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Convolutional Neural Network for Differentiating Gastric Cancer from Gastritis Using Magnified Endoscopy with Narrow Band Imaging

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Abstract

Background

Early detection of early gastric cancer (EGC) allows for less invasive cancer treatment. However, differentiating EGC from gastritis remains challenging. Although magnifying endoscopy with narrow band imaging (ME-NBI) is useful for differentiating EGC from gastritis, this skill takes substantial effort. Since the development of the ability to convolve the image while maintaining the characteristics of the input image (convolution neural network: CNN), allowing the classification of the input image (CNN system), the image recognition ability of CNN has dramatically improved.

Aims

To explore the diagnostic ability of the CNN system with ME-NBI for differentiating between EGC and gastritis.

Methods

A 22-layer CNN system was pre-trained using 1492 EGC and 1078 gastritis images from ME-NBI. A separate test data set (151 EGC and 107 gastritis images based on ME-NBI) was used to evaluate the diagnostic ability [accuracy, sensitivity, positive predictive value (PPV), and negative predictive value (NPV)] of the CNN system.

Results

The accuracy of the CNN system with ME-NBI images was 85.3%, with 220 of the 258 images being correctly diagnosed. The method’s sensitivity, specificity, PPV, and NPV were 95.4%, 71.0%, 82.3%, and 91.7%, respectively. Seven of the 151 EGC images were recognized as gastritis, whereas 31 of the 107 gastritis images were recognized as EGC. The overall test speed was 51.83 images/s (0.02 s/image).

Conclusions

The CNN system with ME-NBI can differentiate between EGC and gastritis in a short time with high sensitivity and NPV. Thus, the CNN system may complement current clinical practice of diagnosis with ME-NBI.

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Acknowledgments

This work was supported in part by the Foundation for Promotion of Cancer Research in Japan.

Funding

This work was supported in part by the Foundation for Promotion of Cancer Research in Japan.

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Authors and Affiliations

  1. Department of Gastroenterology, Cancer Institute Hospital, 3-10-6 Ariake, Koto-ku, Tokyo, 135-8550, Japan

    Yusuke Horiuchi, Yoshitaka Tokai, Toshiaki Hirasawa, Shoichi Yoshimizu, Akiyoshi Ishiyama, Toshiyuki Yoshio, Tomohiro Tsuchida & Junko Fujisaki

  2. AI Medical Service Inc., Arai Building 2F, 1-10-13 Minami Ikebukuro, Toshima-ku, Tokyo, 171-0022, Japan

    Kazuharu Aoyama & Tomohiro Tada

  3. Tada Tomohiro Institute of Gastroenterology and Proctology, 7-2-1 Bessho, Minami-ku, Saitama, 336-0021, Japan

    Tomohiro Tada

Authors
  1. Yusuke Horiuchi
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  2. Kazuharu Aoyama
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  3. Yoshitaka Tokai
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  4. Toshiaki Hirasawa
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  5. Shoichi Yoshimizu
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  6. Akiyoshi Ishiyama
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  7. Toshiyuki Yoshio
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  8. Tomohiro Tsuchida
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  9. Junko Fujisaki
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  10. Tomohiro Tada
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Corresponding author

Correspondence to Yusuke Horiuchi.

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Conflicts of interest

There are no conflicts of interest associated with this study.

Ethical approval

The study has been approved by the institutional review board of the Cancer Institute Hospital (IRB no. 2016-1171) and the Japan Medical Association (ID JMA-IIA00283). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

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Written informed consent for use of pathological specimens and imaging data for research purposes was obtained from each patient.

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Horiuchi, Y., Aoyama, K., Tokai, Y. et al. Convolutional Neural Network for Differentiating Gastric Cancer from Gastritis Using Magnified Endoscopy with Narrow Band Imaging. Dig Dis Sci 65, 1355–1363 (2020). https://doi.org/10.1007/s10620-019-05862-6

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  • DOI: https://doi.org/10.1007/s10620-019-05862-6

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