Search

Tumor Treating Fields (TTFields) are electric fields known to exert an anti-mitotic effect on cancerous tumors. TTFields have been approved for the treatment of glioblastoma and malignant pleural mesothelioma. Recent studies have shown a correlation between TTFields doses delivered to the tumor bed and patient survival. These findings suggest that patient outcome could be significantly improved with rigorous treatment planning, in which numerical simulations are used to plan treatment in order to optimize delivery of TTFields to the tumor bed.Performing such adaptive planning in a practical and meaningful manner requires a rigorous and scientifically proven framework defining TTFields dose and showing how dose distribution influences disease progression in different malignancies (TTFields dosimetry). At EMBC 2019, several talks discussing key components related to TTFields dosimetry and treatment planning were presented. Here we provide a short overview of this work and discuss how it sets the foundations for the emerging field of TTFields dosimetry and treatment planning.

BACKGROUND For understanding the electric field distributions in glioblastoma (GBM) patients receiving OptuneTM therapy computational head models are employed. Accurate and fast model creation is of high importance to patient-specific treatment planning for improving efficacy, i.e., for maximizing intensity delivered to the tumor which depends on the tissues’ electric properties (EPs). Traditional model creation relies on time-consuming tissue segmentation and troublesome binary categorization of distinct tumor areas for assigning homogenous EPs. Here, we present a feasibility study of a new approach for fast model creation that uses individually created, heterogeneous EP maps from conventional MRIs. METHODS In a previous animal study we adapted water-content based electrical properties tomography (wEPT) for creating electrical conductivity (σ) maps at 200 kHz, the operating frequency of OptuneTM therapy. This adapted wEPT approach uses a T1w and a PD image to map the tissues’ water-content (WC) with a simple function. Subsequently the σ map is calculated as a function of WC based on Maxwell’s mixture theory. Three patients of the EF-14 trial were selected for calculating WC and σ maps. One patient was chosen to create a computational head model for simulating OptuneTM treatment. RESULTS The wEPT-estimated values of WC and σ in the healthy brain are accurate, homogenous and consistent among patients. Contrary, wEPT-estimates of WC and σ in tumor tissues are very heterogeneous and variable between patients. The patient-specific model with wEPT reveals more detailed current pathways during OptuneTM therapy. CONCLUSIONS The results emphasize the need for individual head model creation, since binary segmentation masks with pre-defined σ values are not recommended for the heterogeneous and variable tumor. The presented approach holds great promise for rapid creation of patient-specific computational models because only conventional MRIs are needed. However, this method needs to be validated and further established with analyzing more patients.

BACKGROUND Tumor Treating Fields (TTFields; approved anti-neoplastic treatment modality) are delivered via application of low intensity, intermediate frequency, alternating electrical fields. The electrical properties of cells (eg, permittivity and conductivity) determine the optimal TTFields frequency that would elicit the greatest cell count reduction. Currently, no predictive markers exist to determine TTFields response and optimal frequency for individual patient application. The study goal was to evaluate the correlation between electrical properties of cells and TTFields’s optimal frequency and sensitivity. The 3DEPTM reader (LabTech) determines cellular electrical properties, including permittivity and conductivity, by using dielectrophoresis (DEP) force. DEP is a physical effect that generates a force on polarizable particles, such as cells, subjected to non-uniform electric fields. METHODS Utilizing the 3DEP reader, baseline electrical properties (permittivity and conductivity) of 17 cell lines from different tumor-types were determined. Curves were analyzed using 2-way ANOVA. The optimal TTFields frequency of each cell line was determined by evaluating TTFields cytotoxicity at various frequencies using the inovitroTM system. Electrical properties of each cell line were compared with the optimal TTFields frequency and sensitivity. RESULTS Significant differences (P< 0.001) were demonstrated between the lower frequency range of the 3DEP curves that correspond to cellular membrane capacitance at TTFields optimal frequencies of 150 kHz (9 cell lines) and 200 kHz (8 cell lines). Also, membrane capacitance was a good predictor of TTFields sensitivity based on curve differences within the low-frequency range. CONCLUSIONS These results demonstrate that cell membrane capacitance correlates with TTFields optimal frequency and sensitivity. Based on these data, there is a strong rational to further explore the potential of measuring the electrical properties of cells as predictive markers to help determine the optimal TTFields frequency for individual patient application and to identify ideal treatment-responders to TTFields.

Tumor-treating fields (TTFields) are a cancer treatment modality that uses alternating electric fields of intermediate frequency (∼100-500 kHz) and low intensity (1-3 V/cm) to disrupt cell division. TTFields are delivered by transducer arrays placed on the skin close to the tumor and act regionally and noninvasively to inhibit tumor growth. TTFields therapy is U.S. Food and Drug Administration approved for the treatment of glioblastoma multiforme, the most common and aggressive primary human brain cancer. Clinical trials testing the safety and efficacy of TTFields for other solid tumor types are underway. The objective of this paper is to review computational approaches used to characterize TTFields. The review covers studies of the macroscopic spatial distribution of TTFields generated in the human head, and of the microscopic field distribution in tumor cells. In addition, preclinical and clinical findings related to TTFields and principles of its operation are summarized. Particular emphasis is put on outlining the potential clinical value inferred from computational modeling.

