Benefit of replacing the Sigma-60 by the Sigma-Eye applicator: A Monte Carlo-based uncertainty analysis

Adapting Temperature Predictions to MR Imaging in Treatment Position to Improve Simulation-Guided Hyperthermia for Cervical Cancer

Hyperthermia treatment consists of elevating the temperature of the tumor to increase the effectiveness of radiotherapy and chemotherapy. Hyperthermia treatment planning (HTP) is an important tool to optimize treatment quality using pre-treatment temperature predictions. The accuracy of these predictions depends on modeling uncertainties such as tissue properties and positioning. In this study, we evaluated if HTP accuracy improves when the patient is imaged inside the applicator at the start of treatment. Because perfusion is a major uncertainty source, the importance of accurate treatment position and anatomy was evaluated using different perfusion values. Volunteers were scanned using MR imaging without ("planning setup") and with the MR-compatible hyperthermia device ("treatment setup"). Temperature-based quality indicators were used to assess the differences between the standard, apparent and the optimized hyperthermia dose. We conclude that pre-treatment imaging can improve HTP predictions accuracy but also, that tissue perfusion modelling is crucial if temperature-based optimization is applied.

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.

Using MRI to measure position and anatomy changes and assess their impact on the accuracy of hyperthermia treatment planning for cervical cancer

PURPOSE

We studied the differences between planning and treatment position, their impact on the accuracy of hyperthermia treatment planning (HTP) predictions, and the relevance of including true treatment anatomy and position in HTP based on magnetic resonance (MR) images.

MATERIALS AND METHODS

All volunteers were scanned with an MR-compatible hyperthermia device, including a filled waterbolus, to replicate the treatment setup. In the planning setup, the volunteers were scanned without the device to reproduce the imaging in the current HTP. First, we used rigid registration to investigate the patient position displacements between the planning and treatment setup. Second, we performed HTP for the planning anatomy at both positions and the treatment mimicking anatomy to study the effects of positioning and anatomy on the quality of the simulated hyperthermia treatment. Treatment quality was evaluated using SAR-based parameters.

RESULTS

We found an average displacement of 2 cm between planning and treatment positions. These displacements caused average absolute differences of ∼12% for TC25 and 10.4%-15.9% in THQ. Furthermore, we found that including the accurate treatment position and anatomy in treatment planning led to an improvement of 2% in TC25 and 4.6%-10.6% in THQ.

CONCLUSIONS

This study showed that precise patient position and anatomy are relevant since these affect the accuracy of HTP predictions. The major part of improved accuracy is related to implementing the correct position of the patient in the applicator. Hence, our study shows a clear incentive to accurately match the patient position in HTP with the actual treatment.

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.

Rapid SAR optimization for hyperthermic oncology: combining multi-goal optimization and time-multiplexed steering for hotspot suppression

PURPOSE

Healthy tissue hotspots are a main limiting factor in administering deep hyperthermia cancer therapy. We propose an optimization scheme that uses time-multiplexed steering (TMPS) among minimally correlated (nearly) Pareto-optimal solutions to suppress hotspots without reducing tumor heating. Furthermore, tumor heating homogeneity is maximized, thus reducing toxicity and avoiding underexposed tumor regions, which in turn may reduce recurrence.

MATERIALS AND METHODS

The novel optimization scheme combines random generation of steering parameters with local optimization to efficiently identify the set of (Pareto-) optimal solutions of conflicting optimization goals. To achieve simultaneous suppression of hotspots, multiple steering parameter configurations with minimally correlated hotspots are selected near the Pareto front and combined in TMPS. The performance of the novel scheme was compared with that of a multi-goal Genetic Algorithm for a range of simulated treatment configurations involving a modular applicator heating a generic tumor situated in the bladder, cervix, or pelvic bone. SAR cumulative histograms in tumor and healthy tissue, as well as hotspot volumes are used as metrics.

RESULTS

Compared to the non-TMPS optimization, the proposed scheme was able to reduce the peak temperature in healthy tissue by 0.2 °C-1.0 °C (a thermal dose reduction by at least 26%) and, importantly, the hotspot volume above 42 °C in healthy tissue by 41%-86%. At the same time, tumor heating homogeneity was maintained or improved.

CONCLUSIONS

The extremely rapid optimization (5 s for TMPS part, on a standard PC) permits closed-loop treatment reoptimization during treatment administration, and empowers physicians with a selection of optimal treatment scenarios reflecting different weighting of conflicting treatment goals.

