

Objective.To improve hyperthermia in clinical practice, pre-clinical hyperthermia research is essential to investigate hyperthermia effects and assess novel treatment strategies. Translating pre-clinical hyperthermia findings into clinically viable protocols requires laboratory animal treatment techniques similar to clinical hyperthermia techniques. The ALBA micro8 electromagnetic heating system (Med-logix SRL, Rome, Italy) has recently been developed to provide the targeted locoregional tumour heating currently lacking for pre-clinical research. This study evaluates the heat focusing properties of this device and its ability to induce robust locoregional tumour heating under realistic physiological conditions using simulations.Approach.Simulations were performed using the Plan2Heat treatment planning package (Amsterdam UMC, the Netherlands). First, the specific absorption rate (SAR) focus was characterised using a homogeneous phantom. Hereafter, a digital mouse model was used for the characterisation of heating robustness in a mouse. Device settings were optimised for treatment of a pancreas tumour and tested for varying circumstances. The impact of uncertainties in tissue property and perfusion values was evaluated using polynomial chaos expansion. Treatment quality and robustness were evaluated based on SAR and temperature distributions.Main results.The SAR distributions within the phantom are well-focused and can be adjusted to target any specific location. The focus size (full-width half-maximum) is a spheroid with diameters 9 mm (radially) and 20 mm (axially). The mouse model simulations show strong robustness against respiratory motion and intestine and stomach filling (∆T90≤0.14°C).Mouse positioning errors in the cranial-caudal direction lead to∆T90≤0.23°C. Uncertainties in tissue property and perfusion values were found to impact the treatment plan up to 0.56 °C (SD), with a variation onT90of 0.32 °C (1 SD).Significance.Our work shows that the pre-clinical phased-array system can provide adequate and robust locoregional heating of deep-seated target regions in mice. Using our software, robust treatment plans can be generated for pre-clinical hyperthermia research.

Quantification of tissue property and perfusion uncertainties in hyperthermia treatment planning: Multianalysis using polynomial chaos expansion

INTRODUCTION

Hyperthermia treatment planning (HTP) tools can guide treatment delivery, particularly with locoregional radiative phased array systems. Uncertainties in tissue and perfusion property values presently lead to quantitative inaccuracy of HTP, leading to sub-optimal treatment. Assessment of these uncertainties would allow for better judgement of the reliability of treatment plans and improve their value for treatment guidance. However, systematically investigating the impact of all uncertainties on treatment plans is a complex, high-dimensional problem and too computationally expensive for traditional Monte Carlo approaches. This study aims to systematically quantify the treatment-plan impact of tissue property uncertainties by investigating their individual contribution to, and combined impact on predicted temperature distributions.

METHODS

A novel Polynomial Chaos Expansion (PCE)-based HTP uncertainty quantification was developed and applied for locoregional hyperthermia of modelled tumours in the pancreatic head, prostate, rectum, and cervix. Patient models were based on the Duke and Ella digital human models. Using Plan2Heat, treatment plans were created to optimise tumour temperature (represented by T90) for treatment using the Alba4D system. For all 25-34 modelled tissues, the impact of tissue property uncertainties was analysed individually i.e., electrical and thermal conductivity, permittivity, density, specific heat capacity and perfusion. Next, combined analyses were performed on the top 30 uncertainties with the largest impact.

RESULTS

Uncertainties in thermal conductivity and heat capacity were found to have negligible impact on the predicted temperature ( < 1 × 10-10 °C), density and permittivity uncertainties had a small impact (< 0.3 °C). Uncertainties in electrical conductivity and perfusion can lead to large variations in predicted temperature. However, variations in muscle properties result in the largest impact at locations that could limit treatment quality, with a standard deviation up to almost 6 °C (pancreas) and 3.5 °C (prostate) for perfusion and electrical conductivity, respectively. The combined influence of all significant uncertainties leads to large variations with a standard deviation up to 9.0, 3.6, 3.7 and 4.1 °C for the pancreatic, prostate, rectal and cervical cases, respectively.

CONCLUSION

Uncertainties in tissue and perfusion property values can have a large impact on predicted temperatures from hyperthermia treatment planning. PCE-based analysis helps to identify all major uncertainties, their impact and judge the reliability of treatment plans.

