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Tytuł pozycji:

Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study.

Tytuł :
Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study.
Autorzy :
Ariji Y; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. .
Fukuda M; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Nozawa M; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Kuwada C; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Goto M; Department of Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Ishibashi K; Department of Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.; Department of Oral and Maxillofacial Surgery, Ogaki Municipal Hospital, Ogaki, Japan.
Nakayama A; Department of Oral and Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Sugita Y; Department of Oral Pathology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Nagao T; Department of Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Ariji E; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Pokaż więcej
Źródło :
Oral radiology [Oral Radiol] 2021 Apr; Vol. 37 (2), pp. 290-296. Date of Electronic Publication: 2020 Jun 06.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Publication: 2004- : Tokyo : Springer-Verlag Tokyo
Original Publication: Gifu, Japan : Japanese Society of Dental Radiology,
MeSH Terms :
Carcinoma, Squamous Cell*/diagnostic imaging
Deep Learning*
Head and Neck Neoplasms*
Mouth Neoplasms*/diagnostic imaging
Humans ; Lymph Nodes/diagnostic imaging ; Lymphatic Metastasis/diagnostic imaging ; Squamous Cell Carcinoma of Head and Neck
References :
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Contributed Indexing :
Keywords: Cervical lymph node metastasis; Computed tomography; Deep learning; Object detection; Oral squamous cell carcinoma
Entry Date(s) :
Date Created: 20200608 Date Completed: 20210419 Latest Revision: 20210419
Update Code :
20210420
DOI :
10.1007/s11282-020-00449-8
PMID :
32506212
Czasopismo naukowe
Objective: To apply a deep learning object detection technique to CT images for detecting cervical lymph nodes metastasis in patients with oral cancers, and to clarify the detection performance.
Methods: One hundred and fifty-nine metastatic and 517 non-metastatic lymph nodes on 365 CT images in 56 patients with oral squamous cell carcinoma were examined. The images were arbitrarily assigned to training, validation, and testing datasets. Using the neural network, 'DetectNet' for object detection, the training procedure was conducted for 1000 epochs. Testing image datasets were applied to the learning model, and the detection performance was calculated.
Results: The learning curve indicated that the recall (sensitivity) for detecting metastatic and non-metastatic lymph nodes reached 90% and 80%, respectively, while the model performance recall by applying the test dataset was 73.0% and 52.5%, respectively. The recall for detecting level IB and Level II metastatic lymph nodes was relatively high.
Conclusions: A system that has the potential to automatically detect cervical lymph nodes was constructed.

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