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

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Title: Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor
Authors: Nakagawa, Junichi Browse this author
Fujima, Noriyuki Browse this author →KAKEN DB
Hirata, Kenji Browse this author →KAKEN DB
Tang, Minghui Browse this author
Tsuneta, Satonori Browse this author
Suzuki, Jun Browse this author
Harada, Taisuke Browse this author
Ikebe, Yohei Browse this author
Homma, Akihiro Browse this author →KAKEN DB
Kano, Satoshi Browse this author →KAKEN DB
Minowa, Kazuyuki Browse this author
Kudo, Kohsuke Browse this author →KAKEN DB
Keywords: Head and neck
Nasal or sinonasal tumor
Orbital invasion
Deep learning
Transfer learning
Issue Date: 22-Sep-2022
Publisher: BioMed Central
Journal Title: Cancer Imaging
Volume: 22
Issue: 1
Start Page: 52
Publisher DOI: 10.1186/s40644-022-00492-0
Abstract: 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.
Type: article
Appears in Collections:北海道大学病院 (Hokkaido University Hospital) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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