Spatiotemporal Optimization of Intratumoural Electric Field Modulation for Cancer Therapy
Abstract
The use of anti-cancer non-ablative electric fields is an expanding area of research that includes clinically available external devices for the treatment of glioblastoma (GBM), and a pre-clinical internal system called Intratumoural Modulation Therapy (IMT). IMT uses multiple electrodes implanted within the tumour to apply low intensity electric fields (~1 V/cm) focused on the target region, anywhere in the brain, with no externally visible devices. In this thesis, multi-electrode spatiotemporally dynamic IMT is investigated through computer simulation, numerical optimization, brain phantom and in vitro validation methods. These planning and validation strategies are hypothesized to improve tumour coverage with the necessary electric field and improving treatment efficiency through minimizing number of electrodes, power consumption, and manual planning time.
The development of an IMT optimization algorithm that considers the placement of multiple electrodes, voltage amplitude and phase shift of input waveforms showed that human scale tumours are coverable with anti-cancer electric fields. Additionally, maximally separating the relative phase shifts of sinusoidal voltage waveforms applied to the electrodes, induces rotating electric fields that cover the tumour over time, with spatially homogeneous time averaged fields. A treatment planning system designed specifically for IMT considered optimizable electrode trajectories and patient images to create custom field plans for each patient, which was validated using robotic electrode implantation on a brain phantom. These custom fields can be optimized to conform to patient-specific tumour size, shape, or location. The efficacy of spatiotemporally dynamic fields was validated by developing a purpose-built in vitro device to deliver multi-electrode IMT to patient derived GBM cells. Cell viability was reduced when subjected to these rotating electric fields, supporting the optimality criteria derived analytically.
The IMT optimization algorithm and planning system, supporting phantom validation and in vitro data, together with an accompanying planning system user guide support the move to clinical trials in the future. Overall, IMT technology has been advanced in this thesis to include patient-specific treatment planning optimization, a development that holds significance towards the future clinical implementation of IMT and treatment goals.