Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor

Nakagawa, Junichi; Fujima, Noriyuki; Hirata, Kenji; Tang, Minghui; Tsuneta, Satonori; Suzuki, Jun; Harada, Taisuke; Ikebe, Yohei; Homma, Akihiro; Kano, Satoshi; Minowa, Kazuyuki; Kudo, Kohsuke

Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor

Nakagawa, Junichi; Fujima, Noriyuki; Hirata, Kenji et al.

Cancer Imaging, 2022, 22(1), 52

Permalink : https://hdl.handle.net/2115/87390

このアイテムのアクセス数:434件（2026-01-17 04:05 集計）

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アクセス権	metadata only access
URI	https://hdl.handle.net/2115/87390
タイトル	Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor
著者	Nakagawa, Junichi ; Fujima, Noriyuki ; Hirata, Kenji ; Tang, Minghui ; Tsuneta, Satonori ; Suzuki, Jun ; Harada, Taisuke ; Ikebe, Yohei ; Homma, Akihiro ; Kano, Satoshi ; Minowa, Kazuyuki ; Kudo, Kohsuke
著者名 Nakagawa, Junichi
e-Rad_Researcher 80431360 著者名 Fujima, Noriyuki
e-Rad_Researcher 30431365 著者名 Hirata, Kenji
著者名 Tang, Minghui
著者名 Tsuneta, Satonori
著者名 Suzuki, Jun
著者名 Harada, Taisuke
著者名 Ikebe, Yohei
e-Rad_Researcher 30312359 著者名 Homma, Akihiro
e-Rad_Researcher 00374421 著者名 Kano, Satoshi
著者名 Minowa, Kazuyuki
e-Rad_Researcher 10374232 著者名 Kudo, Kohsuke
言語	英語
キーワード	Head and neck
	Nasal or sinonasal tumor
	Orbital invasion
	Periorbita
	Deep learning
	Transfer learning
発行日	2022-09-22
出版者	BioMed Central
収録物名	Cancer Imaging
巻(号)	22(1)
ページ	52
抄録	Background In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep learning technique for the diagnosis of orbital invasion, using computed tomography (CT) images. Methods A total of 168 lesions with malignant nasal or sinonasal tumors were divided into a training dataset (n = 119) and a test dataset (n = 49). The final diagnosis (invasion-positive or -negative) was determined by experienced radiologists who carefully reviewed all of the CT images. In a CNN-based deep learning analysis, a slice of the square target region that included the orbital bone wall was extracted and fed into a deep-learning training session to create a diagnostic model using transfer learning with the Visual Geometry Group 16 (VGG16) model. The test dataset was subsequently tested in CNN-based diagnostic models and by two other radiologists who were not specialized in head and neck radiology. At approx. 2 months after the first reading session, two radiologists again reviewed all of the images in the test dataset, referring to the diagnoses provided by the trained CNN-based diagnostic model. Results The diagnostic accuracy was 0.92 by the CNN-based diagnostic models, whereas the diagnostic accuracies by the two radiologists at the first reading session were 0.49 and 0.45, respectively. In the second reading session by two radiologists (diagnosing with the assistance by the CNN-based diagnostic model), marked elevations of the diagnostic accuracy were observed (0.94 and 1.00, respectively). Conclusion The CNN-based deep learning technique can be a useful support tool in assessing the presence of orbital invasion on CT images, especially for non-specialized radiologists.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
PISSN	1740-5025
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.1186/s40644-022-00492-0