The application of cochlear implants can be studied with computational models. The electrical potential distribution induced by an implanted device is evaluated with a volume conductor model, which is used as input for neuron models to simulate the reaction of cochlear neurons to micro-stimulation. In order to reliably predict the complex excitation profiles it is vital to consider an accurate representation of the human cochlea geometry including detailed three-dimensional pathways of auditory neurons reaching from the organ of Corti through the cochlea-volume. In this study, high-resolution micro-CT imaging (Δx = Δy = Δz = 3 μm) was used to reconstruct the pathways of 30 tonotopically organized nerve fiber bundles, distributed over eight octaves (11500–40 Hz). Results of the computational framework predict: (i) the peripheral process is most sensitive to cathodic stimulation (CAT), (ii) in many cases CAT elicits spikes in the peripheral terminal at threshold but with larger stimuli there is a second spike initiation site within the peripheral process, (iii) anodic stimuli (ANO) can excite the central process even at threshold, (iv) the recruitment of fibers by electrodes located in the narrowing middle- and apical turn is complex and impedes focal excitation of low frequency fibers, (v) degenerated cells which lost the peripheral process are more sensitive to CAT when their somata are totally covered with 2 membranes of a glial cell but they become ANO sensitive when the myelin covering is reduced.

BACKGROUND Electrical properties (EPs) of brain tissue, specifically brain tumors, crucially determine the field distribution of Tumor Treating Fields (TTFields), an anti-mitotic treatment approved for glioblastoma multiforme (GBM). Due to the correlation of TTFields efficacy and field intensity at the tumor region, the knowledge of EPs in each patient is of great importance for patient-specific planning of treatment. Water content electrical properties tomography (wEPT) is a non-invasive imaging technique using water content (WC) maps obtained from rapidly acquired and processed conventional sequences to estimate the EPs of brain tissue at 128 MHz. The WC maps of this approach are constructed from two spin echo sequences similar to a T1 and a PD image. Following previous studies in rat tumor models demonstrating promising wEPT mapping of EPs in the brain at 200, this study examines the feasibility of this approach in human GBM patients. MATERIAL AND METHODS For three patients of the EF-14 trial population, we divided T1 and PD images pixel-by-pixel to obtain the image ratio. Using a transfer function, WC maps were generated and maps of the electrical conductivity σ and the relative permittivity ε r at 200 kHz were calculated with two different equations. RESULTS The median value of estimated WC remains similar in healthy brain tissues among all patients, ~73.5% in the white matter, ~82% in the gray matter. The median values of wEPT-estimated σ at 200 kHz in the white matter is ~0.09 S/m and in the gray matter ~0.18 S/m, corresponding median values of ε r at 200 kHz are ~2100 and ~3000 in white and gray matter respectively. Contrary, in the tumor the spread between the median values of WC and EPs is much higher. Stating the most important findings, in the necrosis median WC are 90.3%, 92.3%, 85.2% in patients 1–3 respectively with corresponding median σ values of 0.494, 0.657, 0.25 S/m. In the enhancing tumor the spread of median WC is even higher (67.2%, 83.6%, 85.5%), yet lower spread but also very heterogeneous median σ values of 0.075 S/m, 0.208, 0.259 S/m are estimated with wEPT. CONCLUSION Our results demonstrate the adaption of wEPT for mapping of WC and EPs at 200 kHz in three human GBM patients. In contrast to the vastly irregular tumor tissue, our estimations in healthy brain tissue are similar between patients and in accordance with EPs experimentally measured during our animal experiments and consistent with reported values in the literature. Hence, wEPT is a promising, fast technique based on regular MRI that might help patient-specific treatment planning of TTFields therapy, although the mapping of tumor tissue needs further confirmation in a greater population and investigations of EPs of excised tumor tissue samples should be conducted.

BACKGROUND Tumor treating fields (TTFields) are currently approved for the treatment of glioblastoma multiforme (GBM, using 200 kHz), and being tested in other tumor types such as non-small cell lung cancer and brain metastases occurring in this indication (LUNAR and METIS trials, using 150 kHz). Response to TTFields in cancer cells was empirically shown to be frequency-dependent specific for cell type; however, there are no markers available predicting optimal frequency or response in different cancer types or individual patients to date. There is evidence indicating electrical properties determine the optimal anti-mitotic frequency. This study analyzed the correlation of electrical properties of cells with their optimal TTFields frequency and sensitivity using the 3DEP reader (LabTech) to determine the electrical properties with the help of Dielectrophoresis (DEP) force. With this technique, cell movements within electric fields of different frequencies can by analyzed based on the physical effect of DEP, exercising a force on polarizable particles inside a non-homogeneous electric field. MATERIAL AND METHODS We used the 3DEP reader to obtain baseline properties (permittivity and conductivity) of 17 different cell lines of several tumor types. The resulting curves were analyzed by a 2-way ANOVA. Additionally, we determined the optimal frequency for maximum cytotoxic effect for each cell line using the inovitroTM system and eventually compared with the detected electrical properties. RESULTS We found cell lines with an optimal TTFields frequency of 150 kHz (corresponding to cells with a membrane capacitance in the lower range of the observed 3DEP curves, n=9) to possess significantly different (p CONCLUSION This study showed a correlation of cell membrane capacitance and optimal TTFields frequency and response. Our results provide a substantial rationale for further studies investigating the predictive potential of electrical properties of tumor cells as a measure for the optimal frequency and sensitivity to TTFields in individual patients.

