Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?
Yükleniyor...
Dosyalar
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Deep learning, Granulomatous diseases, Lymphadenopathy, Lymphoma, Reactive hyperplasia, Squamous cell tumor
Kaynak
European Archives of Oto-Rhino-Laryngology
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
281
Sayı
1
Künye
Can, S., Türk, Ö., Ayral, M. et al. Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?. Eur Arch Otorhinolaryngol 281, 359–367 (2024). https://doi.org/10.1007/s00405-023-08181-9
