【摘要】 目的 比较5个联合诊断模型对诊断非小细胞肺癌（NSCLC）的应用价值。方法 回顾性选取2015年1月11日至2019年10月28日电子科技大学医学院附属绵阳医院413例NSCLC患者作为肺癌组，选取同期723例肺部良性疾病者和282例健康体检者分别为良性组和健康对照组。测定血清癌胚抗原，甲胎蛋白，糖类抗原125，糖类抗原199和神经元特异性烯醇化酶，联合建立多层感知人工神经网络（MPL-ANN）、径向基函数神经网络（RBFANN）、决策树、logistic回归和经典判别分析（CDA）模型并比较各模型的诊断效能。结果 单项指标中癌胚抗原对NSCLC的诊断价值较高，其AUC为0.76(95%CI:0.74～0.78)，但其特异度较低，为64.9%。所有联合诊断模型中，MPL-ANN模型最佳，其AUC为0.91(95%CI:0.89～0.96)，诊断NSCLC的灵敏度为75.3%，特异度为91.1%，对肺癌组和非肺癌组（良性组+对照组）的诊断正确率分别为71.7%(76/106)和94.4%(305/323)。结论 采用肿瘤标志物建立的MPL-ANN模型，能较好地诊断和预测NSCLC，为NSCLC的鉴别诊断提供了一种新思路。更多还原

【Abstract】 Objective To compare the application value of five combined patterns for diagnosing non-small cell lung cancer(NSCLC). Methods A total of 413 NSCLC patients in Mianyang Central Hospital Affiliated to School of Medicine,University of Electronic Science and Technology of China from January 11, 2015 to October 28, 2019 were retrospectively selected as the lung cancer group, a total of 723 patients with benign pulmonary disease and 282 healthy subjects were selected as benign group and healthy control group respectively. Serum carcinoembryonic antigen(CEA), alpha fetoprotein(AFP), carbohydrate antigen 125(CA125), carbohydrate antigen 199(CA199) and neuron-specific enolase(NSE) were measured, and were used to develop the models of multiplayer artificial neural network(MPL-ANN), radial basis function artificial neural network(RBF-ANN), decision tree, logistic regression and classical discriminant analysis(CDA). Then the diagnostic efficacy of each model was compared. Results Among the single indicators, CEA had the highest diagnostic value for NSCLC, with the AUC of 0.76(95%CI: 0.74-0.78), but its specificity was low(64.9%). Of all the diagnostic models for the diagnosis of NSCLC, MPL-ANN model was the optimal with 0.91(95%CI: 0.89-0.96)of AUC, 75.3% of sensitivity, 91.1% of specificity, as well as 71.7%(76/106) and 94.4%(305/323) of diagnosis accuracy for the lung cancer group and non-lung cancer group, respectively. Conclusion The MPL-ANN model based on tumor markers measurement can be helpful for the diagnosis of NSCLC, which provides a new idea for the differential diagnosis of NSCLC.更多还原