Here we describe preliminary results of a simulation-based study investigating the connection between tumor-treating fields (TTFields) distribution in the brain and glioblastoma patient outcomes. In order to perform this study, we developed a semiautomatic method for creating realistic head models from glioblastoma patient MRI using a deformable template and atlas-based registration. This method, which is described in detail in this chapter, is robust and fast, making it suitable for rapid creation of multiple realistic head models. Using this method, we created 119 head models of newly diagnosed glioblastoma patients that were treated with tumor-treating fields. Finite element simulations were used to simulate delivery of TTFields to these patients, and the connection between field intensity distribution at the tumor bed and patient outcome was analyzed. The result of this analysis support the hypothesis that increasing field intensity at the tumor bed improves patient outcome.

Electrical properties tomography (EPT) has been studied in order to non-invasively map the conductivity and permittivity of brain tissues. Various approaches have been investigated for predicting the electrical properties (EPs) at either very low or high Larmor frequencies. An example of the latter is water-based EPT (wEPT), which derives the EP maps from the image ratio of two T1-weighted images with different repetition times. The rationale is based on the fact that EPs can be modeled as monotonic functions of water content, which in turn can be estimated from the image ratio. The objective of this study is to examine if wEPT can be adapted to accurately create conductivity maps of the brain in the frequency range of 200–1000 kHz, and if EPs of pathological tissues can be estimated using the same approach. These frequencies are of particular interest as a cancer treatment modality, Tumor Treating Fields, utilizes electric fields with 200 kHz to treat glioblastoma. The treatment efficacy depends on the delivered field intensity at the target, which is determined by the EP distribution within the brain and specifically within the heterogeneous tumor. Experimental measurements and wEPT imaging estimations were performed in animal brain samples and tumor-bearing rats. These studies suggest that wEPT-estimated water content maps are reliable. In calf samples, the average error of wEPT-estimated water content is between 2.5% and 3.5%, and in rat samples between 2.2% and 6.4% (measurement error ± 1%). For wEPT estimations of conductivity at 200 kHz, the average errors range from 13.2% to 13.6% in calf tissue samples and from 20.7% to 22.1% rat tissue samples (measurement error ± 10%). Testing various brain and tumor tissues demonstrates a clear trend for wEPT estimations of water content and EPs. The approach might be enhanced by including adaptions according to additional radiological features derived from other imaging modalities, such as T2-weighted imaging or diffusion imaging.

BACKGROUND: Understanding how Tumor Treating Fields (TTFields) distribute in the brain is important for optimizing delivery. The distribution depends on the electric properties (EPs) of the brain, which are heterogeneous. Therefore, there is a need for methods that map electric properties within tissue with high spatial resolution. Water content based EP tomography (wEPT) utilizes the ratio of two T1w to map tissue water content, and EPs. wEPT has been applied to map EPs at 128 MHz. This study examines the suitability of wEPT to map EPs in the brain at 100–1000 kHz (TTFields frequency range). MATERIALS AND METHODS: A model connecting T1 images, Water content (WC) and EPs in the 100–1000 kHz range was created using tissue samples derived from bovine brains and aporcine CSF sample. For each sample, T1w images were acquired and WC and EPs measured. Curve fitting yielded models connecting Ir, WC and EPs. Next, wEPT was applied to map water content and EPs in tumor-bearing rat brains. EPs and WC were measured on 6 samples excised from each brain. The measured values were compared to the WC and EPs in the regions of the wEPT map corresponding to the sample. RESULTS: Water content estimated using wEPT agreed well with measurements on excised sample. A connection between EPs estimated with wEPT and the measured values was found. However, in some samples, especially samples excised from the tumors, large differences between wEPT-derived EP values and measurements existed. CONCLUSION: wEPT maps WC in healthy and tumor brain tissues However, the relationship between WC and EPs within this frequency range is not clear. Further work is required to refine this relationship enabling a robust non-invasive method for mapping electrical properties within the brain, and better strategies for optimizing TTFields treatment.