Assessment of the thermal tissue models for the head and neck hyperthermia treatment planning

PURPOSE

To compare different thermal tissue models for head and neck hyperthermia treatment planning, and to assess the results using predicted and measured applied power data from clinical treatments.

METHODS

Three commonly used temperature models from literature were analysed: "constant baseline", "constant thermal stress" and "temperature dependent". Power and phase data of 93 treatments of 20 head and neck patients treated with the HYPERcollar3D applicator were used. The impact on predicted median temperature T50 inside the target region was analysed with maximum allowed temperature of 44 °C in healthy tissue. The robustness of predicted T50 for the three models against the influence of blood perfusion, thermal conductivity and the assumed hotspot temperature level was analysed.

RESULTS

We found an average predicted T50 of 41.0 ± 1.3 °C (constant baseline model), 39.9 ± 1.1 °C (constant thermal stress model) and 41.7 ± 1.1 °C (temperature dependent model). The constant thermal stress model resulted in the best agreement between the predicted power (P = 132.7 ± 45.9 W) and the average power measured during the hyperthermia treatments (P = 129.1 ± 83.0 W).

CONCLUSION

The temperature dependent model predicts an unrealistically high T50. The power values for the constant thermal stress model, after scaling simulated maximum temperatures to 44 °C, matched best to the average measured powers. We consider this model to be the most appropriate for temperature predictions using the HYPERcollar3D applicator, however further studies are necessary for developing of robust temperature model for tissues during heat stress.

The Relevance of High Temperatures and Short Time Intervals Between Radiation Therapy and Hyperthermia: Insights in Terms of Predicted Equivalent Enhanced Radiation Dose

PURPOSE

The radiosensitization effect of hyperthermia can be considered and quantified as an enhanced equivalent radiation dose (EQD_RT), that is, the dose needed to achieve the same effect without hyperthermia. EQD_RT can be predicted using an extended linear quadratic model, with temperature-dependent parameters. Clinical data show that both the achieved temperature and time interval between radiation therapy and hyperthermia correlate with clinical outcome, but their effect on expected EQD_RT is unknown and was therefore evaluated in this study.

METHODS AND MATERIALS

Biological modeling was performed using our in-house developed software (X-Term), considering a 23- × 2-Gy external beam radiation scheme, as applied for patients with locally advanced cervical cancer. First, the EQD_RT was calculated for homogeneous temperature levels, evaluating time intervals between 0 and 4 hours. Next, realistic heterogeneous hyperthermia treatment plans were combined with radiation therapy plans and the EQD_RT was calculated for 10 patients. Furthermore, the effect of achieving 0.5°C to 1°C lower or higher temperatures was evaluated.

RESULTS

EQD_RT increases substantially with both increasing temperature and decreasing time interval. The effect of the time interval is most pronounced at higher temperatures (>41°C). At a typical hyperthermic temperature level of 41.5°C, an enhancement of ∼10 Gy can be realized with a 0-hour time interval, which is decreased to only ∼4 Gy enhancement with a 4-hour time interval. Most enhancement is already lost after 1 hour. Evaluation in patients predicted an average additional EQD_RT (D95%) of 2.2 and 6.3 Gy for 4- and 0-hour time intervals, respectively. The effect of 0.5°C to 1°C lower or higher temperatures is most pronounced at high temperature levels and short time intervals. The additional EQD_RT (D95%) ranged between 1.5 and 3.3 Gy and between 4.5 and 8.5 Gy for 4- and 0-hour time intervals, respectively.

CONCLUSIONS

Biological modeling provides relevant insight into the relationship between treatment parameters and expected EQD_RT. Both high temperatures and short time intervals are essential to maximize EQD_RT.

Application of photogrammetry reconstruction for hyperthermia quality control measurements

PURPOSE

Hyperthermia is a cancer treatment in which the target region is heated to temperatures of 40-44 °C usually applying external electromagnetic field sources. The behavior of the hyperthermia applicators (antennas) in clinical practice should be periodically checked with phantom experiments to verify the applicator's performance over time. The purpose of this study was to investigate the application of photogrammetry reconstructions of 3D applicator position in these quality control procedure measurements.

