摘要
目的探索多项肿瘤标志物联合检测对肺癌的诊断价值,建立适宜肿瘤标志物检验策略。方法测定280例原发性肺癌(PLC)患者和455例肺良性疾病(BPD)患者血清甲胎蛋白(AFP)、癌胚抗原(CEA)、糖类抗原(CA)50、CA242、CA125、CA15-3、CA19-9、细胞角蛋白19片段(CYFRA 21-1)、CA72-4、鳞状上皮细胞癌抗原(SCC-Ag)、神经元特异性烯醇化酶(NSE)和肿瘤特异性生长因子(TSGF)。采用受试者工作特征(ROC)曲线分析各项指标的诊断价值,并采用探索性因子分析(EFA)和Logistic回归模型逐步探索、优化多项肿瘤标志物联合检测策略。结果除TSGF外,PLC组CEA、CA15-3、CA72-4、CA242、CYFRA21-1、CA125、CA19-9、CA50、SCC-Ag、NSE和AFP水平均明显高于BPD组(P<0.01)。ROC曲线分析显示,PLC组CA15-3、NSE、CA125、CEA、CYFRA 21-1、CA72-4的曲线下面积(AUC)≥0.7,TSGF、CA19-9、CA242、AFP、CA50、SCC-Ag的AUC<0.7。EFA结果显示,剔除TSGF后PLC组获得4个独立的公共因子,综合评估每个因子内的肿瘤标志物,筛选出PLC预测模型(由CA125、CA19-9、CYFRA 21-1、NSE、SCC-Ag、CEA、CA153组成)。PLC预测模型的诊断价值与11项指标联合检测的诊断价值相当(Z=1.744,P=0.081)。PLC预测模型的AUC为0.831,敏感性为70.7%,特异性为83.7%,阳性似然比(+LR)为4.35,阴性似然比(-LR)为0.35,最佳临界值为-0.958 8,诊断价值优于单项指标。结论 PLC预测模型与单项肿瘤标志物检测相比,平衡了敏感性和特异性,检测指标的减少并未降低诊断效能,具有一定的临床应用价值。
Objective To investigate the role of combined determination of multiple tumor markers in the diagnosis of lung cancer,and to establish appropriate strategies of tumor marker determinations.Methods A total of 280 patients with primary lung cancer(PLC)and 455 patients with benign pulmonary disease(BPD)were enrolled.Serum alpha-fetoprotein(AFP),carcinoembryonic antigen(CEA),carbohydrate antigen(CA)50,CA242,CA125,CA15-3,CA19-9,cytokeratin 19 fragment(CYFRA 21-1),CA72-4,squamous cell carcinoma antigen(SCC-Ag),neuron-specific enolase(NSE)and tumor specific growth factor(TSGF)were determined.Receiver operating characteristic(ROC)curve was used to evaluate diagnosis performance.Exploratory factor analysis(EFA)and Logistic regression model were used to evaluate the role of combined determination.Results The levels of CEA,CA15-3,CA72-4,CA242,CYFRA 21-1,CA125,CA19-9,CA50,SCC-Ag,NSE and AFP in PLC group were higher than those in BPD group(P<0.01),except for TSGF.ROC curve analysis showed that the areas under curves(AUC)of CA15-3,NSE,CA125,CEA,CYFRA 21-1 and CA72-4 in PLC group were≥0.7,while the AUC of TSGF,CA19-9,CA242,AFP,CA50 and SCC-Ag were<0.7.After excluding TSGF,EFA showed that there were 4 independent potential factors in PLC group.The independent potential factors were evaluated by comprehensive evaluation in order to give PLC predictive model,which was made up of CA125,CA19-9,CYFRA 21-1,NSE,SCC-Ag,CEA and CA15-3.The diagnosis performance of PLC predictive model and the combined determination of 11 tumor markers had no difference(Z=1.744,P=0.081).The PLC predictive model had better diagnosis performance comparing to that of any single tumor marker determination,having the AUC of 0.831,the sensitivity of 70.7%and the specificity of 83.7%,and the positive likelihood ratio(+LR)and negative likelihood ratio(-LR)were 4.35 and 0.35,respectively.The optimal cut-off value was-0.958 8.Conclusions Comparing to single tumor marker determinations,the PLC predictive model reached a balance between sensitivity and specificity.The diagnosis performance of PLC predictive model is not decreased with the reduction of tumor marker number.
作者
张海晨
王浩
宋云霄
马进
ZHANG Haichen;WANG Hao;SONG Yunxiao;MA Jin(Department of Clinical Laboratory,Shanghai Xuhui Central Hospital,Shanghai 200031,China;Department of Clinical Laboratory,Jiangxi Mental Hospital,Nanchang 330029,Jiangxi,China;School of Public Health,Shanghai Jiaotong University School of Medicine,Shanghai 200025,China)
出处
《检验医学》
CAS
2018年第7期590-596,共7页
Laboratory Medicine
基金
上海市卫生与计划生育委员会资助项目(201540238)
