Advertisement

Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos)

  • Author Footnotes
    ∗ Drs He and L. Wu contributed equally to this article.
    Xinqi He
    Footnotes
    ∗ Drs He and L. Wu 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
    Search for articles by this author
  • Author Footnotes
    ∗ Drs He and L. Wu contributed equally to this article.
    Lianlian Wu
    Footnotes
    ∗ Drs He and L. Wu 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
    Search for articles by this author
  • 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
    Search for articles by this author
  • Dexin Gong
    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
  • 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
    Search for articles by this author
  • Heng Zhang
    Affiliations
    Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
    Search for articles by this author
  • Yaowei Ai
    Affiliations
    Department of Gastroenterology, The People's Hospital of China Three Gorges University, The First People’s Hospital of Yichang, Yichang, China
    Search for articles by this author
  • Qiaoyun Tong
    Affiliations
    Department of Gastroenterology, Yichang Central People’s Hospital & Institute of Digestive Diseases, China Three Gorges University, Yichang, China
    Search for articles by this author
  • Peihua Lv
    Affiliations
    Spleen and Stomach Department, Jingmen Petrochemical Hospital, Jingmen, China
    Search for articles by this author
  • Bin Lu
    Affiliations
    Department of Gastroenterology, Xiaogan Central Hospital, Xiaogan, China
    Search for articles by this author
  • Qi Wu
    Affiliations
    Department of Endoscopy Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
    Search for articles by this author
  • Jingping Yuan
    Affiliations
    Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
    Search for articles by this author
  • Ming Xu
    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
  • Honggang Yu
    Correspondence
    Reprint requests: Honggang Yu, 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 He and L. Wu contributed equally to this article.
Published:December 08, 2021DOI:https://doi.org/10.1016/j.gie.2021.11.040

      Background and Aims

      Endoscopy is a pivotal method for detecting early gastric cancer (EGC). However, skill among endoscopists varies greatly. Here, we proposed a deep learning–based system named ENDOANGEL-ME to diagnose EGC in magnifying image-enhanced endoscopy (M-IEE).

      Methods

      M-IEE images were retrospectively obtained from 6 hospitals in China, including 4667 images for training and validation, 1324 images for internal tests, and 4702 images for external tests. One hundred eighty-seven stored videos from 2 hospitals were used to evaluate the performance of ENDOANGEL-ME and endoscopists and to assess the effect of ENDOANGEL-ME on improving the performance of endoscopists. Prospective consecutive patients undergoing M-IEE were enrolled from August 17, 2020 to August 2, 2021 in Renmin Hospital of Wuhan University to assess the applicability of ENDOANGEL-ME in clinical practice.

      Results

      A total of 3099 patients undergoing M-IEE were enrolled in this study. The diagnostic accuracy of ENDOANGEL-ME for diagnosing EGC was 88.44% and 90.49% in internal and external images, respectively. In 93 internal videos, ENDOANGEL-ME achieved an accuracy of 90.32% for diagnosing EGC, significantly superior to that of senior endoscopists (70.16% ± 8.78%). In 94 external videos, with the assistance of ENDOANGEL-ME, endoscopists showed improved accuracy and sensitivity (85.64% vs 80.32% and 82.03% vs 67.19%, respectively). In 194 prospective consecutive patients with 251 lesions, ENDOANGEL-ME achieved a sensitivity of 92.59% (25/27) and an accuracy of 83.67% (210/251) in real clinical practice.

      Conclusions

      This multicenter diagnostic study showed that ENDOANGEL-ME can be well applied in the clinical setting. (Clinical trial registration number: ChiCTR2000035116.)

      Abbreviations:

