scholarly journals NIMG-65. STUDY OF LOCAL PERTURBATION IN COMPUTATIONAL MODELLING ON TUMOR TREATING FIELDS (TTFIELDS) THERAPY

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii162-ii162
Author(s):  
Oshrit Zeevi ◽  
Zeev Bomzon ◽  
Tal Marciano
Keyword(s):  
Electric Field ◽  
Treatment Planning ◽  
Computational Models ◽  
Computational Modelling ◽  
Clinical Trial Data ◽  
Patient Specific ◽  
Local Perturbation ◽  
Tumor Bed ◽  
Tumor Treating Fields ◽  
Local Perturbations

Abstract INTRODUCTION Tumor Treating Fields (TTFields) are an approved therapy for glioblastoma (GBM). A recent study combining post-hoc analysis of clinical trial data and extensive computational modelling demonstrated that TTFields dose at the tumor has a direct impact on patient survival (Ballo MT, et al. Int J Radiat Oncol Biol Phys, 2019). Hence, there is rationale for developing TTFields treatment planning tools that rely on numerical simulations and patient-specific computational models. To assist in the development of such tools is it important to understand how inaccuracies in the computational models influence the estimation of the TTFields dose delivered to the tumor bed. Here we analyze the effect of local perturbations in patient-specific head models on TTFields dose at the tumor bed. METHODS Finite element models of human heads with tumor were created. To create defects in the models, conductive spheres with varying conductivities and radii were placed into the model’s brains at different distances from the tumor. Virtual transducer arrays were placed on the models, and delivery of TTFields numerically simulated. The error in the electric field induced by the defects as a function of defect conductivity, radius, and distance to tumor was investigated. RESULTS Simulations showed that when a defect of radius R is placed at a distance, d >7R, the error is below 1% regardless of the defect conductivity. Further the defects induced errors in the electric field that were below 1% when σrR/d < 0.16, where σrR/d < 0.16, where σr = (σsphere – σsurrounding)/(σsphere + σsurrounding).σsurroundings is the average conductivity around the sphere and σsphere is the conductivity of the sphere. CONCLUSIONS This study demonstrates the limited impact of local perturbations in the model on the calculated field distribution. These results could be used as guidelines on required model accuracy for TTFields treatment planning.

RBIO-01. DEVELOPING THE FRAMEWORK FOR TUMOR TREATING FIELDS (TTFIELDS) TREATMENT PLANNING FOR A PATIENT WITH ASTROCYTOMA IN THE SPINAL CORD

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi191-vi191
Author(s):  
Jennifer de Los Santos ◽  
Smadar Arvatz ◽  
Oshrit Zeevi ◽  
Shay Levi ◽  
Noa Urman ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Treatment Planning ◽  
Spinal Tumor ◽  
Specific Model ◽  
Patient Specific ◽  
Transducer Array ◽  
Tumor Bed ◽  
Tumor Treating Fields ◽  
Transducer Arrays ◽  
Healthy Female

Abstract The use of Tumor Treating Fields (TTFields) following resection and chemoradiation has increased survival in patients with Glioblastoma. Patient-specific planning for TTFields transducer array placement has been demonstrated to maximize TTFields dose at the tumor: providing higher TTFields intensity (≥ 1.0 V/cm) and power density (≥ 1.1 mW/cm3) which are associated with improved overall survival. Treatment planning was performed for a 48 year old patient following T10-L1 laminectomy, gross total resection, and postoperative chemoradiation for an anaplastic astrocytoma of the spinal cord. An MRI at 3 weeks following chemoradiation showed tumor recurrence. Based on the post-chemoradiation MRI, a patient-specific model was created. The model was created by modifying a realistic computational phantom of a healthy female. To mimic the laminectomy, the lamina in T10-L1 was removed, and the region assigned electric conductivity similar to that of muscle. A virtual mass was introduced into the spinal cord. Virtual transducer arrays were placed on the model at multiple positions, and delivery of TTFields simulated. The dose delivered by different transducer array layouts was calculated, and the layouts that yielded maximal dose to the tumor and spine identified. Transducer array layouts, in which the arrays were placed on the back of the patient with one array above the tumor and one array below the tumor, yielded the highest doses at the tumor site. Such layouts yielded TTFields doses of over 3.4mW/cm3 which is well above the threshold dose of 1.1 mW/cm3 reported previously [Ballo et al. Red Jour 2019]. The framework developed for TTFields dosimetry and treatment planning for this spinal tumor will have the potential to increase dose delivery to the tumor bed while optimizing placement that may enhance comfort and encourage device usage.

