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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.

Purpose To develop a head and neck hyperthermia phased array system compatible with a 1.5 T magnetic resonance (MR) scanner for noninvasive thermometry. Methods We designed a dielectric-parabolic-reflector antenna (DiPRA) based on a printed reflector backed dipole antenna and studied its predicted and measured performance in a flat configuration (30 mm thick water bolus and muscle equivalent layer). Thereafter, we designed a phased array applicator model (‘MRcollar’) consisting of 12 DiPRA modules placed on a radius of 180 mm. Theoretical heating performance of the MRcollar model was benchmarked against the current clinical applicator (HYPERcollar3D) using specific (3D) head and neck models of 28 treated patients. Lastly, we assessed the influence of the DiPRA modules on MR scanning quality. Results The predicted and measured reflection coefficients (S11) of the DiPRA module are below −20 dB. The maximum specific absorption rate (SAR) in the area under the antenna was 47% higher than for the antenna without encasing. Compared to the HYPERcollar3D, the MRcollar design incorporates 31% less demineralized water (−2.5 L), improves the predicted TC25 (target volume enclosed by 25% iso-SAR contour) by 4.1% and TC50 by 8.5%, while the target-to-hotspot quotient (THQ) is minimally affected (−1.6%). MR experiments showed that the DiPRA modules do not affect MR transmit/receive performance. Conclusion Our results suggest that head and neck hyperthermia delivery quality with the MRcollar can be maintained, while facilitating simultaneous noninvasive MR thermometry for treatment monitoring and control.

Purpose Thermal dose-effect relations have demonstrated that clinical effectiveness of hyperthermia would benefit from more controlled heating of the tumor. Hyperthermia treatment planning (HTP) is a potent tool to study strategies enabling target conformal heating, but its accuracy is affected by patient modeling approximations. Homogeneous phantoms models are being used that do not match the body shape of patients in treatment position and often have unrealistic target volumes. As a consequence, simulation accuracy is affected, and performance comparisons are difficult. The aim of this study is to provide the first step toward standardization of HTP simulation studies in terms of patient modeling by introducing the Erasmus Virtual Patient Repository (EVPR): a virtual patient model database. Methods Four patients with a tumor in the head and neck or the pelvis region were selected, and corresponding models were created using a clinical segmentation procedure. Using the Erasmus University Medical Center standard procedure, HTP was applied to these models and compared to HTP for commonly used surrogate models. Results Although this study was aimed at presenting the EVPR database, our study illustrates that there is a non-negligible difference in the predicted SAR patterns between patient models and homogeneous phantom-based surrogate models. We further demonstrate the difference between actual and simplified target volumes being used today. Conclusion Our study describes the EVPR for the research community as a first step toward standardization of hyperthermia simulation studies.

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 R2T50 = 0.72 and R2T90=0.66. The inter-patient analysis identified the highest correlation with TC25 (R2T50 = 0.76, R2T90=0.54) and TC50 (R2T50 = 0.74, R2T90 = 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.