      AI (artificial intelligence), CI (confidence interval), EGC (early gastric cancer), GC (gastric cancer), M-IEE (magnifying image-enhanced endoscopy), M-NBI (magnifying narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), RHWU (Renmin Hospital of Wuhan University)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Gastrointestinal Endoscopy
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Karimi P.
        • Islami F.
        • Anandasabapathy S.
        • et al.
        Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention.
        Cancer Epidemiol Biomarkers Prev. 2014; 23: 700-713
        • 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
        • Chen W.
        • Zheng R.
        • Baade P.D.
        • et al.
        Cancer statistics in China, 2015.
        CA Cancer J Clin. 2016; 66: 115-132
        • Laks S.
        • Meyers M.O.
        • Kim H.J.
        Surveillance for gastric cancer.
        Surg Clin North Am. 2017; 97: 317-331
        • Bisschops R.
        • Areia M.
        • Coron E.
        • et al.
        Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative.
        Endoscopy. 2016; 48: 843-864
        • Pasechnikov V.
        • Chukov S.
        • Fedorov E.
        • et al.
        Gastric cancer: prevention, screening and early diagnosis.
        World J Gastroenterol. 2014; 20: 13842-13862
        • Kaise M.
        Advanced endoscopic imaging for early gastric cancer.
        Best Pract Res Clin Gastroenterol. 2015; 29: 575-587
        • Song M.A.T.
        Early detection of early gastric cancer using image-enhanced endoscopy: current trends.
        Gastrointest Intervent. 2014; 3: 1-7
        • Dohi O.
        • Yagi N.
        • Majima A.
        • et al.
        Diagnostic ability of magnifying endoscopy with blue laser imaging for early gastric cancer: a prospective study.
        Gastric Cancer. 2017; 20: 297-303
        • Otsuka Y.
        • Niwa Y.
        • Ohmiya N.
        • et al.
        Usefulness of magnifying endoscopy in the diagnosis of early gastric cancer.
        Endoscopy. 2004; 36: 165-169
        • Nakanishi H.
        • Doyama H.
        • Ishikawa H.
        • et al.
        Evaluation of an e-learning system for diagnosis of gastric lesions using magnifying narrow-band imaging: a multicenter randomized controlled study.
        Endoscopy. 2017; 49: 957-967
        • Ueyama H.
        • Kato Y.
        • Akazawa Y.
        • et al.
        Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.
        J Gastroenterol Hepatol. 2021; 36: 482-489
        • Horiuchi Y.
        • Hirasawa T.
        • Ishizuka N.
        • et al.
        Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging (with videos).
        Gastrointest Endosc. 2020; 92: 856-865
        • Wu L.
        • Zhou W.
        • Wan X.
        • et al.
        A deep neural network improves endoscopic detection of early gastric cancer without blind spots.
        Endoscopy. 2019; 51: 522-531
        • Wu L.
        • He X.
        • Liu M.
        • et al.
        Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial.
        Endoscopy. 2021; 53: 1199-1207
        • Ling T.
        • Wu L.
        • Fu Y.
        • et al.
        A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy.
        Endoscopy. 2021; 53: 469-477
        • Tang D.
        • Zhou J.
        • Wang L.
        • et al.
        A novel model based on deep convolutional neural network improves diagnostic accuracy of intramucosal gastric cancer (with video).
        Front Oncol. 2021; 11: 622827
        • Xu M.
        • Zhou W.
        • Wu L.
        • et al.
        Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video).
        Gastrointest Endosc. 2021; 94: 540-548.e4
        • Axon A.
        Is diagnostic and therapeutic endoscopy currently appropriate? Suggestions for improvement.
        Best Pract Res Clin Gastroenterol. 2008; 22: 959-970
        • Le Berre C.
        • Sandborn W.J.
        • Aridhi S.
        • et al.
        Application of artificial intelligence to gastroenterology and hepatology.
        Gastroenterology. 2020; 158: 76-94
        • Li L.
        • Chen Y.
        • Shen Z.
        • et al.
        Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging.
        Gastric Cancer. 2020; 23: 126-132
        • Hu H.
        • Gong L.
        • Dong D.
        • et al.
        Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study.
        Gastrointest Endosc. 2021; 93: 1333-1341
        • Yao K.
        • Uedo N.
        • Kamada T.
        • et al.
        Guidelines for endoscopic diagnosis of early gastric cancer.
        Dig Endosc. 2020; 32: 663-698
        • Yao K.
        The endoscopic diagnosis of early gastric cancer.
        Ann Gastroenterol. 2013; 26: 11-22
      1. Japanese gastric cancer treatment guidelines 2010 (ver. 3).
        Gastric Cancer. 2011; 14: 113-123
        • Ikenoyama Y.
        • Hirasawa T.
        • Ishioka M.
        • et al.
        Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists.
        Dig Endosc. 2021; 33: 141-150
        • Ezoe Y.
        • Muto M.
        • Uedo N.
        • et al.
        Magnifying narrowband imaging is more accurate than conventional white-light imaging in diagnosis of gastric mucosal cancer.
        Gastroenterology. 2011; 141: 2017-2025

      References

        • Wu L.
        • Zhou W.
        • Wan X.
        • et al.
        A deep neural network improves endoscopic detection of early gastric cancer without blind spots.
        Endoscopy. 2019; 51: 522-531
        • Chernyi S.
        The implementation of technology of multi-user client-server applications for systems of decision making support.
        Metallurg Mining Indust. 2015;
      1. Wen Z-K, Zhu W-Z, Ouyang J, et al. A robust and discriminative image perceptual hash algorithm. Presented at the 2010 Fourth International Conference on Genetic and Evolutionary Computing, 2010. p. 709-712.

      Linked Article

      • Can artificial intelligence be your angel to diagnose early gastric cancer in real clinical practice?
        Gastrointestinal EndoscopyVol. 95Issue 4
        • Preview
          Early gastric cancer (EGC), which is defined as a gastric cancer confined to mucosa or submucosa regardless of lymph node metastasis, is known to be curable because of its excellent disease-specific survival: >95% after surgery.1 When a lesion is adequately selected, endoscopic resection, specifically endoscopic submucosal dissection, is an organ-preserving approach, which provides excellent long-term outcomes comparable with those of gastrectomy with lymph node dissection.2,3 ESD is now considered the standard of care for EGC that has little risk of lymph node metastasis.
        • Full-Text
        • PDF