METHODS

Photogrammetry reconstruction was applied at superficial hyperthermia scenario using the Lucite cone applicator (LCA) and phased-array heating in the head and neck region using the HYPERcollar3D. Wire-frame models of the entire measurement setups were created from multiple-view images and used for recreation of the setup inside 3D electromagnetic field simulation software. We evaluated applicator relation (R_a) between measured and simulated absolute specific absorption rate (SAR) for manually created and photogrammetry reconstructed simulation setups.

RESULTS

We found a displacement of 7.9 mm for the LCA and 8.2 mm for the HYPERcollar3D setups when comparing manually created and photogrammetry reconstructed applicator models placements. R_a improved from 1.24 to 1.18 for the LCA and from 1.17 to 1.07 for the HYPERcollar3D when using photogrammetry reconstructed simulation setups.

CONCLUSION

Photogrammetry reconstruction technique holds promise to improve measurement setup reconstruction and agreement between measured and simulated absolute SAR.

Validation and practical use of Plan2Heat hyperthermia treatment planning for capacitive heating

BACKGROUND

Capacitive devices are used for hyperthermia delivery, initially mainly in Asia, but nowadays also increasingly in Europe. Treatment planning can be very useful to determine the most effective patient-specific treatment set-up. This paper provides a validation of GPU-based simulations using Plan2Heat for capacitive hyperthermia devices.

METHODS

Validation was first performed by comparing simulations with an analytical solution for a spherical object placed inside a uniform electric field. Resolution was 5, 2.5 or 1 mm. Next, simulations for homogeneous and inhomogeneous phantom setups were performed for Thermotron RF8 and Celsius TCS capacitive heating devices at 2.5 mm resolution. Also different combinations of electrode sizes were evaluated. Normalized SAR profiles were compared to phantom measurements from the literature. Possible clinical use of treatment planning was demonstrated for an anal cancer patient, evaluating different treatment set-ups in prone and supine position.

RESULTS

Numerical and analytical solutions showed excellent agreement. At the center of the sphere, the error was 5.1%, 2.9% and 0.2% for a resolution of 5, 2.5 and 1 mm, respectively. Comparison of measurements and simulations for both Thermotron RF8 and Celsius TCS showed very good agreement within 5% for all phantom set-ups. Simulations were capable of accurately predicting the penetration depth; a very relevant parameter for clinical application. The patient case illustrated that planning can provide insight by comparing effectiveness of different treatment strategies.

CONCLUSION

Plan2Heat can rapidly and accurately predict heating patterns generated by capacitive devices. Thus, Plan2Heat is suitable for patient-specific treatment planning for capacitive hyperthermia.

Adapt2Heat: treatment planning-assisted locoregional hyperthermia by on-line visualization, optimization and re-optimization of SAR and temperature distributions

BACKGROUND

Hyperthermia treatment planning is increasingly used in clinical applications and recommended in quality assurance guidelines. Assistance in phase-amplitude steering during treatment requires dedicated software for on-line visualization of SAR/temperature distributions and fast re-optimization in response to hot spots. As such software tools are not yet commercially available, we developed Adapt2Heat for on-line adaptive hyperthermia treatment planning and illustrate possible application by different relevant real patient examples.

METHODS

Adapt2Heat was developed as a separate module of the treatment planning software Plan2Heat. Adapt2Heat runs on a Linux operating system and was developed in C++, using the open source Qt, Qwt and VTK libraries. A graphical user interface allows interactive and flexible on-line use of hyperthermia treatment planning. Predicted SAR/temperature distributions and statistics for selected phase-amplitude settings can be visualized instantly and settings can be re-optimized manually or automatically in response to hot spots.

RESULTS

Pretreatment planning E-Field, SAR and temperature calculations are performed with Plan2Heat and imported in Adapt2Heat. Examples show that Adapt2Heat can be helpful in assisting with phase-amplitude steering, e.g., by suppressing indicated hot spots. The effects of phase-amplitude adjustments on the tumor and potential hot spot locations are comprehensively visualized, allowing intuitive and flexible assistance by treatment planning during locoregional hyperthermia treatments.