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.

ESHO benchmarks for computational modeling and optimization in hyperthermia therapy

BACKGROUND

The success of cancer hyperthermia (HT) treatments is strongly dependent on the temperatures achieved in the tumor and healthy tissues as it correlates with treatment efficacy and safety, respectively. Hyperthermia treatment planning (HTP) simulations have become pivotal for treatment optimization due to the possibility for pretreatment planning, optimization and decision making, as well as real-time treatment guidance.

MATERIALS AND METHODS

The same computational methods deployed in HTP are also used for in silico studies. These are of great relevance for the development of new HT devices and treatment approaches. To aid this work, 3 D patient models have been recently developed and made available for the HT community. Unfortunately, there is no consensus regarding tissue properties, simulation settings, and benchmark applicators, which significantly influence the clinical relevance of computational outcomes.

RESULTS AND DISCUSSION

Herein, we propose a comprehensive set of applicator benchmarks, efficacy and safety optimization algorithms, simulation settings and clinical parameters, to establish benchmarks for method comparison and code verification, to provide guidance, and in view of the 2021 ESHO Grand Challenge (Details on the ESHO grand challenge on HTP will be provided at https://www.esho.info/).

CONCLUSION

We aim to establish guidelines to promote standardization within the hyperthermia community such that novel approaches can quickly prove their benefit as quickly as possible in clinically relevant simulation scenarios. This paper is primarily focused on radiofrequency and microwave hyperthermia but, since 3 D simulation studies on heating with ultrasound are now a reality, guidance as well as a benchmark for ultrasound-based hyperthermia are also included.

MR Thermometry Accuracy and Prospective Imaging-Based Patient Selection in MR-Guided Hyperthermia Treatment for Locally Advanced Cervical Cancer

Simple Summary

Monitoring and controlling the temperature distribution combined with precise energy delivery are key components for hyperthermia treatment success. Magnetic resonance (MR) imaging is used clinically to monitor the temperature of the treated volume non-invasively. However, there are no comprehensive systematic studies on MR thermometry accuracy during deep pelvic hyperthermia, and the few investigational studies suffer from a high probability of bias due to lacking objective criteria for data inclusion. This study presents the first systematic analysis and defines an imaging-based criterion for prospective patient selection to standardize clinical MR thermometry accuracy assessments.

Abstract

The efficacy of a hyperthermia treatment depends on the delivery of well-controlled heating; hence, accurate temperature monitoring is essential for ensuring effective treatment. For deep pelvic hyperthermia, there are no comprehensive and systematic reports on MR thermometry. Moreover, data inclusion generally lacks objective selection criteria leading to a high probability of bias when comparing results. Herein, we studied whether imaging-based data inclusion predicts accuracy and could serve as a tool for prospective patient selection. The accuracy of the MR thermometry in patients with locally advanced cervical cancer was benchmarked against intraluminal temperature. We found that gastrointestinal air motion at the start of the treatment, quantified by the Jaccard similarity coefficient, was a good predictor for MR thermometry accuracy. The results for the group that was selected for low gastrointestinal air motion improved compared to the results for all patients by 50% (accuracy), 26% (precision), and 80% (bias). We found an average MR thermometry accuracy of 2.0 °C when all patients were considered and 1.0 °C for the selected group. These results serve as the basis for comprehensive benchmarking of novel technologies. The Jaccard similarity coefficient also has good potential to prospectively determine in which patients the MR thermometry will be valuable.

Advanced patient-specific hyperthermia treatment planning

Hyperthermia treatment planning (HTP) is valuable to optimize tumor heating during thermal therapy delivery. Yet, clinical hyperthermia treatment plans lack quantitative accuracy due to uncertainties in tissue properties and modeling, and report tumor absorbed power and temperature distributions which cannot be linked directly to treatment outcome. Over the last decade, considerable progress has been made to address these inaccuracies and therefore improve the reliability of hyperthermia treatment planning. Patient-specific electrical tissue conductivity derived from MR measurements has been introduced to accurately model the power deposition in the patient. Thermodynamic fluid modeling has been developed to account for the convective heat transport in fluids such as urine in the bladder. Moreover, discrete vasculature trees have been included in thermal models to account for the impact of thermally significant large blood vessels. Computationally efficient optimization strategies based on SAR and temperature distributions have been established to calculate the phase-amplitude settings that provide the best tumor thermal dose while avoiding hot spots in normal tissue. Finally, biological modeling has been developed to quantify the hyperthermic radiosensitization effect in terms of equivalent radiation dose of the combined radiotherapy and hyperthermia treatment. In this paper, we review the present status of these developments and illustrate the most relevant advanced elements within a single treatment planning example of a cervical cancer patient. The resulting advanced HTP workflow paves the way for a clinically feasible and more reliable patient-specific hyperthermia treatment planning.

