Evidence map›Paper›PMID 28871138›Full record

ArticleScientific reports2017

Predicting irreversible electroporation-induced tissue damage by means of magnetic resonance electrical impedance tomography.

Matej Kranjc, Simona Kranjc, Franci Bajd, Gregor Serša, Igor Serša, Damijan Miklavčič

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

14 citing papers in PubMed.

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  8. Review
  9. Normal and fibrotic liver parenchyma respond differently to irreversible electroporation.HPB : the official journal of the International Hepato Pancreato Biliary Association · 2019
    Article
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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

6 authors.

Matej KranjcUniversity of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000, Ljubljana, Slovenia.
Simona KranjcDepartment of Experimental Oncology, Institute of Oncology Ljubljana, Zaloška 2, 1000, Ljubljana, Slovenia.
Franci BajdJozef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia.
Gregor SeršaDepartment of Experimental Oncology, Institute of Oncology Ljubljana, Zaloška 2, 1000, Ljubljana, Slovenia.
Igor SeršaJozef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia.
Damijan MiklavčičUniversity of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000, Ljubljana, Slovenia. damijan.miklavcic@fe.uni-lj.si.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Irreversible electroporation (IRE) is gaining importance in routine clinical practice for nonthermal ablation of solid tumors. For its success, it is extremely important that the coverage and exposure time of the treated tumor to the electric field is within the specified range. Measurement of electric field distribution during the electroporation treatment can be achieved using magnetic resonance electrical impedance tomography (MREIT). Here, we show improved MREIT-enabled electroporation monitoring of IRE-treated tumors by predicting IRE-ablated tumor areas during IRE of mouse tumors in vivo. The in situ prediction is enabled by coupling MREIT with a corresponding Peleg-Fermi mathematical model to obtain more informative monitoring of IRE tissue ablation by providing cell death probability in the IRE-treated tumors. This technique can potentially be used in electroporation-based clinical applications, such as IRE tissue ablation and electrochemotherapy, to improve and assure the desired treatment outcome.

Indexed as

Electric ImpedanceElectroporationMagnetic Resonance SpectroscopyModels, TheoreticalTomographyAlgorithmsAnimalsDisease Models, AnimalFemaleMiceWounds and Injuries

Identifiers

PMID28871138
PMCPMC5583379

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Registered trials

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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.