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CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification

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Abstract

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. CT images were cropped to an arbitrary size to include lymph nodes and surrounding tissues. All images were automatically divided into two datasets, assigning 80% as the training dataset and 20% as the testing dataset. The automated selection was repeated five times. Each training dataset was imported to a deep learning training system “DIGITS”. Five learning models were created after 300 epochs of the learning process using a neural network “AlexNet”. Each testing dataset was applied to each created learning model and resulting five performances were averaged as estimated diagnostic performances. 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. This method is expected to improve diagnostic accuracy by further study with increasing the number of patients.

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Author notes

  1. Yoshiko Ariji and Yoshihiko Sugita contributed equally to this work.

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, Yoshitaka Kise, Michihito Nozawa, Masako Nishiyama & Eiichiro Ariji

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

    Yoshihiko Sugita

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

    Toru Nagao

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

    Atsushi Nakayama

  5. Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan

    Akitoshi Katumata

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  1. Yoshiko Ariji
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  2. Yoshihiko Sugita
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Correspondence to Yoshiko Ariji.

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Conflict of interest

Yoshiko Ariji, Yoshihiko Sugita, Toru Nagao, Atsushi Nakayama, Motoki Fukuda, Yoshitaka Kise, Michihito Nozawa, Masako Nishiyama, Akitoshi Katumata and Eiichiro Ariji declare that they have no conflict of interest.

Human rights statements and informed consent

All procedures followed were 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.

Animal rights statement

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

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Ariji, Y., Sugita, Y., Nagao, T. et al. CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification. Oral Radiol 36, 148–155 (2020). https://doi.org/10.1007/s11282-019-00391-4

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  • DOI: https://doi.org/10.1007/s11282-019-00391-4

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