郑州大学学报（医学版）
鄭州大學學報（醫學版）
정주대학학보（의학판）
JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES)
2014年
5期
658-661
,共4页
李尊税%魏小玲%何其栋%张红巧%吴拥军
李尊稅%魏小玲%何其棟%張紅巧%吳擁軍
리존세%위소령%하기동%장홍교%오옹군
肺癌%肿瘤标志%人工神经网络%Fisher判别分析%辅助诊断
肺癌%腫瘤標誌%人工神經網絡%Fisher判彆分析%輔助診斷
폐암%종류표지%인공신경망락%Fisher판별분석%보조진단
lung cancer%tumor marker%artificial neural network%Fisher discriminant analysis%auxiliary diagnosis
目的：应用人工神经网络（ ANN）技术联合肿瘤标志蛋白芯片建立人工智能辅助诊断模型，探讨其对肺癌诊断的价值。方法：采用蛋白芯片（化学发光法）测定201例肺良性疾病患者、202例肺癌患者血清中9项血清肿瘤标志（ CA199、Ferritin、AFP、CA153、CEA、NSE、CA242、CA125、HGH）的水平，logistic回归筛选，建立ANN和Fisher判别分析肺癌诊断模型。结果：4项肿瘤标志（ CEA、NSE、Ferritin、CA153）建立的ANN模型的ROC曲线下面积（0．850）高于4项 Fisher、6项（CEA、NSE、Ferritin、CA153、AFP、CA125） Fisher 和6项 ANN的ROC 曲线下面积（0．793、0．767和0．825）。结论：基于4种肿瘤标志的ANN模型判别诊断肺癌的效果优于Fisher判别分析，优于6种肿瘤标志建立的ANN模型；ANN模型诊断效果优于Fisher判别分析。
目的：應用人工神經網絡（ ANN）技術聯閤腫瘤標誌蛋白芯片建立人工智能輔助診斷模型，探討其對肺癌診斷的價值。方法：採用蛋白芯片（化學髮光法）測定201例肺良性疾病患者、202例肺癌患者血清中9項血清腫瘤標誌（ CA199、Ferritin、AFP、CA153、CEA、NSE、CA242、CA125、HGH）的水平，logistic迴歸篩選，建立ANN和Fisher判彆分析肺癌診斷模型。結果：4項腫瘤標誌（ CEA、NSE、Ferritin、CA153）建立的ANN模型的ROC麯線下麵積（0．850）高于4項 Fisher、6項（CEA、NSE、Ferritin、CA153、AFP、CA125） Fisher 和6項 ANN的ROC 麯線下麵積（0．793、0．767和0．825）。結論：基于4種腫瘤標誌的ANN模型判彆診斷肺癌的效果優于Fisher判彆分析，優于6種腫瘤標誌建立的ANN模型；ANN模型診斷效果優于Fisher判彆分析。
목적：응용인공신경망락（ ANN）기술연합종류표지단백심편건립인공지능보조진단모형，탐토기대폐암진단적개치。방법：채용단백심편（화학발광법）측정201례폐량성질병환자、202례폐암환자혈청중9항혈청종류표지（ CA199、Ferritin、AFP、CA153、CEA、NSE、CA242、CA125、HGH）적수평，logistic회귀사선，건립ANN화Fisher판별분석폐암진단모형。결과：4항종류표지（ CEA、NSE、Ferritin、CA153）건립적ANN모형적ROC곡선하면적（0．850）고우4항 Fisher、6항（CEA、NSE、Ferritin、CA153、AFP、CA125） Fisher 화6항 ANN적ROC 곡선하면적（0．793、0．767화0．825）。결론：기우4충종류표지적ANN모형판별진단폐암적효과우우Fisher판별분석，우우6충종류표지건립적ANN모형；ANN모형진단효과우우Fisher판별분석。
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 .
