The potential of time-multiplexed steering in phased array microwave hyperthermia for head and neck cancer treatment

Abstract

Clinical studies have shown that hyperthermia sensitizes tumor cells for conventional therapies. During phased-array microwave hyperthermia, an array of antennas is used to focus the electromagnetic waves at the target region. Selective heating, while preserving the healthy tissue, is a demanding challenge and currently patient specific pre-treatment planning is used to optimize the amplitudes and phases of the waves. In addition, when needed, this single optimal heat distribution is adapted using the simulations based on the feedback from thermo-sensors and the patient. In this paper, we hypothesize that sequential, i.e. 'time-multiplexed', application of multiple Pareto optimal heating patterns provides a better time-averaged treatment quality. To test the benefit of such a time-multiplexed approach, a multi-objective genetic algorithm was introduced to balance two objectives that both focus the specific absorption rate (SAR) delivered to the target region but differ in the suppressing of pre-defined hotspots. This step leads to two Pareto optimal distributions. These 'diverse' antenna settings are then applied sequentially and thermal simulations are used to evaluate the effectiveness of the time-multiplexed steering. The proposed technique is tested using treatment planning data of a representative dataset of five head and neck patients for the HYPERcollar3D. Steering dynamics are analysed and the time-multiplexed steering is compared to the current static solution used in the clinic, i.e. hotspot-target SAR quotient optimization using particle swarm optimization. Our results demonstrate that realistic steering periods of 10s suffice to stabilize temperatures within 0.04 °C and the ability to enhance target heating while reducing hotspots, i.e. 0.3 °C-1.2 °C improvement in T while reducing hotspot temperatures by 0.6 °C-1.5 °C.

Robustness of Time-Multiplexed Hyperthermia to Temperature Dependent Thermal Tissue Properties

“…A time-multiplexed steering strategy for H&N hyperthermia was introduced in [15]. The time-multiplexed antenna settings were found by a multi-objective genetic algorithm and a novel objective function was developed to suppress a specific hotspot.…”

“…Robustness of Time-Multiplexed Hyperthermia to Temperature Dependent Thermal Tissue Properties functions have been proposed [9]- [13]; • Delivery of EM energy into biological tissue using fixed amplitude and phase settings [14] or different antenna settings applied sequentially [15]; • Specific Absorption Rate (SAR) or temperaturebased optimizations [16], [17]. Uncertainty still exists as to the exact thermal properties of tissues and as to whether SAR or temperature simulations are a better basis for optimization.…”

“…Canters et al [18] found that, considering these uncertainties, SAR better predicted the median temperature for hyperthermia applied in the pelvic region. In [15], we proposed a time-multiplexed hyperthermia approach which involved the application of sequential energy distributions, controlled by different antenna settings, with the aim of improving the temperature distribution. A multi-objective genetic algorithm (MOGA) was developed to obtain the time-multiplexed antenna settings (TMS) by simultaneously optimizing the Hotspot to Target SAR Quotient (HTQ) as well as a novel objective function formulated to reduce the hotspot temperature.…”

“…A dataset of five H&N cancer patients treated using the HYPERcollar3D system were used to compare the method to the particle swarm optimization (PSO) currently used in clinical practice at the Erasmus MC Cancer Institute [16]. The study in [15] investigated the effectiveness of TMS with thermal simulations using constant values of blood perfusion and thermal conductivity for tumor, muscle and fat tissue, as described in in [19].…”

“…In this paper, the benefits of time-multiplexed hyperthermia are further evaluated using temperature dependent thermal properties. In particular, the work evaluates the robustness of the time-multiplexed hyperthermia treatment planning algorithm from [15] using the thermal dependent perfusion model developed in [20].…”