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A nomogram composed of clinicopathologic features and preoperative serum tumor markers to predict lymph node metastasis in early gastric cancer patients

机译:由临床病理特征和术前血清肿瘤标志物组成的诺模图预测早期胃癌患者的淋巴结转移

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摘要

Predicting lymph node metastasis (LNM) accurately is of great importance to formulate optimal treatment strategies preoperatively for patients with early gastric cancer (EGC). This study aimed to explore risk factors that predict the presence of LNM in EGC. A total of 697 patients underwent gastrectomy enrolled in this study, were divided into training and validation set, and the relationship between LNM and other clinicopathologic features, preoperative serum combined tumor markers (CEA, CA19-9, CA125) were evaluated. Risk factors for LNM were identified using logistic regression analysis, and a nomogram was created by R program to predict the possibility of LNM in training set, while receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in validation set. Consequently, LNM was significantly associated with tumor size, macroscopic type, differentiation type, ulcerative findings, lymphovascular invasion, depth of invasion and combined tumor marker. In multivariate logistic regression analysis, factors including of tumor size, differentiation type, ulcerative findings, lymphovascular invasion, depth of invasion and combined tumor marker were demonstrated to be independent risk factors for LNM. Moreover, a predictive nomogram with these independent factors for LNM in EGC patients was constructed, and ROC curve demonstrated a good discrimination ability with the AUC of 0.847 (95% CI: 0.789-0.923), which was significantly larger than those produced in previous studies. Therefore, including of these tumor markers which could be convenient and feasible to obtain from the serum preoperatively, the nomogram could effectively predict the incidence of LNM for EGC patients.
机译:准确地预测淋巴结转移(LNM)对于早期胃癌(EGC)患者的术前制定最佳治疗策略至关重要。这项研究旨在探讨预测EGC中LNM的存在的危险因素。本研究共纳入697例行胃切除术的患者,分为训练和验证集,并评估LNM与其他临床病理特征,术前血清联合肿瘤标志物(CEA,CA19-9,CA125)之间的关系。使用逻辑回归分析确定了LNM的危险因素,并通过R程序创建了诺模图以预测训练集中LNM的可能性,同时应用接收器工作特征(ROC)分析评估诺模图模型在验证中的预测价值组。因此,LNM与肿瘤的大小,宏观类型,分化类型,溃疡性表现,淋巴血管浸润,浸润深度和组合的肿瘤标志物显着相关。在多因素logistic回归分析中,肿瘤大小,分化类型,溃疡性发现,淋巴管浸润,浸润深度和组合的肿瘤标志物等因素被证明是LNM的独立危险因素。此外,构建了具有这些独立因素的EGC患者LNM的预测列线图,ROC曲线显示出良好的辨别能力,AUC为0.847(95%CI:0.789-0.923),明显大于以前的研究结果。 。因此,包括这些术前可以方便,可行地从血清中获得的肿瘤标志物,列线图可以有效地预测EGC患者LNM的发生率。

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