Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

Percutaneous thermal ablation has proved to be an effective modality for treating both benign and malignant tumors in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50 oC, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumor destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of- the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non- invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow

Highlighting the effect of heterogeneous blood perfusion on radio-frequency ablation of human brain tumors : An image-based numerical investigation

Peer reviewedPostprin

Investigation of Heat Therapies using Multi-Scale Models and Statistical Methods

Ph.DDOCTOR OF PHILOSOPH

Preoperative trajectory planning for percutaneous procedures in deformable environments

International audienceIn image-guided percutaneous interventions, a precise planning of the needle path is a key factor to a successful intervention. In this paper we propose a novel method for computing a patient-specific optimal path for such interventions, accounting for both the deformation of the needle and soft tissues due to the insertion of the needle in the body. To achieve this objective, we propose an optimization method for estimating preoperatively a curved trajectory allowing to reach a target even in the case of tissue motion and needle bending. Needle insertions are simulated and regarded as evaluations of the objective function by the iterative planning process. In order to test the planning algorithm, it is coupled with a fast needle insertion simulation involving a flexible needle model and soft tissue finite element modeling, and experimented on the use-case of thermal ablation of liver tumors. Our algorithm has been successfully tested on twelve datasets of patient-specific geometries. Fast convergence to the actual optimal solution has been shown. This method is designed to be adapted to a wide range of percutaneous interventions

Spatiotemporal Optimization of Intratumoural Electric Field Modulation for Cancer Therapy

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

Computer modelling and simulation of radiofrequency ablation of bone tumors

Radiofrequency ablation (RFA) is a minimally invasive technique used for the treatment of many types of tumors, with a growing interest in the treatment of bone tumors. It uses radio waves to heat up the tissues surrounding a needle-like applicator to destroy the target tumor by exposing the surrounding tissues to high temperatures for a long enough time. However, the technique has been used mostly to treat tumors in other organs, and although it is safe and effective, with little data regarding how much damage it causes in bone tumors, it makes prospective planning challenging. Tumors must be completely destroyed to avoid recurrence but damage to healthy tissues must be minimized. The generation of heat and heat transfer can be modeled mathematically. With this, it was possible to create computer models to simulate the procedure. By looking at retrospective data from patients treated for RFA of bone tumors, the extensions of damage were measured, and the length of the procedure and other parameters were also captured. With these data, it was possible to fit the models to find the optimal parameters to predict the outcomes. Finally, complex 3D computational patient-specific models were created from medical images, and it was possible to replicate the clinical outcomes. This thesis thus showed that computational models could be used to predict the extension of thermal damage, allowing interventional radiologist to plan prospectively with greater accuracy, allowing safer and more effective interventions