Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning

Takano, Yuki; Fujima, Noriyuki; Nakagawa, Junichi; Dobashi, Hiroki; Shimizu, Yukie; Kanaya, Motoma; Kano, Satoshi; Homma, Akihiro; Kudo, Kohsuke

Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning

Takano, Yuki; Fujima, Noriyuki; Nakagawa, Junichi et al.

European journal of radiology, 2025, 189, 112168

Permalink : https://hdl.handle.net/2115/97850

このアイテムのアクセス数:45件（2026-08-15 04:09 集計）

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/97850
タイトル	Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning
著者	Takano, Yuki ; Fujima, Noriyuki ; Nakagawa, Junichi ; Dobashi, Hiroki ; Shimizu, Yukie ; Kanaya, Motoma ; Kano, Satoshi ; Homma, Akihiro ; Kudo, Kohsuke
著者名 Takano, Yuki
e-Rad_Researcher 80431360 著者名 Fujima, Noriyuki
著者名 Nakagawa, Junichi
著者名 Dobashi, Hiroki
e-Rad_Researcher 60880474 著者名 Shimizu, Yukie
著者名 Kanaya, Motoma
e-Rad_Researcher 00374421 著者名 Kano, Satoshi
e-Rad_Researcher 30312359 著者名 Homma, Akihiro
e-Rad_Researcher 10374232 著者名 Kudo, Kohsuke
言語	英語
キーワード	Head and neck
	Laryngeal and hypopharyngeal cancer
	Thyroid cartilage invasion
	Deep learning
	Convolutional neural network
発行日	2025-05-13
出版者	Elsevier
収録物名	European journal of radiology
巻(号)	189
ページ	112168
抄録	Objectives: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance. Methods: We retrospectively analyzed 91 cases of laryngeal or hypopharyngeal cancer treated surgically at our hospital during the period April 2010 through May 2023, and we divided the cases into datasets for training (n = 61) and testing (n = 30). We reviewed the CT images and pathological diagnoses in all cases to determine the invasion positive- or negative-status as a ground truth. We trained the new CNN model to classify thyroid cartilage invasion-positive or -negative status from the pre-treatment axial CT images by transfer learning from Residual Network 101 (ResNet101), using the training dataset. We then used the test dataset to evaluate the model's performance. Two radiologists, one with extensive head and neck imaging experience (senior reader) and the other with less experience (junior reader) reviewed the CT images of the test dataset to determine whether thyroid cartilage invasion was present. Results: The following were obtained by the CNN model with the test dataset: area under the curve (AUC), 0.82; 90 % accuracy, 80 % sensitivity, and 95 % specificity. The CNN model showed a significant difference in AUCs compared to the junior reader (p = 0.035) but not the senior reader (p = 0.61). Conclusions: The CNN-based diagnostic model can be a useful supportive tool for the assessment of thyroid cartilage invasion in patients with laryngeal or hypopharyngeal cancer.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
権利情報	© 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
PISSN	0720-048X
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1016/j.ejrad.2025.112168