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Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos)

  • Author Footnotes
    ∗ Drs L. Wu and J. Wang contributed equally to this article.
    Lianlian Wu
    Footnotes
    ∗ Drs L. Wu and J. Wang contributed equally to this article.
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Author Footnotes
    ∗ Drs L. Wu and J. Wang contributed equally to this article.
    Jing Wang
    Footnotes
    ∗ Drs L. Wu and J. Wang contributed equally to this article.
    Affiliations
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China
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  • Xinqi He
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Yijie Zhu
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Xiaoda Jiang
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Yiyun Chen
    Affiliations
    School of Resources and Environmental Sciences of Wuhan University, Wuhan, China
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  • Yonggui Wang
    Affiliations
    School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
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  • Li Huang
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Renduo Shang
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Zehua Dong
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Boru Chen
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Xiao Tao
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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  • Qi Wu
    Correspondence
    Qi Wu, MD, Carcinogenesis and Translational Research (Ministry of Education), Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
    Affiliations
    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
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  • Honggang Yu
    Correspondence
    Reprint requests: Honggang Yu, MD, Department of Gastroenterology, Renmin Hospital of Wuhan University. 99 Zhangzhidong Rd, Wuhan 430060, Hubei Province, China
    Affiliations
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China

    Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China

    Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
    Search for articles by this author
  • Author Footnotes
    ∗ Drs L. Wu and J. Wang contributed equally to this article.

      Background and Aims

      We aimed to develop and validate a deep learning–based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human–machine competition.

      Methods

      This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band imaging endoscopy at the Peking University Cancer Hospital from June 9, 2020 to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read patients’ videos and made diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs.

      Results

      One hundred videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51% [95% confidence interval [CI], 81.23-85.79] and 87.13% [95% CI, 83.75-90.51], respectively). Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75% [95% CI, 61.12-66.39] and 64.41% [95% CI, 60.65-68.16], respectively).

      Conclusions

      The system outperformed endoscopists in identifying EGCs and was comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. This deep learning–based system could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.

      Abbreviations:

      AI (artificial intelligence), EGC (early gastric cancer), GC (gastric cancer), ICC (intraclass correlation coefficient), M-NBI (magnifying narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), SD (standard deviation), WLE (white-light endoscopy)
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      Linked Article

      • Erratum
        Gastrointestinal EndoscopyVol. 96Issue 1
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          In the article, “Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos),” by Wu et al (Gastrointest Endosc 2022;95:92-104), the affiliation listed for Dr Qi Wu was incorrect. The complete list of authors with the correct affiliations follows.
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