郑州大学学报（医学版）Issue(5)：658-661,4.DOI:10.13705/j.issn.1671-6825.2014.05.017
基于肿瘤标志群的人工神经网络模型对肺癌辅助诊断的价值
Value of artificial neural network combined with optimal biomarkers in diagnosis of lung cancer
摘要
Abstract
Aim:To establish the model by artificial neural network ( ANN ) technology combined with tumor marker protein chip for the diagnosis of lung cancer ,and to explore the diagnosis value of artificial intelligence model .Methods:Protein chips based on chemiluminescence were used to measure the levels of nine serum tumor markers (CA199,Ferritin, AFP,CA153,CEA,NSE,CA242,CA125,HGH) in 201 cases of benign lung diseases and 203 cases of lung cancer.Multi-variate logistic regression was employed to optimize the tumor marker group .ANN and Fisher discriminant analysis was used to develop the two diagnostic model of lung cancer .Results:Based on the optimal four tumor markers ( CEA,NSE,Ferritin, CA153),area under the ROC curve of ANN model (0.850) was higher than those of the Fisher discriminant analysis based on the optimal four and six tumor markers (CEA,NSE,Ferritin,CA153,AFP,CA125) as well as ANN model based on the optimal six tumor markers(0.793,0.767 and 0.825).Conclusion:Based on the four kinds of tumor markers in the diagno-sis of lung cancer ,ANN model is better than Fisher discriminant analysis .ANN model established by six tumor markers is superior to Fisher discriminant analysis .关键词
肺癌/肿瘤标志/人工神经网络/Fisher判别分析/辅助诊断Key words
lung cancer/tumor marker/artificial neural network/Fisher discriminant analysis/auxiliary diagnosis分类
医药卫生引用本文复制引用
李尊税,魏小玲,何其栋,张红巧,吴拥军..基于肿瘤标志群的人工神经网络模型对肺癌辅助诊断的价值[J].郑州大学学报（医学版）,2014,(5):658-661,4.基金项目
国家自然科学基金资助项目30972457，81001239；河南省重大科技攻关项目112102310102；河南省医学科技攻关计划项目2011020082 （）
