EView: An electric field visualization web platform for electroporation-based therapies

A novel gas embolotherapy using microbubbles electrocoalescence for cancer treatment

BACKGROUND AND OBJECTIVE

Embolotherapy has been increasingly used to disrupt tumor growth. Despite its success in the occlusion of microvessels, it has drawbacks such as limited access to the target location, limited control of the blocker size, and inattention to the tumor characteristics, especially high interstitial fluid pressure. The present work introduces a novel numerical method of gas embolotherapy for cancer treatment through tumor vessel occlusion.

METHODS

The gas microbubbles are generated from Levovist bolus injection into the tumor microvessel. The microbubble movement in the blood flow is innovatively controlled by an electric field applied to the tumor-feeding vessel. The interaction between the Levovist microbubbles and the electric field is resolved by developing a fully coupled model using the phase-field model, Carreau model for non-Newtonian blood, Navier-Stokes equations and Maxwell stress tensor. Additionally, the critical effect of high interstitial fluid pressure as a characteristic of solid tumors is included.

RESULTS

The findings of this study indicate that the rates of microbubble deformation and displacement increase with the applied potential intensity to the microvessel wall. Accordingly, the required time for a microbubble to join the upper microvessel wall reduces from 1.97ms to 22 μs with an increase of the electric potential from 3.5V to 12.5V. Additionally, an electric potential of 12.5V causes the microbubbles coalescence and formation of a gas column against the bloodstream.

CONCLUSIONS

Clinically, our novel embolization procedure can be considered a non-invasive targeted therapy, and under a controlled electric field, the blocker size can be precisely controlled. Also, the proposed method has the potential to be used as a gradual treatment in advanced cancers as tumors develop resistance and relapse.

Rapid estimation of electroporation-dependent tissue properties in canine lung tumors using a deep neural network

The efficiency of electroporation treatments depends on the application of a critical electric field over the targeted tissue volume. Both the electric field and temperature distribution strongly depend on the tissue-specific electrical properties, which both differ between patients in healthy and malignant tissues and change in an electric field-dependent manner from the electroporation process itself. Therefore, tissue property estimations are paramount for treatment planning with electroporation therapies. Ex vivo methods to find electrical tissue properties often misrepresent the targeted tissue, especially when translating results to tumors. A voltage ramp is an in situ method that applies a series of increasing electric potentials across treatment electrodes and measures the resulting current. Here, we develop a robust deep neural network, trained on finite element model simulations, to directly predict tissue properties from a measured voltage ramp. There was minimal test error (R2>0.94;p<0.0001) in three important electric tissue properties. Further, our model was validated to correctly predict the complete dynamic conductivity curve in a previously characterized ex vivo liver model (R2>0.93;p<0.0001) within 100 s from probe insertion, showing great utility for a clinical application. Lastly, we characterize the first reported electrical tissue properties of lung tumors from five canine patients (R2>0.99;p<0.0001). We believe this platform can be incorporated prior to treatment to quickly ascertain patient-specific tissue properties required for electroporation treatment planning models or real-time treatment prediction algorithms. Further, this method can be used over traditional ex vivo methods for in situ tissue characterization with clinically relevant geometries.

PIRET-A Platform for Treatment Planning in Electroporation-Based Therapies

Tissue electroporation is the basis of several therapies. Electroporation is performed by briefly exposing tissues to high electric fields. It is generally accepted that electroporation is effective where an electric field magnitude threshold is overreached. However, it is difficult to preoperatively estimate the field distribution because it is highly dependent on anatomy and treatment parameters.

OBJECTIVE

We developed PIRET, a platform to predict the treatment volume in electroporation-based therapies.

METHODS

The platform seamlessly integrates tools to build patient-specific models where the electric field is simulated to predict the treatment volume. Patient anatomy is segmented from medical images and 3D reconstruction aids in placing the electrodes and setting up treatment parameters.

RESULTS

Four canine patients that had been treated with high-frequency irreversible electroporation were retrospectively planned with PIRET and with a workflow commonly used in previous studies, which uses different general-purpose segmentation (3D Slicer) and modeling software (3Matic and COMSOL Multiphysics). PIRET outperformed the other workflow by 65 minutes (× 1.7 faster), thanks to the improved user experience during treatment setup and model building. Both approaches computed similarly accurate electric field distributions, with average Dice scores higher than 0.93.

CONCLUSION

A platform which integrates all the required tools for electroporation treatment planning is presented. Treatment plan can be performed rapidly with minimal user interaction in a stand-alone platform.

SIGNIFICANCE

This platform is, to the best of our knowledge, the most complete software for treatment planning of irreversible electroporation. It can potentially be used for other electroporation applications.

