[1]张永,李晓勇#,宋瑜,等.优化肿瘤标志物群建立的决策树模型对肝癌辅助诊断的价值[J].郑州大学学报(医学版),2015,(06):794-797.
 ZHANG Yong,LI Xiaoyong,SONG Yu,et al.Application of decision tree combined with filtered biomarkers in the diagnosis of liver cancer[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2015,(06):794-797.
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优化肿瘤标志物群建立的决策树模型对肝癌辅助诊断的价值()
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2015年06期
页码:
794-797
栏目:
论著
出版日期:
2015-12-20

文章信息/Info

Title:
Application of decision tree combined with filtered biomarkers in the diagnosis of liver cancer
作者:
张永1李晓勇1#宋瑜2周百中1崔卫东1赵甦1陈艳军1杨战锋1郜宇1杨华1
1郑州大学第五附属医院肝胆胰腺外科 郑州 4500522郑州大学第五附属医院妇产科 郑州 450052
Author(s):
ZHANG Yong1 LI Xiaoyong1 SONG Yu2 ZHOU Baizhong1 CUI Weidong1ZHAO Su1 CHEN Yanjun1 YANG Zhanfeng1GAO Yu1YANG Hua1
1Department of Hepatobiliary Pancreatic Surgery,the Fifth Affiliated Hospital,Zhengzhou University,Zhengzhou 4500522Department of Obstetrics and Gynecology,the Fifth Affiliated Hospital,Zhengzhou University,Zhengzhou 450052
关键词:
肝癌血清肿瘤标志物决策树辅助诊断
Keywords:
liver cancer serum tumor marker decision tree auxiliary diagnosis
分类号:
R735.7
摘要:
目的:探讨血清肿瘤标志物检测结合决策树模型在肝癌诊断中的价值。方法:运用肿瘤标志物定量检测试剂盒对119例肝部良性疾病及98例肝癌患者血清中9项肿瘤标志[甲胎蛋白(AFP)、癌胚抗原(CEA)、CA125、CA242、CA199、神经元特异性烯醇化酶(NSE)、铁蛋白(Ferritin)、人生长激素(HGH)和CA153]水平进行检测,应用logistic回归筛选肿瘤标志物,并于筛选前后建立决策树模型和Fisher判别分析模型。结果:肝癌组9项血清肿瘤标志物水平均高于肝良性疾病组(P<0.05)。筛选前基于9项肿瘤标志物、筛选后基于3项肿瘤标志物分别建立Fisher判别分析模型、决策树模型,其预测准确度分别为76.5%、91.2%、74.4%、90.8%。筛选前后决策树模型ROC曲线的AUC分别为0.912 和0.908,高于Fisher判别分析的0.745和0.727(Z=4.512 和4.589,P均<0.05);但决策树模型和Fisher判别分析筛选前后自身相比,差异均无统计学意义(Z=1.855和1.122,P均>0.05)。结论:基于3项血清肿瘤标志物建立的决策树模型诊断肝癌的效果优于 Fisher 判别分析。
Abstract:
Aim: To establish decision tree model based on filtered biomarkers to achieve rapid diagnosis of liver cancer.Methods: The serum levels of 9 tumor markers(AFP,CEA,CA125,CA242,CA199,NSE,Ferritin,HGH and CA153) in 98 patients with liver cancer and 119 patients with benign liver lesion were measured by multiple tumor marker protein biochip, and the models of decision tree and Fisher discrimination analysis were developed based on the tumor markers before and after being filtered by logistic regression.Results: The serum levels of the 9 tumor markers in patients with liver cancer were significantly higher than those in patients with benign liver disease(P<0.05). The accuracies of Fisher discrimination analysis and dicision tree models based on 9 tumor markers and 3 tumor markers filtered by logistic regression were 76.5%, 91.2%, 74.4%, 90.8%, respectively. The area under receiver operating curve(AUC) of dicision tree model was higher than that of Fisher discrimination analysis in both of 9 tumor markers model and 3 tumor markers model(Z=4.512 and 4.589, P<0.05). However, there was no significant difference in AUC between before and after screening in both models(Z=1.855 and 1.122, P>0.05).Conclusion: Compared with the effect of Fisher discrimination analysis, the effect of the decision tree model is better in diagnosing liver cancer especially based on three tumor markers screened by logistic regression.

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更新日期/Last Update: 1900-01-01