The dielectric properties of skin and their influence on the delivery of tumor treating fields to the torso: a study combining in vivo measurements with numerical simulations.

The study of the dielectric properties of tissues plays a key role in understanding the interaction between electromagnetic energy and the human body, for safety assessments of human exposure to electromagnetic fields, as well as for numerous biomedical applications such as tumor treating fields (TTFields). TTFields are low-intensity alternating electric fields in the 100-500 kHz frequency range, which have an antimitotic effect on cancerous cells. TTFields are delivered to the body through pairs of transducer arrays placed on a patient's skin in close proximity to the tumor. Therefore, it is essential to understand how the skin's dielectric properties affect TTFields delivery in clinical settings. In this paper, we present a study combining in vivo measurements with numerical simulations that elucidate how different layers of the skin influence TTFields distribution in the body. The dielectric properties of the skin were measured on volunteers using a setup that ensured skin conditions resembled those when TTFields are delivered to patients. The measured properties were incorporated into a realistic human computational phantom and delivery of TTFields to the phantom's abdomen was simulated. The total impedance of the simulated model was within the mid-range of impedance values measured in patients with pancreatic cancer treated with TTFields. A computational study investigating model sensitivity to the dielectric properties of the skin and subcutaneous adipose tissue (SAT) showed that when skin conductivity increased above a threshold value, the total impedance of the model was largely insensitive to changes in the conductivity of these tissues. Furthermore, for a given current, the field intensity within the internal organs was mostly unaffected by skin properties but was highly sensitive to the conductivity of the organ itself. This study provides a new insight into the role of skin in determining the distribution of TTFields within the body.

First steps to creating a platform for high throughput simulation of TTFields.

Tumor Treating Fields (TTFields) are low intensity alternating electric fields in the 100-500 KHz frequency range that are known to have an anti-mitotic effect on cancerous cells. In the USA, TTFields are approved by the Food and Drug Administration (FDA) for the treatment of glioblastoma (GBM) in both the newly diagnosed and recurrent settings. Optimizing treatment with TTFields requires a deep understanding of how TTFields distribute within the brain. To address this issue, simulations using realistic head models have been performed. However, the preparation of such models is time-consuming and requires a high level of expertise, limiting the usefulness of these models for systematic studies in which the testing of multiple cases is required. Here we present a platform for rapidly simulating TTFields distributions in multiple scenarios. This platform enables high throughput computational simulations to be performed, allowing comparison of field distributions within the head in multiple clinically relevant scenarios. The simulation setup is simple and intuitive, allowing non-expert users to run simulations and evaluate results, thereby providing a valuable tool for studying how to optimize TTFields delivery in the clinic.

Using computational phantoms to improve delivery of Tumor Treating Fields (TTFields) to patients.

This paper reviews the state-of-the-art in simulation-based studies of Tumor Treating Fields (TTFields) and highlights major aspects of TTFields in which simulation-based studies could affect clinical outcomes. A major challenge is how to simulate multiple scenarios rapidly for TTFields delivery. Overcoming this challenge will enable a better understanding of how TTFields distribution is correlated with disease progression, leading to better transducer array designs and field optimization procedures, ultimately improving patient outcomes.