Abstract：
The discrepancies between the diagnosis of preoperative endoscopic forceps biopsy (EFB) and endoscopic submucosal dissection (ESD) in patients with early gastric neoplasm (EGN) exist objectively. Among them, pathological upgrading directly influences the accuracy and appropriateness of clinical decisions. The aims of this study were to investigate the risk factors for the discrepancies, with a particular focus on pathological upgrading and to establish a prediction model for estimating the risk of pathological upgrading after EFB.
We retrospectively collected the records of 978 patients who underwent ESD from December 1, 2017 to July 31, 2021 and who had a final histopathology determination of EGN. A nomogram to predict the risk of pathological upgrading was constructed after analyzing subgroup differences among the 901 lesions enrolled.
The ratio of pathological upgrading was 510 of 953 (53.5%). Clinical, laboratorial and endoscopic characteristics were analyzed using univariable and binary multivariable logistic regression analyses. A nomogram was constructed by including age, history of chronic atrophic gastritis, symptoms of digestive system, blood high density lipoprotein concentration, macroscopic type, pathological diagnosis of EFB, uneven surface, remarkable redness, and lesion size. The C-statistics were 0.804 (95% confidence interval, 0.774 to 0.834) and 0.748 (95% confidence interval, 0.664 to 0.832) in the training and validation set, respectively. We also built an online webserver based on the proposed nomogram for convenient clinical use.
The clinical value of identifying the preoperative diagnosis of EGN lesions is limited when using EFB separately. We have developed a nomogram that can predict the probability of pathological upgrading with good calibration and discrimination value.
We retrospectively collected the records of 978 patients who underwent ESD from December 1, 2017 to July 31, 2021 and who had a final histopathology determination of EGN. A nomogram to predict the risk of pathological upgrading was constructed after analyzing subgroup differences among the 901 lesions enrolled.
The ratio of pathological upgrading was 510 of 953 (53.5%). Clinical, laboratorial and endoscopic characteristics were analyzed using univariable and binary multivariable logistic regression analyses. A nomogram was constructed by including age, history of chronic atrophic gastritis, symptoms of digestive system, blood high density lipoprotein concentration, macroscopic type, pathological diagnosis of EFB, uneven surface, remarkable redness, and lesion size. The C-statistics were 0.804 (95% confidence interval, 0.774 to 0.834) and 0.748 (95% confidence interval, 0.664 to 0.832) in the training and validation set, respectively. We also built an online webserver based on the proposed nomogram for convenient clinical use.
The clinical value of identifying the preoperative diagnosis of EGN lesions is limited when using EFB separately. We have developed a nomogram that can predict the probability of pathological upgrading with good calibration and discrimination value.
摘要：
UNASSIGNED:早期胃肿瘤(EGN)患者术前内镜钳活检(EFB)与内镜黏膜下剥离术(ESD)诊断存在客观存在差异。其中,病理升级直接影响临床决策的准确性和适当性。这项研究的目的是调查差异的风险因素，特别关注病理升级，并建立EFB后病理升级风险的预测模型。
UNASSIGNED：我们回顾性收集了从2017年12月1日至2021年7月31日接受ESD且最终组织病理学确定为EGN的978例患者的记录。在分析了901个病变之间的亚组差异后，构建了预测病理升级风险的列线图。
UNASSIGNED：病理升级的比率为953人中的510人（53.5%）。临床,使用单变量和二元多变量逻辑回归分析对实验室和内镜特征进行分析.通过包括年龄，慢性萎缩性胃炎病史，消化系统的症状，血高密度脂蛋白浓度，宏观型,EFB的病理诊断，表面不平整,显著的红肿,和病变大小。C统计量为0.804(95%置信区间，0.774至0.834)和0.748(95%置信区间，0.664至0.832)在训练和验证集中，分别。我们还基于建议的列线图构建了一个在线网络服务器，以便于临床使用。
UNASSIGNED：单独使用EFB时，确定EGN病变的术前诊断的临床价值有限。我们开发了一种列线图，可以预测具有良好校准和判别值的病理升级概率。
UNASSIGNED：我们回顾性收集了从2017年12月1日至2021年7月31日接受ESD且最终组织病理学确定为EGN的978例患者的记录。在分析了901个病变之间的亚组差异后，构建了预测病理升级风险的列线图。
UNASSIGNED：病理升级的比率为953人中的510人（53.5%）。临床,使用单变量和二元多变量逻辑回归分析对实验室和内镜特征进行分析.通过包括年龄，慢性萎缩性胃炎病史，消化系统的症状，血高密度脂蛋白浓度，宏观型,EFB的病理诊断，表面不平整,显著的红肿,和病变大小。C统计量为0.804(95%置信区间，0.774至0.834)和0.748(95%置信区间，0.664至0.832)在训练和验证集中，分别。我们还基于建议的列线图构建了一个在线网络服务器，以便于临床使用。
UNASSIGNED：单独使用EFB时，确定EGN病变的术前诊断的临床价值有限。我们开发了一种列线图，可以预测具有良好校准和判别值的病理升级概率。
