Heat transfer during TTFields treatment: Influence of the uncertainty of the electric and thermal parameters on the predicted temperature distribution

Abstract

Background and objectives: Tumor Treating Fields (TTFields) is a technique currently used in the treatment of glioblastoma. It consists in applying an electric field (EF) with a frequency of 200 kHz using two pairs of transducer arrays placed on the head. Current should be injected at least 18 h/day and induce a minimum EF intensity of 1 V/cm at the tumor bed for the treatment to be effective. To avoid scalp burns, Optune, the device used to apply this technique in patients, monitors the temperature of the transducers and keeps them below 41 °C by reducing the injected current. The goal of this study was to quantify the impact of the uncertainty associated with the electric and thermal parameters on the predicted temperature of the transducers and of each tissue when TTFields were applied. Methods: We used a realistic head model, added the two pairs of transducers arrays on the scalp and a virtual lesion, mimicking a glioblastoma tumor in the right hemisphere. Minimum, standard and maximum values for the electric and thermal properties of each tissue were taken from the literature after an extensive review. We used finite element methods (COMSOL Multiphysics) to solve Laplace's equation for the electric potential and Pennes’ equation for the temperature distribution. Results: We observed that the electric conductivity of the scalp and skull, as well as scalp's blood perfusion and thermal conductivity were the parameters to which tissue and transducers temperature were most sensitive to. Considering all simulations, scalp's maximum temperature was around 43.5 °C, skull's 42 °C, CSF's 41.2 °C and brain's 39.3 °C. According to the literature, for this temperature range, some physiological changes are predicted only for the brain. The average temperature of the transducers varied between 38.1 °C and 41.6 °C which suggests that modelling TTFields current injection is very sensitive to the parameters chosen. Conclusions: Better knowledge of the physical properties of tissues and materials and how they change with the temperature is needed to improve the accuracy of these predictions. This information would likely decrease the predicted temperature maxima in the brain and thus help ascertaining TTFields safety from a thermal point of view.