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Abstract：
Little is known about esophageal high-grade intraepithelial neoplasia dominated by cytological atypia (HGINc). We aimed to elucidate the endoscopic features of HGINc compared with esophageal high-grade intraepithelial neoplasia dominated by architectural atypia (HGINa). All patients pathologically diagnosed as esophageal high-grade intraepithelial neoplasia after endoscopic submucosal dissection at our center between January 2018 and December 2019 were included in this study. According to the pathological diagnosis, the patients were divided into two groups: HGINa group and HGINc group. Basic characteristics and endoscopic information were collected in detail. Data were analyzed statistically. Binary logistic regression was performed and a predictive model for HGINc was established. Then we evaluated its predictive value and built a nomogram for clinical application. A total of 175 patients were included in this study (126 with HGINa and 49 with HGINc). Among 228 lesions found in all patients, there were 148 HGINa and 80 HGINc. The independent relevant factors for HGINc were tobacco and alcohol usage, color, and gross type. To predict risk of HGINc, a three-factor model (TFM) was established with a highest area under curve (AUC) as 0.869 (95% CI, 0.852, 0.939). When the cut-off value was set as 0.3569184, the diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for HGINc was 81.14%, 88.75%, 77.03%, 67.62%, and 92.68%, respectively. HGINc differs greatly in endoscopic features from HGINa in our study. It\'s important to reduce misdiagnosis that our model was established with good predictive value for clinical application.
摘要：
对以细胞学异型性（HGINc）为主的食管高级别上皮内瘤变知之甚少。我们旨在阐明HGINc与以建筑异型症为主导的食管高级别上皮内瘤变（HGINa）相比的内镜特征。所有2018年1月至2019年12月在本中心内镜黏膜下剥离术后病理诊断为食管高级别上皮内瘤变的患者均纳入本研究。根据病理诊断,将患者分为两组：HGINa组和HGINc组。详细收集了基本特征和内镜信息。对数据进行统计分析。进行二元logistic回归，建立HGINc的预测模型。然后，我们评估了其预测价值，并建立了临床应用的列线图。本研究共纳入175例患者(126例HGINa患者和49例HGINc患者)。在所有患者中发现的228个病变中，有148Hgina和80HgINc。HGINc的独立相关因素是烟草和酒精的使用，颜色,和粗体类型。为了预测HGINc的风险，建立三因素模型(TFM),曲线下面积(AUC)最高为0.869(95%CI,0.852,0.939).当截止值设置为0.3569184时，诊断的准确性，灵敏度,特异性，正预测值，HGINc的阴性预测值为81.14%，88.75%,77.03%,67.62%,92.68%，分别。在我们的研究中，HGINc在内窥镜特征上与HGINa有很大不同。建立的模型具有良好的临床应用预测价值,对减少误诊具有重要意义。
