Evidence map›Paper›PMID 31329275›Full record

ArticleEpilepsia2019

Stereoelectroencephalography electrode placement: Detection of blood vessel conflicts.

Kuo Li, Vejay N Vakharia, Rachel Sparks, Roman Rodionov, Sjoerd B Vos, Andrew W McEvoy, Anna Miserocchi, Maode Wang, Sebastien Ourselin, John S Duncan

Abstract read
In one paragraph

Article in Epilepsia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Patient-specific prediction of SEEG electrode bending for stereotactic neurosurgical planning.International journal of computer assisted radiology and surgery · 2021
    Article
  6. Automation Advances in Stereoelectroencephalography Planning.Neurosurgery clinics of North America · 2020
    Review
  7. Review
  8. Review
  9. 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.

Kuo LiDepartment of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.ORCID 0000-0003-4571-4771
Vejay N VakhariaDepartment of Clinical and Experimental Epilepsy, University College London, London, UK.ORCID 0000-0002-9476-4225
Rachel SparksSchool of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, King's College London, London, UK.
Roman RodionovDepartment of Clinical and Experimental Epilepsy, University College London, London, UK.
Sjoerd B VosDepartment of Clinical and Experimental Epilepsy, University College London, London, UK.
Andrew W McEvoyNational Hospital for Neurology and Neurosurgery, Queen Square, London, UK.
Anna MiserocchiNational Hospital for Neurology and Neurosurgery, Queen Square, London, UK.
Maode WangDepartment of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Sebastien OurselinSchool of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, King's College London, London, UK.
John S DuncanDepartment of Clinical and Experimental Epilepsy, University College London, London, UK.ORCID 0000-0002-1373-0681

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveVarious forms of vascular imaging are performed to identify vessels that should be avoided during stereoelectroencephalography (SEEG) planning. Digital subtraction angiography (DSA) is the gold standard for intracranial vascular imaging. DSA is an invasive investigation, and a balance is necessary to identify all clinically relevant vessels and not to visualize irrelevant vessels that may unnecessarily restrict electrode placement. We sought to estimate the size of vessels that are clinically significant for SEEG planning.

methodsThirty-three consecutive patients who underwent 354 SEEG electrode implantations planned with computer-assisted planning and DSA segmentation between 2016 and 2018 were identified from a prospectively maintained database. Intracranial positions of electrodes were segmented from postimplantation computed tomography scans. Each electrode was manually reviewed using "probe-eye view" with the raw preoperative DSA images for vascular conflicts. The diameter of vessels and the location of conflicts were noted. Vessel conflicts identified on raw DSA images were cross-referenced against other modalities to determine whether the conflict could have been detected.

resultsOne hundred sixty-six vessel conflicts were identified between electrodes and DSA-identified vessels, with 0-3 conflicts per electrode and a median of four conflicts per patient. The median diameter of conflicting vessels was 1.3 mm (interquartile range [IQR] = 1.0-1.5 mm). The median depth of conflict was 31.0 mm (IQR = 14.3-45.0 mm) from the cortical surface. The addition of sulcal models to DSA, magnetic resonance venography (MRV), and T1 + gadolinium images, as an exclusion zone during computer-assisted planning, would have prevented the majority of vessel conflicts. We were unable to determine whether vessels were displaced or transected by the electrodes. SIGNIFICANCE: Vascular segmentation from DSA images was significantly more sensitive than T1 + gadolinium or MRV images. Electrode conflicts with vessels 1-1.5 mm in size did not result in a radiologically detectable or clinically significant hemorrhage and could potentially be excluded from consideration during SEEG planning.

Indexed as

Electrodes, ImplantedAngiography, Digital SubtractionBlood VesselsBrainCerebral AngiographyElectroencephalographyFemaleHumansMaleNeurosurgical Procedurescomputer-assisted planningsegmentationstereoelectroencephalographyvascular imaging

Identifiers

PMID31329275
PMCPMC6851756

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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.