Patient-Specific Planning for Thermal Magnetic Resonance of Glioblastoma Multiforme

Simple Summary

Hyperthermia was proven to enhance the efficacy of chemo- and radiation therapy treatment of glioblastoma multiforme, an aggressive brain tumor of poor prognosis. Despite good clinical results in other tumor types and locations, hyperthermia induced by electromagnetic waves in the radiofrequency range is not available so far for the treatment of brain tumors due to the highly sensitive surrounding tissue and lack of non-invasive therapy monitoring. ThermalMR integrates non-invasive diagnosis, therapy, and therapy monitoring in a single RF applicator device by employing radiowaves for magnetic resonance imaging, radiofrequency heating, as well as magnetic resonance thermometry. This work examines three optimization algorithms for hyperthermia treatment planning and up to ten RF applicator configurations for a cohort of nine patient models with glioblastoma multiforme. Clinical diversity is represented in target size and location and the inclusion of post-operative models. Our findings indicate the need and potential for patient-specific treatment planning and RF applicator design when targeting brain tumors.

Abstract

Thermal intervention is a potent sensitizer of cells to chemo- and radiotherapy in cancer treatment. Glioblastoma multiforme (GBM) is a potential clinical target, given the cancer’s aggressive nature and resistance to current treatment options. This drives research into optimization algorithms for treatment planning as well as radiofrequency (RF) applicator design for treatment delivery. In this work, nine clinically realistic GBM target volumes (TVs) for thermal intervention are compared using three optimization algorithms and up to ten RF applicator designs for thermal magnetic resonance. Hyperthermia treatment planning (HTP) was successfully performed for all cases, including very small, large, and even split target volumes. Minimum requirements formulated for the metrics assessing HTP outcome were met and exceeded for all patient specific cases. Results indicate a 16 channel two row arrangement to be most promising. HTP of TVs with a small extent in the cranial–caudal direction in conjunction with a large radial extent remains challenging despite the advanced optimization algorithms used. In general, deep seated targets are favorable. Overall, our findings indicate that a one-size-fits-all RF applicator might not be the ultimate approach in hyperthermia of brain tumors. It stands to reason that modular and reconfigurable RF applicator configurations might best suit the needs of targeting individual GBM geometry.

Iterative time-reversal for multi-frequency hyperthermia

Time-reversal (TR) is a known wideband array beam-forming technique that has been suggested as a treatment planning alternative in deep microwave hyperthermia for cancer treatment. While the aim in classic TR is to focus the energy at a specific point within the target, no assumptions are made on secondary lobes that might arise in the healthy tissues. These secondary lobes, together with tissue heterogeneity, may result in hot-spots (HSs), which are known to limit the efficiency of the thermal dose delivery to the tumor. This paper proposes a novel wideband TR focusing method that iteratively shifts the focus away from HSs and towards cold-spots from an initial TR solution, a procedure that improves tumor coverage and reduces HSs. We verify this method on two different applicator topologies and several target volume configurations. The algorithm is deterministic and runs within seconds, enabling its use for real-time applications. At the same time, it yields results comparable to those obtained with global stochastic optimizers such as Particle Swarm.

Locoregional peritoneal hyperthermia to enhance the effectiveness of chemotherapy in patients with peritoneal carcinomatosis: a simulation study comparing different locoregional heating systems

