5个联合诊断模型对非小细胞肺癌诊断价值的比较

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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.

Key words： Non-small cell lung cancer Multiplayer artificial neural network Radial basis function artificial neural network Decision tree Classical discriminant analysis Logistic regression

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	Articles by authors
	CHEN Xiaohong
	YANG Yuwei
	XU Peng
	YANG Wu

Cite this article:

CHEN Xiaohong,YANG Yuwei,XU Peng, et al. Comparison of five diagnostic models in the diagnosis of non-small cell lung cancer[J]. 中国当代医药, 2022, 29(2): 7-10.

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http://dangdaiyiyao.com/EN/ OR http://dangdaiyiyao.com/EN/Y2022/V29/I2/7

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