Introduction Magnetic stimulation (MS) of the peripheral and central nervous system has driven an increasing clinical interest since the 1980’s. Modelling studies of the electric field (E-field) distributions and neuron mathematical models contributed to understand the physical effects underlying MS mechanisms. These results can improve coil design and optimize clinical protocols ( Salvador et al., 2011 ). Objectives The main objective of this study is to calculate the E-field distribution in the lumbar spinal cord (SC) during spinal MS, for clinical applications. Methods A realistic MRI-based human model was built using the MIMICS software (v16, http://www.materialise.com/mimics ). Calculations of the primary E-field were performed with MATLAB (version 2015b, www.mathworks.com ) and the secondary E-field with COMSOL Multiphysics (version 4.3b, www.comsol.com ). The figure-8 coil was based on Magstim’s double 70 mm model and its vertex was located over the T12 spinous process. Two orientations were considered for the coil: PA, postero-anterior; LR, left–right. In the calculation, the time derivative of the current in the coil was set to 61.6 A/(micro s). Results Download : Download high-res image (468KB) Download : Download full-size image and Download : Download high-res image (475KB) Download : Download full-size image show the E-field’s magnitude distribution in the spinal white matter (WM) and grey matter (GM) for PA and LR configurations. For the PA configuration, maximum values of 26.0 and 21.9 V/m are reached in the lumbo-sacral WM and GM, respectively. The E-field is oriented along the caudal-rostral direction. For the LR model, the lower lumbar and sacral SC show the highest values, reaching 15.2 and 11.8 V/m in the WM and GM, respectively. The E-field is oriented along the medial–lateral direction in this region. Conclusion MS of the SC has an E-field magnitude

BACKGROUND: Tumor Treating Fields (TTFields) are delivered to the brain through two pairs of transducer arrays placed on the patient’s scalp. TTFields distribution in the brain depends the array’s position, patient anatomy and the electric properties of the tissues and tumor. Preclinical studies show that the effect of TTFields is dose-dependent, and depends on the intensity of the TTFields. Therefore, it is important to understand how TTFields distribute in the brain, and how this distribution might influence disease progression. Studying TTFields distribution in the brain most often relies on numerically simulating delivery of TTFields to realistic computational models. Preparation of such models usually involves semi-automatic segmentation of MRI images, which can be a time consuming task. In addition, in clinical scenarios, MRI acquisition time is often reduced by increased slice spacing, limited field of view, or increased scan speed, leading inaccuracies in automated segmentation processes, and increasing the time required for model creation. Here we present a novel method designed to enable rapid model creation under those restrictions. MATERIAL AND METHODS: A highly detailed healthy head model serves as a deformable template from which patient models are created. The first step is pre-processing, involving denoising and background noise reduction, as well as super-resolution algorithms when needed. To create the patient model, the tumor is first segmented manually and masked, leaving only healthy tissues in the MRI, which is then registered to the template space to yield the transformation from patient space to template space. The template is then deformed into the patient space using the inverse transformation, and the tumor is placed back creating a full patient model. Next, automatic identification of landmarks on the patient’s head is used to position the transducer arrays on the head, which are then introduced into the model. Finally, boundary conditions are set, and field distribution is simulated using Finite Differences Time Domain (FDTD) method (Sim4Life V3.0, ZMT-Zurich) RESULTS: We have simulated TTFields distribution of 317 patients treated with TTFields as part of the EF-14 trial. Our method is optimized for accurate contouring of tissues highly influencing the distribution of electric field (Scalp, skull, CSF, ventricles), and it is robust, having the ability to give sufficient results even when MRI data quality is low. Thus enabling a study correlating the spatial distribution of TTFields and patient outcome. CONCLUSIONS: Our process for rapidly creating patient presents a breakthrough that enables the first study in which the spatial distribution of therapeutic electric fields correlates with patient outcome. In the future this method can be used for clinical studies investigating other clinical indications of large datasets.

Preclinical studies show that the efficacy of TTFields increases with field intensity. However, studies investigating the connection between field distribution and patient outcome have yet to be performed. We have developed a novel technique that enables rapid numerical simulation of TTFields distribution within the heads of patients. We are utilizing this technique to investigate the connection between TTFields distribution and patient outcome in patients treated with TTFields during the EF-14 trial (n=466). To date, n=57 cases have been simulated and field intensity distributions in a tumor bed comprising the Gross Tumor Volume (GTV) and a zone extending 1 cm from the GTV were derived. To account for compliance, field values for each patient were multiplied by their average compliance over the first six months of treatment. To test the hypothesis that patient outcome correlates with field intensities, we defined E95 such that 95% of the tumor bed receives field intensities above this value. The patients were split into two groups E95>1.3V/cm (n=13) and E951.3.V/cm OS (35.8 months vs. 23.0 months, p=0.04, HR=0.43) and PFS (11.5 months vs 8.1 months, p=0.12, HR=0.57). Within the E95>1.3V/cm group, a higher percentage of patients showed clinical benefit (stable disease/ partial response, 100% vs 75% p=0.045). OS in both groups is superior to OS in the EF-14-control-arm (16.0 months, p=0.049), indicating that patients benefited from treatment even when field intensities were lower. These preliminary results suggest that field intensity at the tumor bed correlates with patient outcome. Analysis of the full patient group will further elucidate this connection.

