Evidence map›Paper›PMID 31602799›Full record

ArticleJournal of biophotonics2020

Multispectral near infrared absorption imaging for histology of skin cancer.

Alexander Spreinat, Gabriele Selvaggio, Luise Erpenbeck, Sebastian Kruss

Open access · hybridAbstract read
In one paragraph

Article in Journal of biophotonics, 2020. 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
3.7field-weighted citation impact, top 7% 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

9 citing papers in PubMed, 33 citations in OpenAlex.

  1. Genetically encoded sensors illuminateIBRO neuroscience reports · 2025
    Article
  2. High-Speed Hyperspectral Imaging for Near Infrared Fluorescence and Environmental Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    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

4 authors at 2 institutions in 1 country.

Alexander SpreinatInstitute of Physical Chemistry, Göttingen University, Göttingen, Germany.
Gabriele SelvaggioInstitute of Physical Chemistry, Göttingen University, Göttingen, Germany.
Luise ErpenbeckDepartment of Dermatology, Venereology and Allergology, University Medical Center, Göttingen, Germany.
Sebastian KrussInstitute of Physical Chemistry, Göttingen University, Göttingen, Germany.ORCID 0000-0003-0638-9822
University of Göttingen · DEUniversitätsmedizin Göttingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multispectral imaging combines the spectral resolution of spectroscopy with the spatial resolution of imaging and is therefore very useful for biomedical applications. Currently, histological diagnostics use mainly stainings with standard dyes (eg, hematoxylin + eosin) to identify tumors. This method is not applicable in vivo and provides low amounts of chemical information. Biomolecules absorb near infrared light (NIR, 800-1700 nm) at different wavelengths, which could be used to fingerprint tissue. Here, we built a NIR multispectral absorption imaging setup to study skin tissue samples. NIR light (900-1500 nm) was used for homogenous wide-field transmission illumination and detected by a cooled InGaAs camera. In this setup, images I(x, y, λ) from dermatological samples (melanoma, nodular basal-cell carcinoma, squamous-cell carcinoma) were acquired to distinguish healthy from diseased tissue regions. In summary, we show the potential of multispectral NIR imaging for cancer diagnostics.

Indexed as

MelanomaSkin NeoplasmsHistological TechniquesHumansInfrared RaysSpectroscopy, Near-Infrareddiagnosticshistologymultispectral imagingnear infrared spectroscopyskin cancer

Identifiers

PMID31602799
PMCPMC7065629
OpenAlexW2980248018

What OpenQuestion holds

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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.