scholarly journals NIMG-06. DOES PERSONALIZING TUMOR TREATING FIELDS(TTFIELDS) WITH NOVOTAL IN GLIOBLASTOMA TREATMENT PLANNING MAKE A DIFFERENCE? INSIGHTS FROM ELECTRIC FIELD SIMULATION STUDIES

2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi124-vi124
Author(s):  
Aafia Chaudhry ◽  
Ze’ev Bomzon ◽  
Hadas Sara Hershkovich ◽  
Dario Garcia-Carracedo ◽  
Cornelia Wenger ◽  
...  
Keyword(s):  
Electric Field ◽  
Treatment Planning ◽  
Simulation Studies ◽  
Tumor Treating Fields ◽  
Field Simulation

Calculation of Carotid Artery Flow Rates Using Doppler Ultrasound: Implications of Velocity Profile Skewing

2012 ◽  
Author(s):  
Jonathan P. Mynard ◽  
David A. Steinman
Keyword(s):  
Carotid Artery ◽  
Velocity Profile ◽  
Doppler Ultrasound ◽  
Computational Models ◽  
Peak Velocity ◽  
Computational Modelling ◽  
Carotid Bifurcation ◽  
Sample Volume ◽  
Patient Specific ◽  
Specific Flow

Doppler ultrasound (DUS) is a non-invasive means of obtaining patient-specific flow boundary conditions in computational modelling studies [1] or estimating volumetric flow in clinical studies [2, 3]. To convert velocity information to a flow waveform, three related assumptions are often applied, 1) that the peak velocity lies in the centre of a cylindrical vessel, 2) that a centrally-located sample volume will thus detect the peak velocity, and 3) that the velocity profile is fully-developed and axisymmetric, being well-approximated by a parabolic (Poiseuille) or Womersley profile. These assumptions may not always be valid, however, even for nominally straight vessels like the common carotid artery (CCA) [4, 5]. While one might expect that flow estimated from DUS would become increasingly inaccurate as the profile becomes less axisymmetric, the scale of such errors and their relation to the true profile shape have not been quantified for the CCA. Moreover, for a heavily skewed velocity profile, the peak velocity may not lie within the DUS sample volume, and hence the choice of sample volume or beam-vessel orientation may also affect the accuracy of flow calculations. In this study, we investigate these issues by performing an idealized virtual DUS on data from image-based computational models of the carotid bifurcation.

scholarly journals Tumor-Treating Fields at EMBC 2019: A Roadmap to Developing a Framework for TTFields Dosimetry and Treatment Planning

2020 ◽  
pp. 3-17
Author(s):  
Ze’ev Bomzon ◽  
Cornelia Wenger ◽  
Martin Proescholdt ◽  
Suyash Mohan
Keyword(s):  
Patient Outcome ◽  
Treatment Planning ◽  
Electric Fields ◽  
Dose Distribution ◽  
Pleural Mesothelioma ◽  
Adaptive Planning ◽  
Tumor Bed ◽  
Short Overview ◽  
Tumor Treating Fields ◽  
Rigorous Treatment

AbstractTumor 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.

scholarly journals INNV-05. TUMOR TREATING FIELDS (TTFIELDS) TREATMENT PLANNING FOR A PATIENT WITH ASTROCYTOMA IN THE SPINAL CORD

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii117-ii117
Author(s):  
Jennifer De Los Santos ◽  
Smadar Arvatz ◽  
Oshrit Zeevi ◽  
Shay levi ◽  
Zeev Bomzon ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Numerical Simulations ◽  
Treatment Planning ◽  
Spinal Tumor ◽  
Specific Model ◽  
Patient Specific ◽  
Transducer Array ◽  
Tumor Treating Fields ◽  
Healthy Female ◽  
Specific Manner

Abstract BACKGROUND The use of Tumor Treating Fields (TTFields) following resection and chemoradiation has increased survival in patients with Glioblastoma. Randomized data provide strong rationale for planning TTFields transducer array placement to maximize TTFields dose at the tumor in a patient-specific manner. Here we present a case demonstrating the use of numerical simulations for patient-specific TTFields treatment planning for a spinal tumor. METHODS Treatment planning was performed for a 48 year old patient following T10-L1 laminectomy, gross total resection, and postoperative chemoradiation for an anaplastic astrocytoma of the spinal cord. An MRI at 3 weeks following chemoradiation showed tumor recurrence. Based on the post-chemoradiation MRI, a patient-specific model was created. The model was created by modifying a realistic computational phantom of a healthy female. To mimic the laminectomy, the lamina in T10-L1 was removed, and the region assigned electric conductivity similar to that of muscle. A virtual mass was introduced into the spinal cord. Virtual transducer arrays were placed on the model at multiple positions, and delivery of TTFields simulated. The dose delivered by different transducer array layouts was calculated, and the layouts that yielded maximal dose to the tumor and spine identified. RESULTS Transducer array layouts, in which the arrays were placed on the back of the patient with one array above the tumor and one array below the tumor, yielded the highest doses at the tumor site. Such layouts yielded TTFields doses of over 3.4mW/cm3 which is well above the threshold dose of 1.1 mW/cm3 reported previously [Ballo et al Red Jour 2019]. CONCLUSIONS These data represent the first ever study on utilizing numerical simulations in order to plan treatment for a spinal tumor in a patient-specific manner. This is an important milestone in the developing a framework for TTFields dosimetry and treatment planning.

