Yoshiko Ariji1, Motoki Fukuda2, Michihito Nozawa2, Chiaki Kuwada2, Mitsuo Goto3, Kenichiro Ishibashi3,4, Atsushi Nakayama5, Yoshihiko Sugita6, Toru Nagao3, Eiichiro Ariji2. 1. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. yoshiko@dpc.agu.ac.jp. 2. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. 3. Department of Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan. 4. Department of Oral and Maxillofacial Surgery, Ogaki Municipal Hospital, Ogaki, Japan. 5. Department of Oral and Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan. 6. Department of Oral Pathology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Abstract
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.
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.
Authors: Kevin C Lee; Scott M Peters; Jaya S Pradhan; David M Alfi; David A Koslovsky; Elizabeth M Philipone Journal: Oral Surg Oral Med Oral Pathol Oral Radiol Date: 2019-10-13
Authors: Rasheed Omobolaji Alabi; Ibrahim O Bello; Omar Youssef; Mohammed Elmusrati; Antti A Mäkitie; Alhadi Almangush Journal: Front Oral Health Date: 2021-07-26