Noninvasive electrical stimulation of the central nervous system is attracting increasing interest from the clinical and academic communities as well as from high-tech companies. This interest was sparked by two landmark studies conducted in 2000 and 2001 at the University of G?ttingen, Germany. Michael Nitsche and Walter Paulus showed that by passing a weak, almost imperceptible electric current between two electrodes on the scalp, they could alter the way the human brain responds to stimuli and that the effect persisted for some time after the current was stopped. These findings opened the prospect of therapeutic applications of transcranial direct-current stimulation (tDCS).

Cancer represents a compilation of diseases characterized by rapidly dividing, invasive cells. Worldwide data indicate that over 14 million new cancers were diagnosed in 2012, with a projected increase of more than 19 million diagnosed cases by 2025 [1]. Survival rates for some cancers have increased dramatically, but there are still cancer types for which the prognosis is poor and few treatments exist. Thus, there is a growing need for new therapies targeting these difficult-to-treat cancers.

Tumor Treating Fields (TTFields) are alternating electric fields in the intermediate frequency range (100–300 kHz) of low-intensity (1–3 V/cm). TTFields are an anti-mitotic treatment against solid tumors, which are approved for Glioblastoma Multiforme (GBM) patients. These electric fields are induced non-invasively by transducer arrays placed directly on the patient's scalp. Cell culture experiments showed that treatment efficacy is dependent on the induced field intensity. In clinical practice, a software called NovoTalTM uses head measurements to estimate the optimal array placement to maximize the electric field delivery to the tumor. Computational studies predict an increase in the tumor's electric field strength when adapting transducer arrays to its location. Ideally, a personalized head model could be created for each patient, to calculate the electric field distribution for the specific situation. Thus, the optimal transducer layout could be inferred from field calculation rather than distance measurements. Nonetheless, creating realistic head models of patients is time-consuming and often needs user interaction, because automated image segmentation is prone to failure. This study presents a first approach to creating simplified head models consisting of convex hulls of the tissue layers. The model is able to account for anisotropic conductivity in the cortical tissues by using a tensor representation estimated from Diffusion Tensor Imaging. The induced electric field distribution is compared in the simplified and realistic head models. The average field intensities in the brain and tumor are generally slightly higher in the realistic head model, with a maximal ratio of 114% for a simplified model with reasonable layer thicknesses. Thus, the present pipeline is a fast and efficient means towards personalized head models with less complexity involved in characterizing tissue interfaces, while enabling accurate predictions of electric field distribution.

Non-invasive brain and spinal cord stimulation techniques are increasingly used for diagnostic and therapeutic purposes. Knowledge of the spatial distribution of the induced electric field is necessary to interpret experimental results and to optimize field delivery. Since the induced electric field cannot be measured in vivo in humans, computational models play a fundamental role in determining the characteristics of the electric field. We produced computational models of the head and trunk to calculate the electric field induced in the brain and spinal cord by transcranial magnetic stimulation, transcranial direct current stimulation and transcutaneous spinal cord direct current stimulation. The field distribution is highly non-uniform and depends on the type of technique used, on the position of the stimulation sources, and on anatomy. In future these models may be improved by using more accurate and precise values for the physical parameters as they become available, by combining them with neuronal models to predict the outcome of stimulation, and by better segmentation and meshing techniques that make producing individual models practicable.

Tumor treating fields (TTFields) is a therapy that inhibits cell proliferation and has been approved by the U.S Food and Drug Administration (FDA) for the treatment of Glioblastoma Multiforme. This anti-mitotic technique works non-invasively and regionally, and is associated with less toxicity and a better quality of life. Currently a device called Optune™ is clinically used which works with two perpendicular and alternating array pairs each consisting of 3×3 transducers. The aim of this study is to investigate a theoretical alternative array design which consists of two rings of 16 transducers and thus permits various field directions. A realistic human head model with isotropic tissues was used to simulate the electric field distribution induced by the two types of array layouts. One virtual tumour was modelled as a sphere in the white matter close to one lateral ventricle. Four alternative ring design directions were evaluated by activating arrays of 2×2 transducers on opposite sides of the head. The same amount of current was passed through active transducer arrays of the Optune system and the ring design. The electric field distribution in the brain differs for the various array configurations, with higher fields between activated transducer pairs and lower values in distant areas. Nonetheless, the average electric field strength values in the tumour are comparable for the various configurations. Values between 1.00 and 1.91 V/cm were recorded, which are above the threshold for effective treatment. Increasing the amount of field directions could possibly also increase treatment efficacy, because TTFields' effect on cancer cells is highest when the randomly distributed cell division axis is aligned with the field. The results further predict that slightly changing transducer positions only has a minor effect on the electric field. Thus patients might have some freedom to adjust array positions without major concern for treatment efficacy.

