Evidence map›Paper›PMID 37578497›Full record

ArticleEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2024

Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?

Sermin Can, Ömer Türk, Muhammed Ayral, Günay Kozan, Hamza Arı, Mehmet Akdağ, Müzeyyen Yıldırım Baylan

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Article in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025
    Article
  4. Artificial intelligence in otorhinolaryngology: current trends and application areas.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
    Review
  5. 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

7 authors.

Sermin Can *Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey. sermin.can@hotmail.com.ORCID http://orcid.org/0000-0003-2688-4927
Ömer Türk *Department of Computer Programming, Mardin Artuklu University Vocational School, Mardin, Turkey.ORCID http://orcid.org/0000-0002-0060-1880
Muhammed Ayral *Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey.ORCID http://orcid.org/0000-0002-2421-4842
Günay Kozan *Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey.ORCID http://orcid.org/0000-0002-8676-6175
Hamza Arı *Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey.ORCID http://orcid.org/0009-0002-7011-5148
Mehmet Akdağ *Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey.ORCID http://orcid.org/0000-0003-1377-4227
Müzeyyen Yıldırım Baylan *Department of Otorhinolaryngology and Head and Neck Surgery Clinic, Dicle University Faculty of Medicine, 21010, Diyarbakir, Turkey.ORCID http://orcid.org/0000-0002-7537-1733

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWe aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations. MATERIAL

methodA total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input.

resultsThe classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively.

conclusionDeep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.

Indexed as

Deep LearningLymphadenopathyDiagnosis, DifferentialHumansNeckRetrospective StudiesDeep learningGranulomatous diseasesLymphadenopathyLymphomaReactive hyperplasiaSquamous cell tumor

Identifiers

PMID37578497

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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.