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Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)

  • Author Footnotes
    ∗ Drs Cai, Li, and Tan contributed equally to this article.
    Shi-Lun Cai
    Footnotes
    ∗ Drs Cai, Li, and Tan contributed equally to this article.
    Affiliations
    Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai, China

    Endoscopy Research Institute of Fudan University, Shanghai, China
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  • Author Footnotes
    ∗ Drs Cai, Li, and Tan contributed equally to this article.
    Bing Li
    Footnotes
    ∗ Drs Cai, Li, and Tan contributed equally to this article.
    Affiliations
    Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai, China

    Endoscopy Research Institute of Fudan University, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    ∗ Drs Cai, Li, and Tan contributed equally to this article.
    Wei-Min Tan
    Footnotes
    ∗ Drs Cai, Li, and Tan contributed equally to this article.
    Affiliations
    School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
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  • Xue-Jing Niu
    Affiliations
    School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
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  • Hon-Ho Yu
    Affiliations
    Kiang Wu Hospital, Macau SAR, China
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  • Li-Qing Yao
    Affiliations
    Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai, China

    Endoscopy Research Institute of Fudan University, Shanghai, China
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  • Ping-Hong Zhou
    Affiliations
    Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai, China

    Endoscopy Research Institute of Fudan University, Shanghai, China
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  • Bo Yan
    Correspondence
    Bo Yan, School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.
    Affiliations
    School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
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  • Yun-Shi Zhong
    Correspondence
    Reprint requests: Yun-Shi Zhong, Endoscopy Research Institute of Fudan University, Shanghai, China
    Affiliations
    Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai, China

    Endoscopy Research Institute of Fudan University, Shanghai, China
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  • Author Footnotes
    ∗ Drs Cai, Li, and Tan contributed equally to this article.

      Background and Aims

      Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection (CAD) using a deep neural network (DNN) to localize and identify early ESCC under conventional endoscopic white-light imaging.

      Methods

      We collected 2428 (1332 abnormal, 1096 normal) esophagoscopic images from 746 patients to set up a novel DNN-CAD system in 2 centers and prepared a validation dataset containing 187 images from 52 patients. Sixteen endoscopists (senior, mid-level, and junior) were asked to review the images of the validation set. The diagnostic results, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared between the DNN-CAD system and endoscopists.

      Results

      The receiver operating characteristic curve for DNN-CAD showed that the area under the curve was >96%. For the validation dataset, DNN-CAD had a sensitivity, specificity, accuracy, PPV, and NPV of 97.8%, 85.4%, 91.4%, 86.4%, and 97.6%, respectively. The senior group achieved an average diagnostic accuracy of 88.8%, whereas the junior group had a lower value of 77.2%. After referring to the results of DNN-CAD, the average diagnostic ability of the endoscopists improved, especially in terms of sensitivity (74.2% vs 89.2%), accuracy (81.7% vs 91.1%), and NPV (79.3% vs 90.4%).

      Conclusions

      The novel DNN-CAD system used for screening of early ESCC has high accuracy and sensitivity, and can help endoscopists to detect lesions previously ignored under white-light imaging.

      Graphical abstract

      Abbreviations:

      AI (artificial intelligence), CAD (computer-aided detection), CNN (convolutional neural network), DNN (deep neural network), EC (esophageal cancer), ESCC (esophageal squamous cell carcinoma), LCE (Lugol’s chromoendoscopy), ME (magnifying endoscopy), NBI (narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), WLI (white-light imaging)
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      References