Optimization of Transpedicular Electrode Insertion for Electroporation-Based Treatments of Vertebral Tumors

Electroporation-based treatments such as electrochemotherapy and irreversible electroporation ablation have sparked interest with respect to their use in medicine. Treatment planning involves determining the best possible electrode positions and voltage amplitudes to ensure treatment of the entire clinical target volume (CTV). This process is mainly performed manually or with computationally intensive genetic algorithms. In this study, an algorithm was developed to optimize electrode positions for the electrochemotherapy of vertebral tumors without using computationally intensive methods. The algorithm considers the electric field distribution in the CTV, identifies undertreated areas, and uses this information to iteratively shift the electrodes from their initial positions to cover the entire CTV. The algorithm performs successfully for different spinal segments, tumor sizes, and positions within the vertebra. The average optimization time was 71 s with an average of 4.9 iterations performed. The algorithm significantly reduces the time and expertise required to create a treatment plan for vertebral tumors. This study serves as a proof of concept that electrode positions can be determined (semi-)automatically based on the spatial information of the electric field distribution in the target tissue. The algorithm is currently designed for the electrochemotherapy of vertebral tumors via a transpedicular approach but could be adapted for other anatomic sites in the future.

Improving Prediction of the Potential Distribution Induced by Cylindrical Electrodes within a Homogeneous Rectangular Grid during Irreversible Electroporation

Background: Irreversible electroporation (IRE) is an ablation technique based on the application of short, high-voltage pulses between needle electrodes (diameter: ~1.0 × 10−3 m). A Finite Difference-based software simulating IRE treatment generally uses rectangular grids, yielding discretization issues when modeling cylindrical electrodes and potentially affecting the validity of treatment planning simulations. Aim: Develop an Electric-Potential Estimation (EPE) method for accurate prediction of the electric-potential distribution in the vicinity of cylindrical electrodes. Methods: The electric-potential values in the voxels neighboring the cylindrical electrode voxels were corrected based on analytical solutions derived for coaxial/cylindrical electrodes. Simulations at varying grid resolutions were validated using analytical models. Low-resolution heterogeneous simulations at 2.0 × 10−3 m excluding/including EPE were compared with high-resolution results at 0.25 × 10−3 m. Results: EPE significantly reduced maximal errors compared to analytical results for the electric-potential distributions (26.6–71.8%→0.4%) and for the electrical resistance (30%→1–6%) at 3.0 × 10−3 m voxel-size. EPE significantly improved the mean-deviation (43.1–52.8%→13.0–24.3%) and the calculation-time gain (>15,000×) of low-resolution compared to high-resolution heterogeneous simulations. Conclusions: EPE can accurately predict the potential distribution of neighboring cylindrical electrodes, regardless of size, position, and orientation in a rectangular grid. The simulation time of treatment planning can therefore be shortened by using large voxel-sized models without affecting accuracy of the electric-field distribution, enabling real-time clinical IRE treatment planning.

Modeling methods for treatment planning in overlapping electroporation treatments

OBJECTIVE

Irreversible electroporation (IRE) is a non thermal tissue ablation therapy which is induced by applying high voltage waveforms across electrode pairs. When multiple electrode pairs are sequentially used, the treatment volume (TV) is typically computed as the geometric union of the TVs of individual pairs. However, this method neglects that some regions are exposed to overlapping treatments. Recently, a model describing cell survival probability was introduced which effectively predicted TV with overlapping fields in vivo. However, treatment overlap has yet to be quantified. This study characterizes TV overlap in a controlled in vitro setup with the two existing methods which are compared to an adapted logistic model proposed here.

METHODS

CHO cells were immobilized in agarose gel. Initially, we characterized the electric field threshold and the cell survival probability for overlapping treatments. Subsequently, we created a 2D setup where we compared and validated the accuracy of the different methods in predicting the TV.

RESULTS

Overlap can reduce the electric field threshold required to induce cell death, particularly for treatments with low pulse number. However, it does not have a major impact on TV in the models assayed here, and all the studied methods predict TV with similar accuracy.

CONCLUSION

Treatment overlap has a minor influence in the TV for typical protocols found in IRE therapies.

SIGNIFICANCE

This study provides evidence that the modeling method used in most pre-clinical and clinical studies seems adequate.

OpenEP: an open-source simulator for electroporation-based tumor treatments

Electroporation (EP), the increase of cell membrane permeability due to the application of electric pulses, is a universal phenomenon with a broad range of applications. In medicine, some of the foremost EP-based tumor treatments are electrochemotherapy (ECT), irreversible electroporation, and gene electrotransfer (GET). The electroporation phenomenon is explained as the formation of cell membrane pores when a transmembrane cell voltage reaches a threshold value. Predicting the outcome of an EP-based tumor treatment consists of finding the electric field distribution with an electric threshold value covering the tumor (electroporated tissue). Threshold and electroporated tissue are also a function of the number of pulses, constituting a complex phenomenon requiring mathematical modeling. We present OpenEP, an open-source specific purpose simulator for EP-based tumor treatments, modeling among other variables, threshold, and electroporated tissue variations in time. Distributed under a free/libre user license, OpenEP allows the customization of tissue type; electrode geometry and material; pulse type, intensity, length, and frequency. OpenEP facilitates the prediction of an optimal EP-based protocol, such as ECT or GET, defined as the critical pulse dosage yielding maximum electroporated tissue with minimal damage. OpenEP displays a highly efficient shared memory implementation by taking advantage of parallel resources; this permits a rapid prediction of optimal EP-based treatment efficiency by pulse number tuning.