Evidence map›Paper›PMID 39781046›Full record

ArticleBiomedical engineering letters2025

Computer modelling and vegetable bench test of a bipolar electrode array intended for use in high frequency irreversible electroporation treatment of skin cancer.

Ana González-Suárez, Cian Kerrigan, Jason McNamara, Seán Kinsella, Maeve Duffy

Abstract read
In one paragraph

Article in Biomedical engineering letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ana González-SuárezTranslational Medical Device Lab, School of Medicine, University of Galway, Galway, Ireland.ORCID 0000-0002-1813-4176
Cian KerriganMirai Medical, 5 Howley Court, Oranmore, Galway, Ireland.
Jason McNamaraMirai Medical, 5 Howley Court, Oranmore, Galway, Ireland.
Seán KinsellaMirai Medical, 5 Howley Court, Oranmore, Galway, Ireland.
Maeve DuffyPower Electronics Research Centre, School of Engineering, University of Galway, Galway, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Pulsed electrical field (PEF) ablation is an energy-based technique used to treat a range of cancers by irreversible electroporation (IRE). Our objective was to use computational and plant-based models to characterize the electric field distribution and ablation zones induced with a commercial 8-needle array-based applicator intended for treatment of skin cancer when high-frequency IRE (H-FIRE) pulses are applied. Electric field characterisation of this device was not previously assessed. Methods: Vegetable experimental were conducted using parallel plate electrodes to obtain the lethal threshold for H-FIRE pulses. Then a 3D computational model of the applicator was built mimicking the experimental conditions. The computational ablation zones were validated with the experiments for different voltage levels ranging from 220 to 525 V. Results: A threshold of 453 V/cm was estimated for H-FIRE pulses, which was used to predict computationally the ablation zones. It was found that the model showed a low prediction error, ranging from 2% for the minor diameter to 4.5% for the depth compared with experiments. Voltages higher than 370 V created an ablation volume with a rectangular prism shape determined by the positions of the needles, whereas lower voltages provoked the appearance of untreated areas (gaps). Conclusions: Our computer model predicts reasonably well the ablation zone induced by H-FIRE pulses, suggesting that a sufficiently large voltage must be applied to avoid the appearance of gaps. The validated model with vegetable experiments could serve as the basis for future computer studies to predict the behaviour of this device on heterogeneous tissues.

Indexed as

Computer modellingElectroporationHigh-frequency irreversible electroporationPulsed electric fieldsVegetal model

Identifiers

PMID39781046
PMCPMC11703801

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.