Evidence map›Paper›PMID 37747574›Full record

ArticleInternational journal of computer assisted radiology and surgery2024

Augmented reality for sentinel lymph node biopsy.

Peter A von Niederhäusern, Carlo Seppi, Robin Sandkühler, Guillaume Nicolas, Stephan K Haerle, Philippe C Cattin

Open access · hybridAbstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 24% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
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 at 3 institutions in 1 country.

Peter A von Niederhäusern *Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland. peter.vonniederhaeusern@unibas.ch.ORCID https://orcid.org/0000-0002-3878-5611
Carlo Seppi *Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.ORCID https://orcid.org/0000-0002-5906-3546
Robin SandkühlerDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.ORCID https://orcid.org/0000-0002-1243-0759
Guillaume NicolasNuclear Medicine Clinic, University Hospital Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-0396-5708
Stephan K HaerleHirslanden Clinic Lucerne, Lucerne, Switzerland.
Philippe C CattinDepartment of Biomedical Engineering, University of Basel, Allschwil, Switzerland.ORCID https://orcid.org/0000-0001-8785-2713
University of Basel · CHHirslanden Klinik St. Anna · CHUniversity Hospital of Basel · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSentinel lymph node biopsy for oral and oropharyngeal squamous cell carcinoma is a well-established staging method. One variation is to inject a radioactive tracer near the primary tumor of the patient. After a few minutes, audio feedback from an external hand-held [Formula: see text]-detection probe can monitor the uptake into the lymphatic system. Such probes place a high cognitive load on the surgeon during the biopsy, as they require the simultaneous use of both hands and the skills necessary to correlate the audio signal with the location of tracer accumulation in the lymph nodes. Therefore, an augmented reality (AR) approach to directly visualize and thus discriminate nearby lymph nodes would greatly reduce the surgeons' cognitive load. MATERIALS AND

methodsWe present a proof of concept of an AR approach for sentinel lymph node biopsy by ex vivo experiments. The 3D position of the radioactive [Formula: see text]-sources is reconstructed from a single [Formula: see text]-image, acquired by a stationary table-attached multi-pinhole [Formula: see text]-detector. The position of the sources is then visualized using Microsoft's HoloLens. We further investigate the performance of our SLNF algorithm for a single source, two sources, and two sources with a hot background.

resultsIn our ex vivo experiments, a single [Formula: see text]-source and its AR representation show good correlation with known locations, with a maximum error of 4.47 mm. The SLNF algorithm performs well when only one source is reconstructed, with a maximum error of 7.77 mm. For the more challenging case to reconstruct two sources, the errors vary between 2.23 mm and 75.92 mm.

conclusionThis proof of concept shows promising results in reconstructing and displaying one [Formula: see text]-source. Two simultaneously recorded sources are more challenging and require further algorithmic optimization.

Indexed as

Augmented RealitySentinel Lymph Node BiopsyHumansLymph NodesNeoplasm StagingARAugmented realityGamma cameraGamma detectorInverse problemNuclear medicineSentinel lymph node biopsySNB

Identifiers

PMID37747574
PMCPMC10770201
OpenAlexW4387002073

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.