Introduction: Intravenous chemotherapy plus abdominal locoregional hyperthermia is explored as a noninvasive alternative to hyperthermic intraperitoneal chemotherapy (HIPEC) in treatment of peritoneal carcinomatosis (PC). First clinical results demonstrate feasibility, but survival data show mixed results and for pancreatic and gastric origin results are not better than expected for chemotherapy alone. In this study, computer simulations are performed to compare the effectiveness of peritoneal heating for five different locoregional heating systems.Methods: Simulations of peritoneal heating were performed for a phantom and two pancreatic cancer patients, using the Thermotron RF8, the AMC-4/ALBA-4D system, the BSD Sigma-60 and Sigma-Eye system, and the AMC-8 system. Specific absorption rate (SAR) distributions were optimized and evaluated. Next, to provide an indication of possible enhancement factors, the corresponding temperature distributions and thermal enhancement ratio (TER) of oxaliplatin were estimated.Results: Both phantom and patient simulations showed a relatively poor SAR coverage for the Thermotron RF8, a fairly good coverage for the AMC-4/ALBA-4D, Sigma-60, and Sigma-Eye systems, and the best and most homogeneous coverage for the AMC-8 system. In at least 50% of the peritoneum, 35-45 W/kg was predicted. Thermal simulations confirmed these favorable peritoneal heating properties of the AMC-8 system and TER values of ∼1.4-1.5 were predicted in at least 50% of the peritoneum.Conclusion: Locoregional peritoneal heating with the AMC-8 system yields more favorable heating patterns compared to other clinically used locoregional heating devices. Therefore, results of this study may promote the use of the AMC-8 system for locoregional hyperthermia in future multidisciplinary studies for treatment of PC.

Integrating Loco-Regional Hyperthermia Into the Current Oncology Practice: SWOT and TOWS Analyses

Moderate hyperthermia at temperatures between 40 and 44°C is a multifaceted therapeutic modality. It is a potent radiosensitizer, interacts favorably with a host of chemotherapeutic agents, and, in combination with radiotherapy, enforces immunomodulation akin to “in situ tumor vaccination.” By sensitizing hypoxic tumor cells and inhibiting repair of radiotherapy-induced DNA damage, the properties of hyperthermia delivered together with photons might provide a tumor-selective therapeutic advantage analogous to high linear energy transfer (LET) neutrons, but with less normal tissue toxicity. Furthermore, the high LET attributes of hyperthermia thermoradiobiologically are likely to enhance low LET protons; thus, proton thermoradiotherapy would mimic ¹²C ion therapy. Hyperthermia with radiotherapy and/or chemotherapy substantially improves therapeutic outcomes without enhancing normal tissue morbidities, yielding level I evidence reported in several randomized clinical trials, systematic reviews, and meta-analyses for various tumor sites. Technological advancements in hyperthermia delivery, advancements in hyperthermia treatment planning, online invasive and non-invasive MR-guided thermometry, and adherence to quality assurance guidelines have ensured safe and effective delivery of hyperthermia to the target region. Novel biological modeling permits integration of hyperthermia and radiotherapy treatment plans. Further, hyperthermia along with immune checkpoint inhibitors and DNA damage repair inhibitors could further augment the therapeutic efficacy resulting in synthetic lethality. Additionally, hyperthermia induced by magnetic nanoparticles coupled to selective payloads, namely, tumor-specific radiotheranostics (for both tumor imaging and radionuclide therapy), chemotherapeutic drugs, immunotherapeutic agents, and gene silencing, could provide a comprehensive tumor-specific theranostic modality akin to “magic (nano)bullets.” To get a realistic overview of the strength (S), weakness (W), opportunities (O), and threats (T) of hyperthermia, a SWOT analysis has been undertaken. Additionally, a TOWS analysis categorizes future strategies to facilitate further integration of hyperthermia with the current treatment modalities. These could gainfully accomplish a safe, versatile, and cost-effective enhancement of the existing therapeutic armamentarium to improve outcomes in clinical oncology.

Predictive value of SAR based quality indicators for head and neck hyperthermia treatment quality

PURPOSE

Hyperthermia treatment quality determines treatment effectiveness as shown by the clinically derived thermal-dose effect relations. SAR based optimization factors are used as possible surrogate for temperature, since they are not affected by thermal tissue properties uncertainty and variations. Previously, target coverage (TC) at the 25% and 50% iso-SAR level was shown predictive for treatment outcome in superficial hyperthermia and the target-to-hot-spot-quotient (THQ) was shown to highly correlate with predictive temperature in deep pelvic hyperthermia. Here, we investigate the correlation with temperature for THQ and TC using an 'intermediate' scenario: semi-deep hyperthermia in the head & neck region using the HYPERcollar3D.

METHODS

Fifteen patient-specific models and two different planning approaches were used, including random perturbations to circumvent optimization bias. The predicted SAR indicators were compared to predicted target temperature distribution indicators T50 and T90, i.e., the median and 90th percentile temperature respectively.

