Evidence map›Paper›PMID 39682026›Full record

ArticleLasers in surgery and medicine2025

Differentiation of Tumors of the Upper Respiratory Tract Using Optical Metabolic Imaging.

Dennis Eggert, David Gaertner, Adrian Rühm, Ronald Sroka, Christoph Arens, Nikolaos Davaris, Konrad Birkmeier, Andreas Brodschelm, Patrick Leisching, Hauke Studier and 3 more

Abstract read
In one paragraph

Article in Lasers in surgery and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

13 authors.

Dennis EggertClinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.ORCID http://orcid.org/0000-0002-6464-9373
David GaertnerClinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Adrian RühmDepartment of Urology, Laser-Forschungslabor, LIFE Center, University Hospital, Planegg, Germany.
Ronald SrokaDepartment of Urology, Laser-Forschungslabor, LIFE Center, University Hospital, Planegg, Germany.
Christoph ArensUniversity Clinic of Otolaryngology, Head and Neck Surgery, Otto von Guericke University Magdeburg, Magdeburg, Germany.
Nikolaos DavarisUniversity Clinic of Otolaryngology, Head and Neck Surgery, Otto von Guericke University Magdeburg, Magdeburg, Germany.
Konrad BirkmeierTOPTICA Photonics AG Lochhamer Schlag, Gräfelfing, Germany.
Andreas BrodschelmTOPTICA Photonics AG Lochhamer Schlag, Gräfelfing, Germany.
Patrick LeischingTOPTICA Photonics AG Lochhamer Schlag, Gräfelfing, Germany.
Hauke StudierBecker & Hickl GmbH, Berlin, Germany.
Wolfgang BeckerBecker & Hickl GmbH, Berlin, Germany.
Karsten KönigJenLab GmbH, Berlin, Germany.
Christan S BetzClinic and Polyclinic for Otolaryngology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Funding

This work was funded by the Federal Ministry of Education and Research of Germany (grant ID: 13N14509).
6 · The paper itself

Abstract

objectivesWith over 184,000 new cases and more than 99,000 deaths per year, malignancies of the larynx are a global health problem. Currently, a dedicated screening method enabling a direct onsite diagnosis is missing. This can lead to delayed diagnosis and worse outcomes of the patients. An endoscopic optical method enabling a direct distinction between healthy tissue, dysplastic tissue and cancerous tissue would be an ideal tool for the detection of tumors of the upper aerodigestive tract (UADT). Healthy and tumor cells differ significantly in their metabolic state due to the different metabolic pathways they use (more oxidative phosphorylation in healthy cells, more glycolysis in tumor cells). Optical metabolic imaging (OMI) measuring relative intracellular concentration of NAD(P)H and FAD redox pairs could be a promising approach for early tumor detection and differentiation of suspicious mucosal lesions.

methodsIn this study, a specially designed endoscopic two-beam two-photon fluorescence lifetime imaging (FLIM) system was used to perform two-photon two-beam FLIM of NAD(P)H and FAD to image the metabolic state in different tissue samples of the UADT. FLIM data sets of 27 tissue samples from 16 patients were recorded directly after surgery ex vivo in a special tissue culture medium at 37°C on a dedicated microscope using multiphoton excitation.

resultsBased on the FLIM measurements of NAD(P)H and FAD, six of the most common indices for the characterization of the cells' metabolism were calculated. Three of them, the ratio of the exponential coefficients (amplitudes) of the short and long lifetime components both for NAD(P)H and FAD (NAD(P)H a1/a2 ratio and FAD a1/a2 ratio) and the fluorescence lifetime redox ratio (FLIRR) enabled differentiation between healthy tissue, benign lesions, dysplastic tissue, and cancer tissue with statistical significance.

conclusionsWe showed by measurements on freshly collected tissue samples that mucosal lesions of the UADT can be differentiated using our newly designed endoscopic FLIM device. In vivo measurements in healthy volunteers were also possible. By means of this technology, differentiation of cancerous, pre-cancerous, and healthy tissue in the UADT by OMI could be possible. Of six indices used to characterize cell metabolism we calculated, the FLIRR showed the most significant differences between tissue types.

Indexed as

Head and Neck NeoplasmsOptical ImagingPrecancerous ConditionsRespiratory MucosaSquamous Cell Carcinoma of Head and NeckAgedEndoscopyFemaleHumansMaleMiddle Agedendoscopyfluorescence lifetime imaginghead and neck canceroptical biopsyoptical metabolic imaging

Identifiers

PMID39682026
PMCPMC11844726

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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.