Evidence map›Paper›PMID 29335429›Full record

ArticleScientific reports2018

RFA Guardian: Comprehensive Simulation of Radiofrequency Ablation Treatment of Liver Tumors.

Philip Voglreiter, Panchatcharam Mariappan, Mika Pollari, Ronan Flanagan, Roberto Blanco Sequeiros, Rupert Horst Portugaller, Jurgen Fütterer, Dieter Schmalstieg, Marina Kolesnik, Michael Moche

Abstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Trial
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  9. Article
  10. Article
  11. Article
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  13. Software-based planning of ultrasound and CT-guided percutaneous radiofrequency ablation in hepatic tumors.International journal of computer assisted radiology and surgery · 2021
    Article
  14. Review
  15. Article
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

10 authors.

Philip VoglreiterGraz University of Technology, Faculty of Computer Science and Biomedical Engineering, Graz, 8010, Austria. voglreiter@icg.tugraz.at.
Panchatcharam MariappanNUMA Engineering Services, Dundalk, 21222, Ireland.
Mika PollariAalto University School of Science and Technology, Department of Computer Science, Espoo, 02150, Finland.
Ronan FlanaganNUMA Engineering Services, Dundalk, 21222, Ireland.
Roberto Blanco SequeirosTurku University Hospital, Medical Imaging Centre of Southwest Finland, Turku, 20521, Finland.
Rupert Horst PortugallerMedical University of Graz, Division of Neuroradiology, Vascular and Interventional Radiology, Graz, 8010, Austria.
Jurgen FüttererRadboud University Nijmegen, Radboud University Medical Centre, Nijmegen, 6525, Netherlands.
Dieter SchmalstiegGraz University of Technology, Faculty of Computer Science and Biomedical Engineering, Graz, 8010, Austria.
Marina KolesnikFraunhofer Gesellschaft, Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, 53754, Germany.
Michael MocheUniversity Hospital Leipzig, Clinic for Diagnostic and Interventional Radiology, Leipzig, 04109, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The RFA Guardian is a comprehensive application for high-performance patient-specific simulation of radiofrequency ablation of liver tumors. We address a wide range of usage scenarios. These include pre-interventional planning, sampling of the parameter space for uncertainty estimation, treatment evaluation and, in the worst case, failure analysis. The RFA Guardian is the first of its kind that exhibits sufficient performance for simulating treatment outcomes during the intervention. We achieve this by combining a large number of high-performance image processing, biomechanical simulation and visualization techniques into a generalized technical workflow. Further, we wrap the feature set into a single, integrated application, which exploits all available resources of standard consumer hardware, including massively parallel computing on graphics processing units. This allows us to predict or reproduce treatment outcomes on a single personal computer with high computational performance and high accuracy. The resulting low demand for infrastructure enables easy and cost-efficient integration into the clinical routine. We present a number of evaluation cases from the clinical practice where users performed the whole technical workflow from patient-specific modeling to final validation and highlight the opportunities arising from our fast, accurate prediction techniques.

Indexed as

Catheter AblationHumansImage Processing, Computer-AssistedLiver NeoplasmsSurgery, Computer-AssistedTomography, X-Ray ComputedTreatment Outcome

Identifiers

PMID29335429
PMCPMC5768804

What OpenQuestion holds

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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.