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Analysis of factors related to poor outcome after e-learning training in endoscopic diagnosis of early gastric cancer using magnifying narrow-band imaging

Published:April 26, 2019DOI:https://doi.org/10.1016/j.gie.2019.04.230

      Background and Aims

      An e-learning system teaching endoscopic diagnostic process for early gastric cancer using magnifying endoscopy with narrow-band imaging (M-NBI) was established, and its efficacy in improving the diagnostic performance for early gastric cancer was proven in a multicenter randomized controlled trial. The aim of this study was to clarify the difference in learning effect in each lesion characteristic.

      Methods

      Three hundred sixty-five participants diagnosed 40 gastric lesions based on M-NBI findings using the vessel-plus-surface classification system. The diagnosis data collected from each participant were assessed in this study. The accuracy of NBI cancer diagnosis was assessed using area under the receiver operating characteristics curve (AUC/ROC) analysis. AUC/ROCs were separately calculated in each lesion characteristic (shape and size), and the data were compared between tests 1 and 3.

      Results

      Continuous net reclassification improvement (cNRI) analysis of all lesions revealed significant improvement in reclassification when participants underwent e-learning (cNRI, 1.17; P < .01). The integrated discrimination improvement analysis demonstrated that the e-learning system improved diagnostic ability (.19; P < .01). According to the analysis depending on the lesion’s characteristics, high AUC/ROCs were demonstrated in depressed and small lesions (<10 mm; .90 and .93, respectively). The cNRI analysis showed remarkable e-learning improvement in both depressed (cNRI, 1.33; P < .01) and small lesions (cNRI, 1.46; P < .01). However, no significant e-learning improvement was observed in elevated or flat lesions.

      Conclusions

      In M-NBI education for endoscopists, a good learning outcome was obtained in depressed and small lesions, but a poor learning outcome was demonstrated in elevated and flat lesions. (Clinical trial registration number: UMIN000008569.)

      Abbreviations:

      AUC (area under the curve), AUC/ROC (area under the receiver operating characteristic curve), cNRI (continuous net reclassification improvement), DL (demarcation line), EGC (early gastric cancer), IDI (integrated discrimination improvement), MS (microsurface), M-NBI (magnifying endoscopy with narrow-band imaging), MV (microvascular), VSCS (vessel-plus-surface classification system), WOS (white opaque substance)
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      References

        • Ferlay J.
        • Soerjomataram I.
        • Dikshit R.
        • et al.
        Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.
        Int J Cancer. 2015; 136: E359-E386
        • Hamashima C.
        • Okamoto M.
        • Shabana M.
        • et al.
        Sensitivity of endoscopic screening for gastric cancer by the incidence method.
        Int J Cancer. 2013; 133: 653-659
        • Gono K.
        • Yamazaki K.
        • Douguchi N.
        • et al.
        Endoscopic observation of tissue by narrow band illumination.
        Opt Rev. 2003; 10: 211-215
        • Gono K.
        • Obi T.
        • Yamaguchi M.
        • et al.
        Appearance of enhanced tissue features in narrow band endoscopic imaging.
        J Biomed Opt. 2004; 9: 568-577
        • Yao K.
        • Oishi T.
        • Matsui T.
        • et al.
        Novel magnified endoscopic findings of microvascular architecture in intramucosal gastric cancer.
        Gastrointest Endosc. 2002; 56: 279-284
        • Yao K.
        • Anagnostopoulos G.K.
        • Ragunath K.
        Magnifying endoscopy for diagnosing and delineating early gastric cancer.
        Endoscopy. 2009; 41: 462-467
        • 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
        • Nakanishi
        • 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
        • Schulz K.F.
        • Altman D.G.
        • Moher D.
        • et al.
        CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials.
        BMC Med. 2010; 8: 18
        • World Medical Association
        World Medical Association Declaration of Helsinki ethical principles for medical research involving human subjects.
        JAMA. 2013; 310: 2191-2194
        • Pencina M.J.
        • D’Agostino Sr., R.B.
        • D’Agostino Jr., R.B.
        • et al.
        Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.
        Stat Med. 2008; 27157207
        • Fleiss J.L.
        Measuring nominal scale agreement among many raters.
        Psychol Bull. 1971; 76: 378-382
        • R Development Core Team
        R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
        (Available at:)
        http://www.R-project.org
        Date accessed: September 13, 2016
        • Yao K.
        • Doyama H.
        • Gotoda T.
        • et al.
        Diagnostic performance and limitations of magnifying narrow-band imaging in screening endoscopy of early gastric cancer: a prospective multicenter feasibility study.
        Gastric Cancer. 2014; 17: 669-679
        • Yao K.
        • Iwashita A.
        • Tanabe H.
        • et al.
        White opaque substance within superficial elevated gastric neoplasia as visualized by magnification endoscopy with narrow-band imaging: a new optical sign for differentiating between adenoma and carcinoma.
        Gastrointest Endosc. 2008; 68: 574-580
        • Yao K.
        • Iwashita A.
        • Nambu M.
        • et al.
        Nature of white opaque substance in gastric epithelial neoplasia as visualized by magnifying endoscopy with narrow-band imaging.
        Dig Endosc. 2012; 24: 419-425
        • Zhu Y.
        • Wang Q.C.
        • Xu M.D.
        • et al.
        Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy.
        Gastrointest Endosc. 2019; 89: 806-815
        • 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