        • Bray F.
        • Ferlay J.
        • Soerjomataram I.
        • et al.
        Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
        CA Cancer J Clin. 2018; 68: 394-424
        • Arnold M.
        • Soerjomataram I.
        • Ferlay J.
        • et al.
        Global incidence of oesophageal cancer by histological subtype in 2012.
        Gut. 2015; 64: 381-387
        • Chen W.
        • Zheng R.
        • Baade P.D.
        • et al.
        Cancer statistics in China, 2015.
        CA Cancer J Clin. 2016; 66: 115-132
        • Enzinger P.C.
        • Mayer R.J.
        Esophageal cancer.
        N Engl J Med. 2003; 349: 2241-2252
        • Wang G.Q.
        • Jiao G.G.
        • Chang F.B.
        • et al.
        Long-term results of operation for 420 patients with early squamous cell esophageal carcinoma discovered by screening.
        Ann Thorac Surg. 2004; 77: 1740-1744
        • Wei W.Q.
        • Chen Z.F.
        • He Y.T.
        • et al.
        Long-term follow-up of a community assignment, one-time endoscopic screening study of esophageal cancer in China.
        J Clin Oncol. 2015; 33: 1951-1957
        • Muto M.
        • Minashi K.
        • Yano T.
        • et al.
        Early detection of superficial squamous cell carcinoma in the head and neck region and esophagus by narrow band imaging: a multicenter randomized controlled trial.
        J Clin Oncol. 2010; 28: 1566-1572
        • Ishihara R.
        • Takeuchi Y.
        • Chatani R.
        • et al.
        Prospective evaluation of narrow-band imaging endoscopy for screening of esophageal squamous mucosal high-grade neoplasia in experienced and less experienced endoscopists.
        Dis Esophagus. 2010; 23: 480-486
        • Misawa M.
        • Kudo S.E.
        • Mori Y.
        • et al.
        Artificial intelligence-assisted polyp detection for colonoscopy: initial experience.
        Gastroenterology. 2018; 154: 2027-2029.e3
        • Urban G.
        • Tripathi P.
        • Alkayali T.
        • et al.
        Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy.
        Gastroenterology. 2018; 155: 1069-1078.e8
        • Dawsey S.M.
        • Fleischer D.E.
        • Wang G.Q.
        • et al.
        Mucosal iodine staining improves endoscopic visualization of squamous dysplasia and squamous cell carcinoma of the esophagus in Linxian, China.
        Cancer. 1998; 83: 220-231
        • Li J.
        • Xu R.
        • Liu M.
        • et al.
        Lugol chromoendoscopy detects esophageal dysplasia with low levels of sensitivity in a high-risk region of China.
        Clin Gastroenterol Hepatol. 2018; 16: 1585-1592
        • Nagami Y.
        • Tominaga K.
        • Machida H.
        • et al.
        Usefulness of non-magnifying narrow-band imaging in screening of early esophageal squamous cell carcinoma: a prospective comparative study using propensity score matching.
        Am J Gastroenterol. 2014; 109: 845-854
        • Takenaka R.
        • Kawahara Y.
        • Okada H.
        • et al.
        Narrow-band imaging provides reliable screening for esophageal malignancy in patients with head and neck cancers.
        Am J Gastroenterol. 2009; 104: 2942-2948
        • Shichijo S.
        • Nomura S.
        • Aoyama K.
        • et al.
        Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images.
        EBioMedicine. 2017; 25: 106-111
        • Hirasawa T.
        • Aoyama K.
        • Tanimoto T.
        • et al.
        Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.
        Gastric Cancer. 2018; 21: 653-660
        • Mori Y.
        • Kudo S.E.
        • Misawa M.
        • et al.
        Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study.
        Ann Intern Med. 2018; 169: 357-366
        • Byrne M.F.
        • Chapados N.
        • Soudan F.
        • et al.
        Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.
        Gut. 2019; 68: 94-100
        • Horie Y.
        • Yoshio T.
        • Aoyama K.
        • et al.
        Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.
        Gastrointest Endosc. 2019; 89: 25-32
        • Zhao Y.Y.
        • Xue D.X.
        • Wang Y.L.
        • et al.
        Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy.
        Endoscopy. 2018; 51: 333-341
        • Swager A.F.
        • van der Sommen F.
        • Klomp S.R.
        • et al.
        Computer-aided detection of early Barrett's neoplasia using volumetric laser endomicroscopy.
        Gastrointest Endosc. 2017; 86: 839-846
        • Ebigbo A.
        • Mendel R.
        • Probst A.
        • et al.
        Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma.
        Gut. 2018; 68: 1143-1145