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

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.

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.

Design of Personalized Neoantigen RNA Vaccines Against Cancer Based on Next-Generation Sequencing Data

The good clinical results of immune checkpoint inhibitors (ICIs) in recent cancer therapy and the success of RNA vaccines against SARS-nCoV2 have provided important lessons to the scientific community. On the one hand, the efficacy of ICI depends on the number and immunogenicity of tumor neoantigens (TNAs) which unfortunately are not abundantly expressed in many cancer subtypes. On the other hand, novel RNA vaccines have significantly improved both the stability and immunogenicity of mRNA and its efficient delivery, this way overcoming past technique limitations and also allowing a quick vaccine development at the same time. These two facts together have triggered a resurgence of therapeutic cancer vaccines which can be designed to include individual TNAs and be synthesized in a timeframe short enough to be suitable for the tailored treatment of a given cancer patient.In this chapter, we explain the pipeline for the synthesis of TNA-carrying RNA vaccines which encompasses several steps such as individual tumor next-generation sequencing (NGS), selection of immunogenic TNAs, nucleic acid synthesis, drug delivery systems, and immunogenicity assessment, all of each step comprising different alternatives and variations which will be discussed.

The Evidence of the Bystander Effect after Bleomycin Electrotransfer and Irreversible Electroporation

One of current applications of electroporation is electrochemotherapy and electroablation for local cancer treatment. Both of these electroporation modalities share some similarities with radiation therapy, one of which could be the bystander effect. In this study, we aimed to investigate the role of the bystander effect following these electroporation-based treatments. During direct CHO-K1 cell treatment, cells were electroporated using one 100 µs duration square wave electric pulse at 1400 V/cm (for bleomycin electrotransfer) or 2800 V/cm (for irreversible electroporation). To evaluate the bystander effect, the medium was taken from directly treated cells after 24 h incubation and applied on unaffected cells. Six days after the treatment, cell viability and colony sizes were evaluated using the cell colony formation assay. The results showed that the bystander effect after bleomycin electrotransfer had a strong negative impact on cell viability and cell colony size, which decreased to 2.8% and 23.1%, respectively. On the contrary, irreversible electroporation induced a strong positive bystander effect on cell viability, which increased to 149.3%. In conclusion, the results presented may serve as a platform for further analysis of the bystander effect after electroporation-based therapies and may ultimately lead to refined application of these therapies in clinics.

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.

CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution

BACKGROUND

Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity.

METHODS AND RESULTS

The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude.

CONCLUSION

The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.