Quality assurance phantoms for deep hyperthermia devices: design principles informed by computational modeling.

OBJECTIVE: Accurate thermal dose delivery is essential for the clinical success of deep hyperthermia (DHT). As the European Society for Hyperthermic Oncology (ESHO) shifts toward temperature-based QA metrics, standardized tissue-mimicking phantoms for DHT become increasingly important. This study provides quantitative evidence to guide phantom design using computational modeling supported by experimental validation. Approach: Numerical simulations were performed using a simplified model of a clinical DHT applicator operating at 75 and 100 MHz. Parametric studies assessed the influence of phantom geometry (diameter, length, wall thickness) and dielectric properties (relative permittivity and electrical conductivity) on specific absorption rate and temperature distributions. Agreement with patient models was evaluated by comparing phantom simulations with temperature profiles derived from anatomical models. A gel phantom was constructed to validate simulations experimentally. The effects of thermal mapping and positioning errors on thermal profiles were also analyzed. Main results: Simulations demonstrated that dielectric properties, especially electrical conductivity, had the strongest effect on heating patterns and temperature gradients; lower conductivity produced steeper focal profiles and reduced peripheral hotspots. Comparisons with patient models revealed good agreement in the focal region. Experimental measurements matched simulated temperatures near the applicator focus, with an average deviation of 0.7 ± 0.5 °C in the most reproducible series. Larger deviations near boundaries were attributed to thermal mapping uncertainties, catheter bending, and phantom misalignment. Including these uncertainties in the model indicated the need for margins reflecting average positioning errors of ±2.5 cm along the probe axis and ±0.5 cm perpendicular to it. Significance: This study provides a validated modeling framework and design recommendations that support temperature-based QA procedures for DHT. By clarifying how phantom parameters influence measurable temperature profiles and quantifying key sources of experimental uncertainty, this work strengthens the basis for standardized QA phantoms and enhances the reliability of performance assessment for clinical DHT systems.