Feng Feifei, Nie Guangjin, Wu Yongjun, Wu Yiming
College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Wei Sheng Yan Jiu. 2009 Jul;38(4):429-32.
To distinguish lung cancer by detecting 6 tumor markers in serum and establishing three classifying models of artificial neural networks (ANN), decision tree (CART), Fisher discrimination analysis, and to compare the differences among three models.
The levels of serum CEA, gastrin, NSE, sialic acid (SA), Cu/ Zn, Ca in 50 healthy individuals, 40 patients with lung benign disease and 50 patients with lung cancers were detected by means of radioimmunology, spectrophotometry, atomic absorption spectrophotometry, respectively, and then developed ANN, CART and Fisher discrimination analysis models.
The sensitivity of ANN, CART and Fisher discrimination analysis models were 100%, 93.33%, 84.00%, the specificity were 100%, 100%, 98.89%, the accuracy were 91.67%, 86.11%, 85.00%. The areas under receiver operating curve (AUROC) of ANN, CART and Fisher discrimination analysis models were 0.964, 0.953, 0.812, respectively. There was no significantly statistical difference between ANN and CART (P > 0.05), while there were significantly statistical differences not only between Fisher discrimination analysis and ANN, but also Fisher discrimination analysis and CART (P < 0.05).
The effects of ANN, CART models established by 6 tumor markers were better than that of Fisher discrimination analysis in discrimination of lung cancer.
通过检测血清中6种肿瘤标志物并建立人工神经网络（ANN）、决策树（CART）、Fisher判别分析三种分类模型来鉴别肺癌，并比较三种模型之间的差异。
分别采用放射免疫法、分光光度法、原子吸收分光光度法检测50例健康人、40例肺良性疾病患者及50例肺癌患者血清中癌胚抗原（CEA）、胃泌素、神经元特异性烯醇化酶（NSE）、唾液酸（SA）、铜/锌、钙的水平，然后建立ANN、CART和Fisher判别分析模型。
ANN、CART和Fisher判别分析模型的灵敏度分别为100%、93.33%、84.00%，特异度分别为100%、100%、98.89%，准确度分别为91.67%、86.11%、85.00%。ANN、CART和Fisher判别分析模型的受试者工作特征曲线下面积（AUROC）分别为0.964、0.953、0.812。ANN与CART之间差异无统计学意义（P>0.05），而Fisher判别分析与ANN之间、Fisher判别分析与CART之间差异均有统计学意义（P<0.05）。
由6种肿瘤标志物建立的ANN、CART模型在鉴别肺癌方面的效果优于Fisher判别分析。