Background Tumor Treating Fields (TTFields) are intermediate frequency, alternating electric fields that non-invasively treat cancer. Transducer arrays positioned on the skin in proximity to the targeted tumor transmit TTFields. A post-hoc analysis [Ballo et al. Red Jour. 2019 In Press] has shown that increased patient usage (percent of time on active treatment) and intensity of TTFields delivery direct to the tumor improved survival. Optimal array positioning may enhance TTFields intensity at the tumor site to improve patient experience and survival. Minimization of array exposure area would enhance patient comfort levels and usage to improve survival. Optimizing TTFields delivery and distribution depends on array positioning and geometry, patient anatomy, and the heterogeneous electrical properties of different tissues. We present methodology to optimize TTFields delivery using numerical simulations. Methods TTFields delivery to the brain, lung, and abdomen utilizing representative computational models was investigated. The effects of transducer array size and position on field distribution within the phantoms was analyzed, and an approach to optimize TTFields delivery was developed. Results Field intensity was typically the greatest in between arrays, with larger arrays transmitting higher field power. Anatomical features, such as bones (spine) or a resection cavity significantly influenced field intensity within this region. A generalized methodology to optimize TTFields delivery for improved patient care was based on: (1) Striking a balance between maximal field intensity (largest arrays feasible) and minimal skin exposure to arrays in the disease area; (2) Positioning virtual arrays on a representative, computational patient model to test tumor localization between arrays, to simulate TTFields delivery to patient, and to assess optimal delivery; and (3) Applying an iterative algorithm to shift arrays around their initial positions until field intensity is maximized directly to the tumor bed. Conclusions A generalized treatment methodology as presented by these data will optimize TTFields delivery to the tumor site. Effective TTFields treatment planning is expected to improve patient outcomes. Legal entity responsible for the study Novocure. Funding Novocure. Disclosure N. Urman: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. Z. Bomzon: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. H.S. Hershkovich: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. E.D. Kirson: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. A. Naveh: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. R. Shamir: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. E. Fedorov: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. C. Wenger: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure. U. Weinberg: Full / Part-time employment: Novocure; Shareholder / Stockholder / Stock options: Novocure.

Background: Tumor Treating Fields (TTFields) are an approved anti-neoplastic treatment modality delivered via application of low intensity, intermediate frequency, alternating electric fields. The electrical properties of cells (such as permittivity and conductivity) determine the optimal frequency of TTFields that incurs the highest reduction in cell counts. Currently, there are no predictive markers for determining TTFields response or the optimal frequency to be applied for individual patient. The goal of this study is to determine the correlation between electrical properties of cells and TTFields’s optimal frequency and sensitivity. The 3DEP reader (LabTech) determines the electrical properties of cells, including permittivity and conductivity, by using Dielectrophoresis (DEP) force. DEP is a physical effect that generates a force on polarizable particles experiencing a non-homogeneous electric field and can therefore be used as a technique to analyse the way cells move within electric fields at different frequencies. Methods: The baseline electrical properties (permittivity and conductivity) of 17 cell lines from different tumor types were determined using the 3DEP reader (LabTech). The curves were analyzed using 2-way ANOVA. The optimal TTFields frequency for each cell line was determined by testing the cytotoxic effect of TTFields at various frequencies using the inovitro system. The electrical properties of cells were compared with the optimal TTFields frequency and sensitivity of each cell line. Results: The results demonstrate significant differences (p Citation Format: Moshe Giladi, Einav Zeevi, Karnit Gotlib, Cornelia Wenger, Ariel Naveh, Zeev Bomzon, Eilon D. Kirson, Uri Weinberg, Adrian Kinzel, Yoram Palti. Testing the electrical properties of different cell lines using 3DEP reader and compare to TTFields response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2168.

e14668 Background: Tumor Treating Fields (TTFields) are alternating electric fields used to non-invasively treat cancer. TTFields are delivered via transducer arrays placed on the skin close to the tumor. Post-hoc analysis [1] has shown that delivering higher field power to the tumor and increasing usage (percent of time patient is actively treated) improve patient survival. Thus, optimizing the position of arrays to maximize TTFields power at the tumor could improve survival. At the same time, minimizing the array area to maximize patient comfort and consequently maximizing usage is also likely to improve survival. However, optimizing TTFields delivery is non-trivial since the field distribution is influenced by array positioning and geometry, the anatomy of the patient and the heterogeneous electric properties of different tissues. Here we present a general approach to optimizing Tumor Treating Fields using numerical simulations. Methods: Delivery of TTFields to the brains, lungs and abdomens of realistic computational models was investigated. The effect of the transducer array size and position on the field distribution within the phantoms was analyzed, and an approach for optimizing TTFields delivery developed. Results: Field power is generally highest in the region between the arrays, with larger arrays generally delivering higher field power. Anatomical features such as bones, the spine or a resection cavity significantly influence the field within this region. A general approach to optimizing TTFields delivery is: Maximize field power by using the largest arrays possible. To maximize patient comfort, array size are chose so that significant portions of the skin in the region of disease are not covered by the arrays. Place virtual arrays on a realistic computational model of the patient such that the tumor is located between them and simulate TTFields delivery to the patient. Apply an iterative algorithm to shift the arrays around their initial positions until field power in the tumor bed is maximized. Conclusions: We have developed a general approach to optimizing delivery of TTFields to the tumor. Effective TTFields treatment planning is expected to improve patient outcome. [1] Ballo et. al., submitted to RED Journal 2018.

