基于炎症及糖类抗原125建立术前预测子宫内膜癌病理分期及分级的列线图预测模型

A Nomogram Prediction Model for Predicting Pathological Stage and Grade of Endometrial Carcinoma Based on Inflammation and Cancer Antigen 125

目的探讨炎症指标、糖类抗原125(CA125)对术前子宫内膜癌(EC)病理分期及分级的预测价值。方法选取2021-2023年收治的214例EC患者作为研究对象,随机分为训练集(134例)与验证集(80例),测定炎症指标[中性粒细胞与淋巴细胞的比值(NLR)、血小板与淋巴细胞的比值(PLR)、全身免疫炎症指数(SII)]、CA125水平,评估训练集EC患者病理分期及分级,比较不同病理分期及分级患者炎症指标、CA125水平差异,利用R软件建立列线图预测模型,并由验证集验证,分析炎症指标、CA125对术前EC病理分期及分级的预测价值。结果训练集中TNM病理分期Ⅰ期120例、Ⅱ期4例、Ⅲ期10例,国际妇产科联盟(FIGO)分级1级16例、2级79例、3级39例。TNM病理分期Ⅱ、Ⅲ期的EC患者NLR、PLR、CA125均高于TNM病理分期Ⅰ期的患者(P<0.05),FIGO分级2、3级的EC患者NLR、PLR、CA125均高于FIGO分级1级的患者(P<0.05)。基于NLR、PLR、SII、CA125建立训练集EC患者TNM病理分期及FIGO分级的列线图预测模型,经Bootstrap法验证模型区分度,绘制校准曲线,结果显示,训练集与验证集校准曲线Y与X直线相近,列线图模型区分度好;绘制ROC曲线发现,训练集与验证集列线图模型预测术前EC高分期、高病理分级风险的AUC>0.90,有较高的预测价值。结论基于炎症及CA125建立的列线图预测模型对术前EC的病理分期及分级具有较高的预测价值。

Objective To investigate the predictive value of inflammatory markers and Cancer antigen 125(CA125)for preoperative pathological staging and grading of endometrial carcinoma(EC).Methods A selection of 214 EC patients admitted from 2021-2023,randomized into the training set(134 cases)and validation set(80 cases).Inflammatory markers(NLR,PLR,SII)and CA125 levels were measured in all patients.The pathological and stages grades of EC patients were evaluated,and the differences in inflammatory markers and CA125 levels among patients with different pathological stages and grades were compared,Establish a column chart prediction model to analyze the predictive value of inflammation indicators and CA125 for preoperative early EC pathological and stages grades.Results There were 120 cases of TNM stageⅠ,4 cases of stageⅡand 10 cases of stageⅢin the training set.16 cases of International Federation of Gynecology and Obstetrics(FIGO)grade 1,79 cases of grade 2 and 39 cases of grade 3 in the training set.NLR,PLR,and CA125 in EC patients with TNM pathological stagesⅡandⅢwere higher than those TNM in pathological stage I(P<0.05).NLR,PLR,and CA125 in EC patients with FIGO grades 2 and 3 were higher than those in FIGO grade 1(P<0.05).Based on NLR,PLR,SII and CA125,a nomogram prediction model for EC TNM pathological stage and FIGO grade in the training set was established.Bootstrap method was used to verify the model discrimination and draw the calibration curve.The results showed that the calibration curve Y and X lines of the training set and the validation set were similar,and the nomogram model discrimination was good.The ROC curve revealed that the AUC of the nomogram model between the training set and the validation set for predicting the risk of high preoperative EC stage and high pathological grade was>0.90,which had a high predictive value.Conclusion The nomogram prediction model based on inflammation and CA125 has a high predictive value for the pathological stage and grade of preoperative EC.