Evidence map›Paper›PMID 32385637›Full record

ArticleActa neurochirurgica2020

Development and initial evaluation of a novel simulation model for comprehensive brain tumor surgery training.

Anne Sophie Grosch, Timo Schröder, Torsten Schröder, Julia Onken, Thomas Picht

Open access · hybridAbstract readEvaluation Study
In one paragraph

Article in Acta neurochirurgica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
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

10 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
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  4. Review
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  9. The utilisation of 3D printing in paediatric neurosurgery.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2021
    Review
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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

5 authors at 1 institution in 1 country.

Anne Sophie GroschDepartment of Neurosurgery, Charité, Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Timo SchröderBerliner Simulations & Trainingszentrum (BeST), Berlin Simulation Centre, Charité, Universitätsmedizin Berlin, Berlin, Germany.
Torsten SchröderBerliner Simulations & Trainingszentrum (BeST), Berlin Simulation Centre, Charité, Universitätsmedizin Berlin, Berlin, Germany.
Julia OnkenDepartment of Neurosurgery, Charité, Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
Thomas PichtDepartment of Neurosurgery, Charité, Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany. thomas.picht@charite.de.
Charité - Universitätsmedizin Berlin · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing technico-manual complexity of procedures and time constraints necessitates effective neurosurgical training. For this purpose, both screen- and model-based simulations are under investigation. Approaches including 3D printed brains, gelatin composite models, and virtual environments have already been published. However, quality of brain surgery simulation is limited due to discrepancies in visual and haptic experience. Similarly, virtual training scenarios are still lacking sufficient real-world resemblance. In this study, we introduce a novel simulator for realistic neurosurgical training that combines real brain tissue with 3D printing and augmented reality.

methodsBased on a human CT scan, a skull base and skullcap were 3D printed and equipped with an artificial dura mater. The cerebral hemispheres of a calf's brain were placed in the convexity of the skullcap and tumor masses composed of aspic, water, and fluorescein were injected in the brain. The skullcap and skull base were placed on each other, glued together, and filled up with an aspic water solution for brain fixation. Then, four surgical scenarios were performed in the operating room as follows: (1) simple tumor resection, (2) complex tumor resection, (3) navigated biopsy via burr hole trepanation, and (4) retrosigmoidal craniotomy. Neuronavigation, augmented reality, fluorescence, and ocular-as well as screen-based (exoscopic)-surgery were available for the simulator training. A total of 29 participants performed at least one training scenario of the simulator and completed a 5-item Likert-like questionnaire as well as qualitative interviews. The questionnaire assessed the realism of the tumor model, skull, and brain tissue as well as the capability for training purposes.

resultsVisual and sensory realism of the skull and brain tissue were rated,"very good," while the sensory and visual realism of the tumor model were rated "good." Both overall satisfaction with the model and eligibility of the microscope and neurosurgical instruments for training purposes were rated with "very good." However, small size of the calf's brain, its limited shelf life, and the inability to simulate bleedings due to the lack of perfusion were significant drawbacks.

conclusionThe combination of 3D printing and real brain tissue provided surgical scenarios with very good real-life resemblance. This novel neurosurgical model features a versatile setup for surgical skill training and allows for efficient training of technological support like image and fluorescence guidance, exoscopic surgery, and robotic technology.

Indexed as

Models, AnatomicAdultBrain NeoplasmsCraniotomyHumansNeuronavigationPrinting, Three-DimensionalSimulation TrainingNeurosurgerySimulationSurgical microscopeTrainingTumor modelTumor resection

Identifiers

PMID32385637
PMCPMC7360639
OpenAlexW3023018612

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.