RESULTS

The intra-patient analysis identified THQ, TC25 and TC50 as good temperature surrogates: with a mean correlation coefficient R²_T50 = 0.72 and R²_T90=0.66. The inter-patient analysis identified the highest correlation with TC25 (R²_T50 = 0.76, R²_T90=0.54) and TC50 (R²_T50 = 0.74, R²_T90 = 0.56).

CONCLUSION

Our investigation confirmed the validity of our current strategy for deep hyperthermia in the head & neck based on a combination of THQ and TC25. TC50 was identified as the best surrogate since it enables optimization and patient inclusion decision making using one single parameter.

Regional deep hyperthermia: quantitative evaluation of predicted and direct measured temperature distributions in patients with high-risk extremity soft-tissue sarcoma

BACKGROUND

Temperature distributions resulting from hyperthermia treatment of patients with high-risk soft-tissue sarcoma (STS) were quantitatively evaluated and globally compared with thermal simulations performed by a treatment planning system. The aim was to test whether the treatment planning system was able to predict correct temperature distributions.

METHODS

Five patients underwent computed tomography (CT) fluoroscopy-guided placement of tumor catheters used for the interstitial temperature measurements. For the simulations, five 3 D patient models were reconstructed by segmenting the patient CT datasets into different tissues. The measured and simulated data were evaluated by calculating the temperature change ( ΔT ), T90, T50, T20, T_mean, T_min and T_max, as well as the 90th percentile thermal dose (CEM43T90). In order to measure the agreement between both methods quantitatively, the Bland-Altman analysis was applied.

RESULTS

The absolute difference between measured and simulated temperatures were found to be 2°, 6°, 1°, 4°, 5° and 4 °C on average for T_min, T_max, T90, T50, T20 and T_mean, respectively. Furthermore, the thermal simulations exhibited relatively higher thermal dose compared to those that were measured. Finally, the results of the Bland-Altman analysis showed that the mean difference between both methods was above 2 °C which is considered to be clinically unacceptable.

CONCLUSION

Given the current practical limitations on resolution of calculation grid, tissue properties, and perfusion information, the software SigmaHyperPlan™ is incapable to produce thermal simulations with sufficient correlation to typically heterogeneous tissue temperatures to be useful for clinical treatment planning.

The potential of constrained SAR focusing for hyperthermia treatment planning: analysis for the head & neck region

Clinical trials have shown that hyperthermia is a potent adjuvant to conventional cancer treatments, but the temperatures currently achieved in the clinic are still suboptimal. Hyperthermia treatment planning simulations have potential to improve the heating profile of phased-array applicators. An important open challenge is the development of an effective optimization procedure that enables uniform heating of the target region while keeping temperature below a threshold in healthy tissues. In this work, we analyzed the effectiveness and efficiency of a recently proposed optimization approach, i.e. focusing via constrained power optimization (FOCO), using 3D simulations of twelve clinical patient specific models. FOCO performance was compared against a clinically used particle swarm based optimization approach. Evaluation metrics were target coverage at the 25% iso-SAR level, target hotspot quotient, median target temperature (T50) and computational requirements. Our results show that, on average, constrained power focusing performs slightly better than the clinical benchmark ([Formula: see text]T50 [Formula: see text] °C), but outperforms this clinical benchmark for large target volumes ([Formula: see text]40 cm[Formula: see text], [Formula: see text]T50 [Formula: see text] °C). In addition, the results are achieved in a shorter time ([Formula: see text]%) and are repeatable because the approach is formulated as a convex optimization problem.

Online Adaptive Hyperthermia Treatment Planning During Locoregional Heating to Suppress Treatment-Limiting Hot Spots

BACKGROUND

Adequate tumor temperatures during hyperthermia are essential for good clinical response, but excessive heating of normal tissue should be avoided. This makes locoregional heating using phased array systems technically challenging. Online application of hyperthermia treatment planning could help to improve the heating quality. The aim of this study was to evaluate the clinical benefit of online treatment planning during treatment of pelvic tumors heated with the AMC-8 locoregional hyperthermia system.