Previous studies have indicated potential neuromodulation of the spinal circuitry by transcutaneous spinal direct current stimulation (tsDCS), such as changes in motor unit recruitment, shortening of the peripheral silent period and interference with supraspinal input to lower motor neurons. All of these effects were dependent on the polarity of the electrodes. The present study investigates how the distance between the electrodes during tsDCS influences the electric field's (E-field) spatial distribution in the lumbar and sacral spinal cord (SC). The electrodes were placed longitudinally along the SC, with the target electrode over the lumbar spine, and the return electrode above the former, considering four different distances (4, 8, 12 and 16 cm from the target). A fifth configuration was also tested with the return electrode over the right deltoid muscle. Peak values of the E-field's magnitude are found in the lumbo-sacral region of the SC for all tested configurations. Increasing the distance between the electrodes results in a wider spread of the E-field magnitude distribution along the SC, with larger maximum peak values and a smoother variation. The fifth configuration does not present the highest maximum values when compared to the other configurations. The results indicate that the choice of the return electrode position relative to the target can influence the distribution and the range of values of the E-field magnitude in the SC. Possible clinical significance of the observed effects will be discussed.

This paper reviews the state-of-the-art in simulation-based studies of Tumor Treating Fields (TTFields) and highlights major aspects of TTFields in which simulation-based studies could affect clinical outcomes. A major challenge is how to simulate multiple scenarios rapidly for TTFields delivery. Overcoming this challenge will enable a better understanding of how TTFields distribution is correlated with disease progression, leading to better transducer array designs and field optimization procedures, ultimately improving patient outcomes.

The use of computational modeling studies accounts currently for the best approach to predict the electric field (E-field) distribution in transcranial direct current stimulation. As with any model, the values attributed to the physical properties, namely the electrical conductivity of the tissues, affect the predicted E-field distribution. A wide range of values for the conductivity of most tissues is reported in the literature. In this work, we used the finite element method to compute the E-field induced in a realistic human head model for two electrode montages targeting the left dorso-lateral prefrontal cortex (DLPFC). A systematic analysis of the effect of different isotropic conductivity profiles on the E-field distribution was performed for the standard bipolar 7×5 cm2 electrodes configuration and also for an optimized multielectrode montage. Average values of the E-field's magnitude, normal and tangential components were calculated in the target region in the left DLPFC. Results show that the field decreases with increasing scalp, cerebrospinal fluid (CSF) and grey matter (GM) conductivities, while the opposite is observed for the skull and white matter conductivities. The tissues whose conductivity most affects the E-field in the cortex are the scalp and the CSF, followed by the GM and the skull. Uncertainties in the conductivity of individual tissues may affect electric field values by up to about 80%.

Tumor Treating Fields (TTFields) are low intensity alternating electric fields in the 100-500 KHz frequency range that are known to have an anti-mitotic effect on cancerous cells. In the USA, TTFields are approved by the Food and Drug Administration (FDA) for the treatment of glioblastoma (GBM) in both the newly diagnosed and recurrent settings. Optimizing treatment with TTFields requires a deep understanding of how TTFields distribute within the brain. To address this issue, simulations using realistic head models have been performed. However, the preparation of such models is time-consuming and requires a high level of expertise, limiting the usefulness of these models for systematic studies in which the testing of multiple cases is required. Here we present a platform for rapidly simulating TTFields distributions in multiple scenarios. This platform enables high throughput computational simulations to be performed, allowing comparison of field distributions within the head in multiple clinically relevant scenarios. The simulation setup is simple and intuitive, allowing non-expert users to run simulations and evaluate results, thereby providing a valuable tool for studying how to optimize TTFields delivery in the clinic.

It is well established that electric fields can influence cells: Low frequency electric fields (f 1 MHz) cause tissue heating. More recently, it was shown that exposing dividing cells to low intensity (∼1 V/cm) electric fields in the frequency range of 100 - 500 kHz leads to disruption of the mitotic process, and ultimately cell death. This discovery motivated the development of Tumor Treating Fields (TTFields) therapy: a non-invasive treatment that utilizes electric fields in the intermediate frequency range to treat solid tumors. TTFields have been approved by the US Food and Drug Administration for the treatment of recurrent glioblastoma multiforme since 2011.It is thought that TTFields exert their effect on cells by inhibiting tubulin polymerization through interactions of the electric fields with the highly polarized tubulin dimers. However, only few studies have been performed that quantify the intracellular intensity of electric fields, in the frequency range of 100-500 kHz, and how these fields disrupt tubulin polymerization. Here we present the results of computational simulations and in-vitro studies that address these issues. The simulations show that the frequency range of 100-500 kHz marks a transition region in which the magnitude of the electric field inside cells increases significantly, and that the threshold at which this increase occurs depends on the dielectric properties of the cell's membrane and cytoplasm. The experiments show that exposure of cell cultures to TTFields leads to a 10-20% increase in the amount of unpolymerized tubulin dimers within the cells, suggesting that TTFields influence tubulin polymerization dynamics. These studies provide new insight into the biophysical mechanisms that underlie TTFields therapy.

