フクダ モトキ   HUKUDA Motoki
  福田 元気
   所属   歯学部 歯科放射線学
   職種   助教
言語種別 英語
発行・発表の年月 2019/06
形態種別 学術雑誌
査読 査読あり
標題 CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification.
執筆形態 共著
掲載誌名 Oral Radiology
掲載区分国内
著者・共著者 Ariji Y, Sugita Y, Nagao T, Nakayama A, Fukuda M, Kise Y, Nozawa M, Nishiyama M, Katumata A, Ariji E.
概要 OBJECTIVE:
To clarify CT diagnostic performance in extranodal extension of cervical lymph node metastases using deep learning classification.
METHODS:
Seven-hundred and three CT images (178 with and 525 without extranodal extension) in 51 patients with cervical lymph node metastases from oral squamous cell carcinoma were enrolled in this study. All images were automatically divided into two datasets, assigning 80% as the training dataset and 20% as the testing dataset. A radiologist measured the minor axis and three radiologists evaluated central necrosis and irregular borders of each lymph node, and the diagnostic performances were obtained.
RESULTS:
The deep learning accuracy of extranodal extension was 84.0%. The radiologists' accuracies based on minor axis ≥ 11 mm, central necrosis, and irregular borders were 55.7%, 51.1% and 62.6%, respectively.
CONCLUSIONS:
The deep learning diagnostic performance in extranodal extension was significantly higher than that of radiologists.