METHODS

For online adaptive hyperthermia treatment planning, a graphical user interface was developed. Electric fields were calculated in a preprocessing step using our in-house-developed finite-difference-based treatment planning system. This allows instant calculation of the temperature distribution for user-selected phase-amplitude settings during treatment and projection onto the patient's computed tomographic scan for online visualization. Online treatment planning was used for 14 treatment sessions in 8 patients to reduce the patients' reports of hot spots while maintaining the same level of tumor heating. The predicted decrease in hot spot temperature should be at least 0.5°C, and the tumor temperature should decrease less than 0.2°C. These predictions were compared with clinical data: patient feedback about the hot spot and temperature measurements in the tumor region.

RESULTS

In total, 17 hot spot reports occurred during the 14 sessions, and the alternative settings predicted the hot spot temperature to decrease by at least 0.5°C, which was confirmed by the disappearance of all 17 hot spot reports. At the same time, the average tumor temperature was predicted to change on average -0.01°C (range, -0.19°C to 0.34°C). The measured tumor temperature change was on average only -0.02°C (range, -0.26°C to 0.31°C). In only 2 cases the temperature decrease was slightly larger than 0.2°C, but at most it was 0.26°C.

CONCLUSIONS

Online application of hyperthermia treatment planning is reliable and very useful to reduce hot spots without affecting tumor temperatures.

Hyperthermia treatment planning for cervical cancer patients based on electrical conductivity tissue properties acquired in vivo with EPT at 3 T MRI

Introduction The reliability of hyperthermia treatment planning (HTP) is strongly dependent on the accuracy of the electric properties of each tissue. The values currently used are mostly based on ex vivo measurements. In this study, in vivo conductivity of human muscle, bladder content and cervical tumours, acquired with magnetic resonance-based electric properties tomography (MR-EPT), are exploited to investigate the effect on HTP for cervical cancer patients. Methods Temperature-based optimisation of five different patients was performed using literature-based conductivity values yielding certain antenna settings, which are then used to compute the temperature distribution of the patient models with EPT-based conductivity values. Furthermore, the effects of altered bladder and muscle conductivity were studied separately. Finally, the temperature-based optimisation was performed with patient models based on EPT conductivity values. Results The tumour temperatures for all EPT-based dielectric patient models were lower compared to the optimal tumour temperatures based on literature values. The largest deviation was observed for patient 1 with ΔT90 = -1.37 °C. A negative impact was also observed when the treatment was optimised based on the EPT values. For four patients ΔT90 was less than 0.6 °C; for one patient it was 1.5 °C. Conclusions Electric conductivity values acquired by EPT are higher than commonly used from literature. This difference has a substantial impact on cervical tumour temperatures achieved during hyperthermia. A higher conductivity in the bladder and in the muscle tissue surrounding the tumour leads to higher power dissipation in the bladder and muscle, and therefore to lower tumour temperatures.

The relevance of MRI for patient modeling in head and neck hyperthermia treatment planning: a comparison of CT and CT-MRI based tissue segmentation on simulated temperature

PURPOSE

In current clinical practice, head and neck (H&N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors' study is to investigate the relevance of using MRI in addition to CT for patient modeling in H&N HTP.

METHODS

CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H&N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRIdb). To quantify the relevance of MRI based segmentation for H&N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (Tmax) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRIdb.

RESULTS

In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (Tmax: 38.0 °C) and CT and MRI (Tmax: 38.1 °C) result in similar simulated temperatures, while CT and MRIdb (Tmax: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly.

CONCLUSIONS

Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.

Simulation techniques in hyperthermia treatment planning

Abstract Clinical trials have shown that hyperthermia (HT), i.e. an increase of tissue temperature to 39-44 °C, significantly enhance radiotherapy and chemotherapy effectiveness [1]. Driven by the developments in computational techniques and computing power, personalised hyperthermia treatment planning (HTP) has matured and has become a powerful tool for optimising treatment quality. Electromagnetic, ultrasound, and thermal simulations using realistic clinical set-ups are now being performed to achieve patient-specific treatment optimisation. In addition, extensive studies aimed to properly implement novel HT tools and techniques, and to assess the quality of HT, are becoming more common. In this paper, we review the simulation tools and techniques developed for clinical hyperthermia, and evaluate their current status on the path from 'model' to 'clinic'. In addition, we illustrate the major techniques employed for validation and optimisation. HTP has become an essential tool for improvement, control, and assessment of HT treatment quality. As such, it plays a pivotal role in the quest to establish HT as an efficacious addition to multi-modality treatment of cancer.