Evidence map›Paper›PMID 39152181›Full record

ArticleScientific reports2024

Classification of melanocytic lesions using direct illumination multispectral imaging.

Elisabeth Victoria Goessinger, Paul-Gerald Dittrich, Philipp Nöcker, Gunther Notni, Sebastian Weber, Sara Cerminara, Beda Mühleisen, Alexander A Navarini, Lara Valeska Maul

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

9 authors.

Elisabeth Victoria Goessinger *Department of Dermatology, University Hospital of Basel, Basel, Switzerland.
Paul-Gerald Dittrich *Imaging and Sensing Department, Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Strasse 7, 07745, Jena, Germany. paul-gerald.dittrich@iof.fraunhofer.de.
Philipp NöckerImaging and Sensing Department, Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Strasse 7, 07745, Jena, Germany.
Gunther NotniGroup for Quality Assurance and Industrial Image Processing, Department of Mechanical Engineering, Technische Universität Ilmenau, Ilmenau, Germany.
Sebastian WeberFotoFinder Systems GmbH, Bad Birnbach, Germany.
Sara CerminaraDepartment of Dermatology, University Hospital of Basel, Basel, Switzerland.
Beda MühleisenDepartment of Dermatology, University Hospital of Basel, Basel, Switzerland.
Alexander A NavariniDepartment of Dermatology, University Hospital of Basel, Basel, Switzerland.
Lara Valeska MaulDepartment of Dermatology, University Hospital of Basel, Basel, Switzerland. lara.maul-duwendag@usz.ch.

Funding

Bundesministerium für Bildung und Forschung 13N14835
6 · The paper itself

Abstract

With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.

Indexed as

MelanomaNevus, PigmentedSkin NeoplasmsAdultAgedFemaleHumansImage Processing, Computer-AssistedLightingMaleMelanocytesMiddle AgedPilot ProjectsSensitivity and SpecificityFilter-on-chipMelanomaMultispectral imagingPilot studySkin cancerSnapshot-mosaic

Identifiers

PMID39152181
PMCPMC11329730

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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.