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Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study

  • Author Footnotes
    ∗ Drs Hu and Dong and Ms Gong contributed equally to this article.
    Hao Hu
    Footnotes
    ∗ Drs Hu and Dong and Ms Gong contributed equally to this article.
    Affiliations
    Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Author Footnotes
    ∗ Drs Hu and Dong and Ms Gong contributed equally to this article.
    Lixin Gong
    Footnotes
    ∗ Drs Hu and Dong and Ms Gong contributed equally to this article.
    Affiliations
    College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, China

    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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  • Author Footnotes
    ∗ Drs Hu and Dong and Ms Gong contributed equally to this article.
    Di Dong
    Footnotes
    ∗ Drs Hu and Dong and Ms Gong contributed equally to this article.
    Affiliations
    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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  • Liang Zhu
    Affiliations
    Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Min Wang
    Affiliations
    Department of Gastroenterology, Hepatology and Nutrition, Shanghai Children’s Hospital, Shanghai Jiaotong University, Shanghai, China
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  • Jie He
    Affiliations
    Endoscopy Center, Zhongshan Hospital (Xiamen Branch), Fudan University, Xiamen, China
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  • Lei Shu
    Affiliations
    Department of Gastroenterology, No. 1 Hospital of Wuhan, Wuhan, China
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  • Yiling Cai
    Affiliations
    Department of Gastroenterology, The Affiliated Dongnan Hospital of Xiamen University, Zhangzhou, China
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  • Shilun Cai
    Affiliations
    Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Wei Su
    Affiliations
    Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Yunshi Zhong
    Affiliations
    Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Cong Li
    Affiliations
    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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  • Yongbei Zhu
    Affiliations
    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
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  • Mengjie Fang
    Affiliations
    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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  • Lianzhen Zhong
    Affiliations
    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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  • Xin Yang
    Affiliations
    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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  • Pinghong Zhou
    Correspondence
    Pinghong Zhou, MD, Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital of Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
    Affiliations
    Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China
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  • Jie Tian
    Correspondence
    Reprint requests: Jie Tian, PhD, Director of the CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95, Zhongguancun East Rd, Beijing, 100190, China
    Affiliations
    CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
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  • Author Footnotes
    ∗ Drs Hu and Dong and Ms Gong contributed equally to this article.
Published:November 25, 2020DOI:https://doi.org/10.1016/j.gie.2020.11.014

      Background and Aims

      Narrow-band imaging with magnifying endoscopy (ME-NBI) has shown advantages in the diagnosis of early gastric cancer (EGC). However, proficiency in diagnostic algorithms requires substantial expertise and experience. In this study, we aimed to develop a computer-aided diagnostic model for EGM (EGCM) to analyze and assist in the diagnosis of EGC under ME-NBI.

      Methods

      A total of 1777 ME-NBI images from 295 cases were collected from 3 centers. These cases were randomly divided into a training cohort (n = 170), an internal test cohort (ITC, n = 73), and an external test cohort (ETC, n = 52). EGCM based on VGG-19 architecture (Visual Geometry Group [VGG], Oxford University, Oxford, UK) with a single fully connected 2-classification layer was developed through fine-tuning and validated on all cohorts. Furthermore, we compared the model with 8 endoscopists with varying experience. Primary comparison measures included accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

      Results

      EGCM acquired AUCs of .808 in the ITC and .813 in the ETC. Moreover, EGCM achieved similar predictive performance as the senior endoscopists (accuracy: .770 vs .755, P = .355; sensitivity: .792 vs .767, P = .183; specificity: .745 vs .742, P = .931) but better than the junior endoscopists (accuracy: .770 vs .728, P < .05). After referring to the results of EGCM, the average diagnostic ability of the endoscopists was significantly improved in terms of accuracy, sensitivity, PPV, and NPV (P < .05).

      Conclusions

      EGCM exhibited comparable performance with senior endoscopists in the diagnosis of EGC and showed the potential value in aiding and improving the diagnosis of EGC by endoscopists.

      Abbreviations:

      AI (artificial intelligence), AUC (area under the receiver operating characteristic curve), EGC (early gastric cancer), EGCM (computer-aided early gastric cancer diagnosis model), ETC (external test cohort), FDZS (Endoscopic Center of Zhongshan Hospital), GC (gastric cancer), Grad-CAM (gradient-weighted class activation mapping), ITC (internal test cohort), ME-NBI (magnifying endoscopy narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), TC (training cohort), WH (Central Hospital of Wuhan), XMDN (Affiliated Dongnan Hospital of Xiamen University)
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      Linked Article

      • Artificial intelligence in the upper GI tract: the future is fast approaching
        Gastrointestinal EndoscopyVol. 93Issue 6
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          Gastric cancer remains a major cause of morbidity and mortality worldwide.1 Identifying early gastric cancer (EGC) during endoscopy is crucial because of the prognostic consequences associated with early diagnosis. Despite considerable technical developments in endoscopic practice, including magnified endoscopy (ME) with narrow-band imaging (NBI), the gastric cancer missed rate remains as high as 10%.2 Furthermore, considerable interobserver differences in the characterization of lesions identified during gastroscopy have been reported.
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