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Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study

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Oral Radiology Aims and scope Submit manuscript

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.

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Acknowledgements

We thank H. Nikki March, PhD, from Edanz Group (https://jp-author-services.edanzgroup.com/) for editing a draft pf this manuscript.

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Authors and Affiliations

  1. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan

    Yoshiko Ariji, Motoki Fukuda, Michihito Nozawa, Chiaki Kuwada & Eiichiro Ariji

  2. Department of Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan

    Mitsuo Goto, Kenichiro Ishibashi & Toru Nagao

  3. Department of Oral and Maxillofacial Surgery, Ogaki Municipal Hospital, Ogaki, Japan

    Kenichiro Ishibashi

  4. Department of Oral and Maxillofacial Surgery, Aichi-Gakuin University School of Dentistry, Nagoya, Japan

    Atsushi Nakayama

  5. Department of Oral Pathology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan

    Yoshihiko Sugita

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  1. Yoshiko Ariji
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Correspondence to Yoshiko Ariji.

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Conflict of interest

Yoshiko Ariji, Motoki Fukuda, Michihito Nozawa, Chiaki Kuwada, Mitsuo Goto, Kenichiro Ishibashi, Atsushi Nakayama, Yoshihiko Sugita,Toru Nagao, and Eiichiro Ariji declare that they have no conflicts of interest.

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All procedures were performed in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later versions.

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This article does not contain any studies with animal subjects performed by any of the authors.

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Ariji, Y., Fukuda, M., Nozawa, M. et al. Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study. Oral Radiol 37, 290–296 (2021). https://doi.org/10.1007/s11282-020-00449-8

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  • DOI: https://doi.org/10.1007/s11282-020-00449-8

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