[Comparison and analysis of three classifying models for discrimination of lung cancer established by 6 tumor markers].
OBJECTIVE
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.


METHODS
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.


RESULTS
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).


CONCLUSIONS
The effects of ANN, CART models established by 6 tumor markers were better than that of Fisher discrimination analysis in discrimination of lung cancer.
Feng
Feifei
F
College of Public Health, Zhengzhou University, Zhengzhou 450001, China. heartnessff@126.com
Nie
Guangjin
G
Wu
Yongjun
Y
Wu
Yiming
Y
D014408:Biomarkers, Tumor / Q000097:blood*; D002272:Carcinoembryonic Antigen / Q000097:blood; D002289:Carcinoma, Non-Small-Cell Lung / Q000097:blood / Q000175:diagnosis; D003663:Decision Trees; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005755:Gastrins / Q000097:blood; D006801:Humans; D008175:Lung Neoplasms / Q000097:blood / Q000175:diagnosis*; D019158:N-Acetylneuraminic Acid / Q000097:blood; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity; D055752:Small Cell Lung Carcinoma / Q000097:blood / Q000175:diagnosis
D014408:Biomarkers, Tumor; D002272:Carcinoembryonic Antigen; D005755:Gastrins; D019158:N-Acetylneuraminic Acid