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European Archives of Oto-Rhino-Laryngology

Erschienen in:

14.08.2023 | Head and Neck

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

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

Erschienen in: European Archives of Oto-Rhino-Laryngology | Ausgabe 1/2024

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Abstract

Introduction

We 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 method

A 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.

Results

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

Conclusion

Deep 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.

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Titel: Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?
verfasst von: Sermin Can
Ömer Türk
Muhammed Ayral
Günay Kozan
Hamza Arı
Mehmet Akdağ
Müzeyyen Yıldırım Baylan
Publikationsdatum: 14.08.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: European Archives of Oto-Rhino-Laryngology / Ausgabe 1/2024
Print ISSN: 0937-4477
Elektronische ISSN: 1434-4726
DOI: https://doi.org/10.1007/s00405-023-08181-9

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Abstract

Introduction

Material method

Results

Conclusion

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