scholarly journals Evaluation of a modified Cheatham-Platinum stent for the treatment of aortic coarctation by finite element modelling

2018 ◽  
Vol 7 ◽  
pp. 204800401877395 ◽  
Author(s):  
Barbara EU Burkhardt ◽  
Nicholas Byrne ◽  
Marí Nieves Velasco Forte ◽  
Francesco Iannaccone ◽  
Matthieu De Beule ◽  
...  
Keyword(s):  
Finite Element ◽  
Aortic Coarctation ◽  
Computational Models ◽  
Three Dimensional ◽  
Computational Modelling ◽  
Patient Specific ◽  
Stent Design ◽  
Initial Length ◽  
Element Analysis

Objectives Stent implantation for the treatment of aortic coarctation has become a standard approach for the management of older children and adults. Criteria for optimal stent design and construction remain undefined. This study used computational modelling to compare the performance of two generations of the Cheatham-Platinum stent (NuMED, Hopkinton, NY, USA) deployed in aortic coarctation using finite element analysis. Design Three-dimensional models of both stents, reverse engineered from microCT scans, were implanted in the aortic model of one representative patient. They were virtually expanded in the vessel with a 16 mm balloon and a pressure of 2 atm. Results The conventional stent foreshortened to 96.5% of its initial length, whereas the new stent to 99.2% of its initial length. Diameters in 15 slices across the conventional stent were 11.6–15 mm (median 14.2 mm) and slightly higher across the new stent: 10.7–15.3 mm (median 14.5 mm) (p= 0.021). Apposition to the vessel wall was similar: conventional stent 31.1% and new stent 28.6% of total stent area. Conclusions The new design Cheatham-Platinum stent showed similar deployment results compared to the conventional design. The new stent design showed slightly higher expansion, using the same delivery balloon. Patient-specific computational models can be used for virtual implantation of new aortic stents and promise to inform subsequent in vivo trials.

A general approach to optimizing tumor treating fields therapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14668-e14668
Author(s):  
Zeev Bomzon ◽  
Noa Urman ◽  
Hadas Sara Hershkovich ◽  
Eilon David Kirson ◽  
Ariel Naveh ◽  
...  
Keyword(s):  
Field Distribution ◽  
Electric Fields ◽  
Computational Models ◽  
Array Size ◽  
Patient Comfort ◽  
Improve Patient Survival ◽  
Tumor Bed ◽  
Tumor Treating Fields ◽  
Alternating Electric Fields ◽  
Improve Patient

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.

scholarly journals P14.69 Evaluation of electric field intensity delivered by Tumor-Treating Fields therapy to PET-defined metabolic volumes in recurrent glioblastomas

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii83-iii84
Author(s):  
S Mittal ◽  
F John ◽  
A Naveh ◽  
Z Bomzon ◽  
G R Barger ◽  
...  
Keyword(s):  
Amino Acid ◽  
Electric Field ◽  
Tumor Volume ◽  
Metabolic Tumor Volume ◽  
Patient Specific ◽  
Factors Affecting ◽  
Tumor Mass ◽  
Tumor Treating Fields ◽  
Treatment Responses

