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Tytuł pozycji:

Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence.

Tytuł :
Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence.
Autorzy :
Ariji Y; Associate Proffessor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan. Electronic address: .
Fukuda M; Instructor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan.
Kise Y; Assistant Professor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan.
Nozawa M; Instructor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan.
Yanashita Y; Postgraduate student, Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Fujita H; Professor, Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Katsumata A; Professor, Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan.
Ariji E; Associate Proffessor, Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of dentistry, Nagoya, Japan.
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Źródło :
Oral surgery, oral medicine, oral pathology and oral radiology [Oral Surg Oral Med Oral Pathol Oral Radiol] 2019 May; Vol. 127 (5), pp. 458-463. Date of Electronic Publication: 2018 Oct 15.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Original Publication: New York, NY : Elsevier
MeSH Terms :
Mouth Neoplasms*
Carcinoma, Squamous Cell ; Contrast Media ; Deep Learning ; Humans ; Lymph Nodes ; Lymphatic Metastasis ; Tomography, X-Ray Computed
Substance Nomenclature :
0 (Contrast Media)
Entry Date(s) :
Date Created: 20181201 Date Completed: 20200116 Latest Revision: 20200116
Update Code :
20210210
DOI :
10.1016/j.oooo.2018.10.002
PMID :
30497907
Czasopismo naukowe
Objective: Although the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis.
Study Design: The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma. The performance of a deep learning image classification system for the diagnosis of lymph node metastasis on CT images was compared with the diagnostic interpretations of 2 experienced radiologists by using the Mann-Whitney U test and χ 2 analysis.
Results: The performance of the deep learning image classification system resulted in accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and area under the receiver operating characteristic curve of 0.80. These values were not significantly different from those found by the radiologists.
Conclusions: The deep learning system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.
(Copyright © 2018 Elsevier Inc. All rights reserved.)

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