International audience; Glioblastoma multiforme is a malignant brain tumor with poor prognosis and high morbidity due to its invasiveness. Hypoxia-driven motility and concentration-driven motility are two mechanisms of glioblastoma multiforme invasion in the brain. The use of anti-angiogenic drugs has uncovered new progression patterns of glioblastoma multiforme associated with significant differences in overall survival. Here, we apply a mathematical model of glioblas- toma multiforme growth and invasion in humans and design computational trials using agents that target angiogenesis, tumor replication rates, or motility. The findings link highly- dispersive, moderately-dispersive, and hypoxia-driven tumors to the patterns observed in glioblastoma multiforme treated by anti-angiogenesis, consisting of progression by Expand- ing FLAIR, Expanding FLAIR + Necrosis, and Expanding Necrosis, respectively. Further- more, replication rate-reducing strategies (e.g. Tumor Treating Fields) appear to be effective in highly-dispersive and moderately-dispersive tumors but not in hypoxia-driven tumors. The latter may respond to motility-reducing agents. In a population computational trial, with all three phenotypes, a correlation was observed between the efficacy of the rate- reducing agent and the prolongation of overall survival times. This research highlights the potential applications of computational trials and supports new hypotheses on glioblastoma multiforme phenotypes and treatment options.

Effects of electric fields on biological cells have been extensively studied but primarily in the low and high frequency regimes. Low frequency AC fields have been investigated for applications to nerve and muscle stimulation or to examine possible environmental effects of 60 Hz excitation. High frequency fields have been studied to understand tissue heating and tumor ablation. Biological effects at intermediate frequencies (in the 100-500 kHz regime) have only recently been discovered and are now being used clinically to disrupt cell division, primarily for the treatment of recurrent glioblastoma multiforme. In this study, we develop a computational framework to investigate the mechanisms of action of these Tumor Treating Fields (TTFields) and to understand in vitro findings observed in cell culture. Using Finite Element Method models of isolated cells we show that the intermediate frequency range is unique because it constitutes a transition region in which the intracellular electric field, shielded at low frequencies, increases significantly. We also show that the threshold at which this increase occurs depends on the dielectric properties of the cell membrane. Furthermore, our models of different stages of the cell cycle and of the morphological changes associated with cytokinesis show that peak dielectrophoretic forces develop within dividing cells exposed to TTFields. These findings are in agreement with in vitro observations, and enhance our understanding of how TTFields disrupt cellular function.

The effects of applied electric fields on biological cells have been a frequently discussed topic within the last decades. Analytical descriptions and theoretical investigations lead to numerous cell characterization and manipulation techniques which were translated into clinical applications. Low-frequency alternating current (AC) fields are employed for nerve and muscle stimulation, while high-frequency fields find medical relevance in tissue heating or tumor ablation. The biological effects of intermediate frequency fields in the kHz region have just recently being discovered. One evolving technique is a cancer treatment modality called Tumor Treating Fields (TTFields) that makes use of the characteristic antimitotic effect of low-intensity (1 to 3 V/cm) and intermediate-frequency (100 to 300 kHz) AC fields. Computational modeling can be used to predict the electric field distribution in and around the quiescent and dividing cell due to the application of an AC field with a frequency in this intermediate region. Our results confirmed several predicted outcomes of TTField application. During metaphase a nonzero intracellular field strength Ei is induced in the cell. The frequency above which Ei approaches the extracellular field strength depends on the cell’s dielectric properties. During cytokinesis a non-uniform Ei is induced, with increased strength at the furrow. Frequency, cell size, and cell shape dependencies were confirmed. Future insights into the biophysical effects of alternating electric fields may be gained through computational modeling at the subcellular level or at the level of cell assemblies.

Tumor Treating Fields (TTFields) is a novel antimitotic cancer treatment modality that works with specialized low-intensity (1–3 V/cm) and intermediate frequency (∼200 kHz) electric fields. For glioblastoma patients, TTFields is delivered noninvasively through two orthogonal pairs of transducer arrays placed on the shaved scalp. Tumor cells are disrupted during mitosis, with the specific frequency tuned for distinct cancer cell lines, while quiescent cells are not affected. This chapter covers a short introduction to the basics of electric fields including a description of the mechanisms of action of TTFields. Preclinical findings are outlined and the clinical application of TTFields discussed. The induced electric field distribution within the cranium is explained and illustrated with computational modeling. The last part of the chapter will comment on neuroimaging in patients treated with TTFields including a discussion of response criteria, pseudoprogression, and other treatment-related changes in patients.