Abstract BACKGROUND Tumor-Treating Fields (TTFields) therapy is a clinical treatment option for patients with newly-diagnosed and recurrent glioblastomas. Electric field intensities (EFIs) delivered to the tumor mass may affect treatment responses. In this study, we used the patients’ neuroimaging data to create realistic head models and evaluate: (i) the magnitude of EFIs delivered to the tumor mass; (ii) factors affecting the EFI values; and (iii) factors affecting treatment responses as assessed by amino acid PET. MATERIAL AND METHODS Fourteen recurrent glioblastomas in 9 patients were evaluated with α-[11C]-methyl-L-tryptophan (AMT)-PET before and up to 3 months after TTFields therapy (mean follow-up: 2.3 months). Individual MRI and CT scans were used to create patient-specific realistic head models and simulate TTFields delivery to the tumors. For each direction of treatment (antero-posterior, left-right), two 9-disk transducer arrays were simulated using disks placed according to the patients’ NovoTAL System™ based treatment plan. To generate TTFields, an alternating voltage difference (200V peak-to-peak, 200 kHz) was imposed on the outer surfaces of the disks. The simulations were performed using the Sim4Life V3.0 (ZMT-Zurich) quasi-electrostatic solver. The field intensities were normalized to simulate 2A peak-to-peak current supplied by the device. 3D EFI maps were created and fused with the pre- and post-TTFields PET images to measure EFIs delivered to the PET-defined metabolic tumor volume. Interval changes of static AMT uptake and kinetic PET variables were also evaluated. RESULTS The mean EFI delivered to the tumors varied between 1.34–2.43 V/cm (mean: 1.86 V/cm). Fronto-parietal tumors received higher mean EFI than temporal lobe tumors (p=0.05). Most tumors showed decreasing (n=9) or stable (n=4) AMT uptake on follow-up PET imaging after TTFields therapy. Higher EFIs delivered to the tumors (r=-0.56, p=0.04) and concomitant bevacizumab treatment (n=7, p=0.01) were associated with a greater PET response. On tracer kinetic analysis, the AMT uptake responses correlated with transport rate changes (p=0.04). CONCLUSION TTFields treatment of recurrent glioblastomas delivers variable EFIs to the metabolic tumor volume. Treatment responses on PET are driven by decreased amino acid transport rates, whose magnitude is associated with higher EFIs delivered to the tumor mass and also with concomitant antiangiogenic treatment in those with combined therapy. (The cost of the PET scans was supported by a grant from NovoCure Ltd., Haifa, Israel)

scholarly journals P.160 Impact of peritumoral edema during tumor treatment field therapy: a computational modelling study

2021 ◽  
Vol 48 (s3) ◽  
pp. S65-S66
Author(s):  
S Lang ◽  
L Gan ◽  
C McLennan ◽  
O Monchi ◽  
J Kelly
Keyword(s):  
Electric Field ◽  
Field Distribution ◽  
Electric Field Distribution ◽  
Computational Modelling ◽  
Electrical Current ◽  
Patient Specific ◽  
Peritumoral Edema ◽  
Tumor Treatment ◽  
Electric Field Magnitude ◽  
Field Magnitude

Background: Tumor treatment fields (TTFields) are an approved adjuvant therapy for glioblastoma. The magnitude of applied electrical field is related to the anti-tumoral response. However, peritumoral edema (ptE) may result in shunting of electrical current around the tumor, thereby reducing the intra-tumoral electric field. In this study, we address this issue with computational simulations. Methods: Finite element models were created with varying amounts of ptE surrounding a virtual tumor. The electric field distribution was simulated using the standard TTFields electrode montage. Electric field magnitude was extracted from the tumor and related to edema thickness. Two patient specific models were created to confirm these results. Results: The inclusion of ptE decreased the magnitude of the electric field within the tumor. In the model considering a frontal tumor and an anterior-posterior electrode configuration, ≥ 6 mm of ptE decreased the electric field by 52%. In the patient specific models, ptE decreased the electric field within the tumor by an average of 26%. The effect of ptE on the electric field distribution was spatially heterogenous. Conclusions: Given the importance of electric field magnitude for the anti-tumoral effects of TTFields, the presence of edema should be considered both in future modelling studies and as a predictor of non-response.

scholarly journals Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis

2021 ◽  
Author(s):  
Fedor Shmarov ◽  
Graham R Smith ◽  
Sophie C Weatherhead ◽  
Nick J Reynolds ◽  
Paolo Zuliani
Keyword(s):  
Clinical Response ◽  
Computational Models ◽  
Disease Onset ◽  
Computational Modelling ◽  
Patient Specific ◽  
Prospective Clinical Study ◽  
Healthy Skin ◽  
Early Assessment ◽  
Psoriasis Area Severity Index ◽  
Sensitivity Parameter

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis that features two stable steady states: healthy skin and psoriasis. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis induced by UVB phototherapy returned the epidermis back to the healthy state. The flexibility of our model permitted the development of a patient-specific "UVB sensitivity" parameter that enabled accurate simulation of individual patients' clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the "UVB sensitivity" parameter and the prediction of patient outcome at the end of phototherapy. Notably, our model was able to distinguish disease flares and offers the potential for clinical application in early assessment of response to UVB therapy outcome, and for individualised optimisation of phototherapy regimes to improve clinical outcome.

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