Numerical modelling challenges for clinical electroporation ablation technique of liver tumors

Gallinato, Olivier; Séror, Olivier; Poignard, Clair

doi:10.1051/mmnp/2019037

Cited by 10 publications

(12 citation statements)

References 50 publications

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“…Although this is an improvement over the educational tool [23], it does not evaluate the effect of the presence of tissues with different conductivities (e.g. tumor, healthy organ, vessels) that is known to be the cause of failure in some IRE treatments [34]. However, the goal of EView is to provide a good initial estimate of the electric field distribution, and multiple tissue scenarios, which require of accurate image segmentation, are out of the current scope.…”

Section: Resultsmentioning

confidence: 99%

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

Perera-Bel

Yagüe

Mercadal

et al. 2020

Computer Methods and Programs in Biomedicine

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Section: Resultsmentioning

confidence: 99%

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

Perera-Bel

Yagüe

Mercadal

et al. 2020

Computer Methods and Programs in Biomedicine

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“…This is not in line with the prior parameter studies that mention healthy liver conductivity having more impact than tumor conductivity. 4,9 This disagreement could be due to the difference in the electrode configuration, which is placed around the tumor in those works. Deviation in every parameter apart from r TNI causes at least 4-5 times more change in the RelTA than r TNI for 5.6 and 8-mm tumor sizes.…”

Section: Effect On the Relative Tumor Ablationmentioning

confidence: 91%

“…Similar sensitivities were inferred in a numerical study using a realistic tumor geometry. 9 The sensitivity for the geometric parameters like position or orientation was also pronounced. Recently, a two-way ANOVA test was used to study the effect of pulse parameters,electrode position,and orientation on the ablation volume.…”

Section: Introductionmentioning

confidence: 97%

Global sensitivity study for irreversible electroporation: Towards treatment planning under uncertainty

et al. 2023

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Background: Electroporation-based cancer treatments are minimally invasive, nonthermal interventional techniques that leverage cell permeabilization to ablate the target tumor. However, the amount of permeabilization is susceptible to the numerous uncertainties during treatment, such as patient-specific variations in the tissue, type of the tumor, and the resolution of imaging equipment. These uncertainties can reduce the extent of ablation in the tissue, thereby affecting the effectiveness of the treatment. Purpose: The aim of this work is to understand the effect of these treatment uncertainties on the treatment outcome for irreversible electroporation (IRE) in the case of colorectal liver metastasis (CRLM). Understanding the nature and extent of these effects can help us identify the influential treatment parameters and build better models for predicting the treatment outcome. Methods: This is an in silico study using a static computational model with a custom applicator design, spherical tissue, and tumor geometry. A nonlinear electrical conductivity, dependent on the local electric field, is considered. Morris analysis is used to identify the influential treatment parameters on the treatment outcome. Seven treatment parameters pertaining to the relative tumor location with respect to the applicator, the tumor growth pattern, and the electrical conductivity of tissue are analyzed. The treatment outcome is measured in terms of the relative tumor ablation with respect to the target ablation volume and total ablation volume. Results: The Morris analysis was performed with 800 model evaluations, sampled from the seven dimensional input parameter space. Electrical properties of the tissue, especially the electrical conductivity of the tumor before ablation, were found to be the most influential parameter for relative tumor ablation and total ablation volume. This parameter was found to be about 4-15 times more influential than the least influential parameter, depending on the tumor size. The tumor border configuration was identified as the least important parameter for treatment effectiveness. The most desired treatment outcome is obtained by a combination of high healthy liver conductivity and low tumor conductivity. This information can be used to tackle worst-case scenarios in treatment planning. Finally, when the safety margins used in the clinical applications are accounted for, the effects of uncertainties in the treatment parameters reduce drastically. Conclusions:The results of this work can be used to create an efficient surrogate estimator for uncertainty quantification in the treatmentThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…Indeed one of the most important steps for the computation of the electric field during an IRE procedure lies in the localisation of the percutaneously inserted needles, as previously shown in [2]. The captured information is then used to determine the method (needle placement, etc) and efficacy of IRE for a patient.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed solution consists in completing the missing data by using an analytical needle representation based on the Hough transform [6]. The distribution of the electric field is then numerically computed using the standard static non linear model as presented in [2] directly in the CBCT framework.…”

Section: Introductionmentioning

confidence: 99%

Automated needle localisation for electric field computation during an electroporation ablation

Inacio

Lafitte

Séror

et al. 2022

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)

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The objective of this paper is to provide a stepforward towards the per procedural visualisation of the electric field distribution during a clinical irreversible electroporation (IRE) procedure. To this end, an automated workflow is needed to compute the electric field distribution on a single Cone Beam Computed Tomography (CBCT) scan.The aim of the current paper is to propose a deep learning strategy for the automatic segmentation of the needles. In particular, a novel coarse-to-fine approach is proposed to extract relevant needle information from the CBCT scan, despite inherent artefacts generated during capture. The obtained needle information is subsequently fed into a standard static linear model for the electric field computation. Since the set-up is performed in the medical image framework, the electric field distribution and the region of interest are visible to provide to the radiologist a visual evaluation of the treatment.The segmentation results are evaluated on 8 of the 16 patients of the dataset using the Dice coefficient to compare the predicted segmentation with the ground truth. The proposed segmentation method is fast (around 2 minutes are needed with a commodity hardware), allowing its use in a clinical setting.

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Numerical modelling challenges for clinical electroporation ablation technique of liver tumors

Cited by 10 publications

References 50 publications

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

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

Global sensitivity study for irreversible electroporation: Towards treatment planning under uncertainty

Automated needle localisation for electric field computation during an electroporation ablation

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