CONCLUSION

Adapt2Heat provides an intuitive and flexible treatment planning tool for on-line treatment planning-assisted hyperthermia. Extensive features for visualization and (re-)optimization during treatment allow practical use in many locoregional hyperthermia applications. This type of tools are indispensable for enhancing the quality of hyperthermia treatment delivery.

Fast Adaptive Temperature-Based Re-Optimization Strategies for On-Line Hot Spot Suppression during Locoregional Hyperthermia

Simple Summary

When treatment limiting hot spots occur during locoregional hyperthermia (i.e., heating tumors to 40–44 °C for ~1 h), system settings are adjusted based on experience. In this study, we developed and evaluated treatment planning with temperature-based re-optimization and compared the predicted effectiveness to clinically applied protocol/experience-based steering. Re-optimization times were typically ~10 s; sufficiently fast for on-line use. Effective hot spot suppression was predicted, while maintaining adequate tumor heating. Inducing new hot spots was avoided. Temperature-based re-optimization to suppress treatment limiting hot spots seemed feasible to match the effectiveness of long-term clinical experience and will be further evaluated in a clinical setting. When numerical algorithms are proven to match long-term experience, the overall treatment quality within hyperthermia centers can significantly improve. Implementing these strategies would then imply that treatments become less dependent on the experience of the center/operator.

Abstract

Background: Experience-based adjustments in phase-amplitude settings are applied to suppress treatment limiting hot spots that occur during locoregional hyperthermia for pelvic tumors. Treatment planning could help to further optimize treatments. The aim of this research was to develop temperature-based re-optimization strategies and compare the predicted effectiveness with clinically applied protocol/experience-based steering. Methods: This study evaluated 22 hot spot suppressions in 16 cervical cancer patients (mean age 67 ± 13 year). As a first step, all potential hot spot locations were represented by a spherical region, with a user-specified diameter. For fast and robust calculations, the hot spot temperature was represented by a user-specified percentage of the voxels with the largest heating potential (HPP). Re-optimization maximized tumor T90, with constraints to suppress the hot spot and avoid any significant increase in other regions. Potential hot spot region diameter and HPP were varied and objective functions with and without penalty terms to prevent and minimize temperature increase at other potential hot spot locations were evaluated. Predicted effectiveness was compared with clinically applied steering results. Results: All strategies showed effective hot spot suppression, without affecting tumor temperatures, similar to clinical steering. To avoid the risk of inducing new hot spots, HPP should not exceed 10%. Adding a penalty term to the objective function to minimize the temperature increase at other potential hot spot locations was most effective. Re-optimization times were typically ~10 s. Conclusion: Fast on-line re-optimization to suppress treatment limiting hot spots seems feasible to match effectiveness of ~30 years clinical experience and will be further evaluated in a clinical setting.

Treatment planning facilitates clinical decision making for hyperthermia treatments

Background: Treatment quality is important in clinical hyperthermia. Guideline-based treatment protocols are used to determine system settings and treatment strategies to ensure effective tumor heating and prevent unwanted treatment-limiting normal tissue hot spots. Realizing both these goals can prove challenging using generic guideline-based and operator-dependent treatment strategies. Hyperthermia treatment planning (HTP) can be very useful to support treatment strategies. Although HTP is increasingly integrated into the standard clinical workflow, active clinical application is still limited to a small number of hyperthermia centers and should be further stimulated.Purpose: This paper aims to serve as a practical guide, demonstrating how HTP can be applied in clinical decision making for both superficial and locoregional hyperthermia treatments.HTP in clinical decision making: Seven problems that occur in daily clinical practice are described and we show how HTP can enhance insight to formulate an adequate treatment strategy. Examples use representative commercially available hyperthermia devices and cover all stages during the clinical workflow. Problems include selecting adequate phase settings, heating ability analysis, hot spot suppression, applicator selection, evaluation of target coverage and heating depth, and predicting possible thermal toxicity in case of an implant. Since we aim to promote a general use of HTP in daily practice, basic simulation strategies are used in these problems, avoiding a need for the application of dedicated advanced optimization routines that are not generally available.Conclusion: Even fairly basic HTP can facilitate clinical decision making, providing a meaningful and clinically relevant contribution to maintaining and improving treatment quality.