Application of the ESHO-QA guidelines for determining the performance of the LCA superficial hyperthermia heating system.

PURPOSE: This study aimed to assess the quality of the lucite cone applicator (LCA), the standard applicator for superficial hyperthermia at the Erasmus MC Cancer Institute, using the most recent quality assurance guidelines, thus verifying their feasibility. MATERIALS AND METHODS: The assessment was conducted on each of the six LCAs available for clinical treatments. The temperature distribution was evaluated using an infrared camera across different layers of a fat-muscle mimicking phantom. The maximum temperature increase, thermal effective penetration depth (TEPD), and thermal effective field size (TEFS) were used as quality metrics. The experimental results were validated through comparison with simulated results, using a canonical phantom model and a realistic phantom model segmented from CT imaging. RESULTS: A maximum temperature increase above 6 °C at 2 cm depth in the fat-muscle phantom for all the experiments was found. A mean negative difference between simulated and experimental data was of 1.3 °C when using the canonical phantom model. This value decreased to a mean negative difference of 0.4 °C when using the realistic model. Simulated and measured TEPD showed good agreement for both in silico scenarios, while discrepancies were present for TEFS. CONCLUSIONS: The LCAs passed all QA guidelines requirements for superficial hyperthermia delivery when used singularly or in an array configuration. A further characterization of parameters such as antenna efficiency and heat transfer coefficients would be beneficial for translating experimental results to simulated values. Implementing the QA guidelines was time-consuming and demanding, requiring careful preparation and correct setup of antenna elements.

Ethylcellulose-stabilized fat-tissue phantom for quality assurance in clinical hyperthermia.

BACKGROUND: Phantoms accurately mimicking the electromagnetic and thermal properties of human tissues are essential for the development, characterization, and quality assurance (QA) of clinically used equipment for Hyperthermia Treatment (HT). Currently, a viable recipe for a fat equivalent phantom is not available, mainly due to challenges in the fabrication process and fast deterioration. MATERIALS AND METHODS: We propose to employ a glycerol-in-oil emulsion stabilized with ethylcellulose to develop a fat-mimicking material. The dielectric, rheological, and thermal properties of the phantom have been assessed by state-of-the-art measurement techniques. The full-size phantom was then verified in compliance with QA guidelines for superficial HT, both numerically and experimentally, considering the properties variability. RESULTS: Dielectric and thermal properties were proven equivalent to fat tissue, with an acceptable variability, in the 8 MHz to 1 GHz range. The rheology measurements highlighted enhanced mechanical stability over a large temperature range. Both numerical and experimental evaluations proved the suitability of the phantom for QA procedures. The impact of the dielectric property variations on the temperature distribution has been numerically proven to be limited (around 5%), even if higher for capacitive devices (up to 20%). CONCLUSIONS: The proposed fat-mimicking phantom is a good candidate for hyperthermia technology assessment processes, adequately representing both dielectric and thermal properties of the human fat tissue while maintaining structural stability even at elevated temperatures. However, further experimental investigations on capacitive heating devices are necessary to better assess the impact of the low electrical conductivity values on the thermal distribution.

A Novel Method for Generation of In Silico Subjects with Type 2 Diabetes.

A type 2 diabetes (T2D) simulator has been recently proposed for supporting drug development and treatment optimization. This tool consists of a physiological model of glucose/insulin/C-peptide dynamics and a virtual cohort of T2D subjects (i.e., random extractions of model parameterizations from a joint parameter distribution) well describing both average and variability realistic T2D dynamics . However, the state-of-art procedure to get a reliable virtual population requires some post-processing after subject extraction, in order to discard implausible behaviors. We propose an improved method for virtual subjects' generation to overcome this burdensome task. To do so, we first assessed a refined joint parameter distribution, from which extracting a number of subjects, greater than the target population size. Then, target-size subsets are undersampled from the large cohort. The final virtual population is selected among the subsets as the one maximizing the similarity with T2D data and model parameter distribution, by means of measurement' outcome metrics and Euclidian distance (Δ), respectively. In the final population, almost all the outcome metrics are statistically identical to the clinical counterparts (p-value>0.05) and model parameters' distribution differs by ~5-10% from that derived from data. The methodology described here is flexible, thus resulting suitable for different T2D stages and type 1 diabetes, as well.Clinical Relevance- A straightforward subjects' generation would ease the availability of tailored in silico trials for testing diabetes treatment in a specific population.