Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan.
Department of Gastroenterology, Ishikawa Prefectural Central Hospital, Ishikawa, Japan.
Gastrointest Endosc. 2019 Sep;90(3):440-447.e1. doi: 10.1016/j.gie.2019.04.230. Epub 2019 Apr 26.
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.
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.
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.
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.).
建立了一个使用窄带成像放大内镜（M-NBI）教授内镜早期胃癌诊断过程的电子学习系统，并在一项多中心随机对照试验中证明了其提高早期胃癌诊断性能的功效。本研究的目的是阐明每个病变特征的学习效果差异。
365 名参与者根据 M-NBI 发现使用血管-表面分类系统诊断了 40 个胃病变。本研究评估了从每个参与者收集的诊断数据。使用受试者工作特征曲线下面积（AUC/ROC）分析评估 NBI 癌症诊断的准确性。分别在每个病变特征（形状和大小）中计算 AUC/ROC，并比较测试 1 和 3 之间的数据。
对所有病变进行连续净重新分类改善（cNRI）分析显示，参与者接受电子学习后重新分类有显著改善（cNRI，1.17；P<.01）。综合鉴别力改善分析表明，电子学习系统提高了诊断能力（.19；P<.01）。根据病变特征的分析，凹陷和小病变（<10mm；分别为.90 和.93）的 AUC/ROC 较高。cNRI 分析显示凹陷（cNRI，1.33；P<.01）和小病变（cNRI，1.46；P<.01）均有显著的电子学习改善。然而，在隆起或平坦病变中没有观察到显著的电子学习改善。
在 M-NBI 内镜医生教育中，凹陷和小病变的学习效果较好，但隆起和平坦病变的学习效果较差。（临床试验注册号：UMIN000008569.）。
