血清肿瘤标志物优化组合人工神经网络模型在大肠癌诊断中的应用

目的:从目前已知的血清肿瘤标志物中筛选出用于大肠癌诊断的最优化肿瘤标志物组合,并联合这组标志物建立基于人工神经网络的大肠癌智能诊断模型.方法:应用酶联免疫吸附法分别测定128例大肠癌患者和113例健康人血清癌胚抗原(CEA)、甲胎蛋白(AFP)、癌抗原199(CA199)、癌抗原724(CA724)、癌抗原242(CA242)、癌抗原211(CA211)、神经元特异性烯醇化酶(NSE)和组织多肽抗原(TPA)共8种肿瘤相关标志物含量,用曲线下面积结合人工神经网络模型的方法评价并筛选最优标志物联合模型,并将此模型应用于大肠癌的诊断.结果:筛选出CEA、CA199、CA242、CA211及CA724 5个最优肿瘤标志物的组合,建立了诊断大肠癌的人工神经网络模型,并用5倍交叉验证,该模型预测大肠癌样本的特异性为95%,敏感性为83%,阳性预测率为95%.结论:本研究筛选出的最优肿瘤标志物组合诊断大肠癌具有较高的敏感性和特异性.

The optimal combination of serum tumor markers with bioinformatics in diagnosis of colorectal carcinoma

Objective: To identify the optimal combination of serum tumor markers with bioinformatics in diagnosis of colorectal cancer. Methods: The serum levels of CEA, AFP, NSE, CA199, CA242, CA724, CA211 and TPA were detected in 128 patients with colorectal carcinoma and 113 health subjects. The serum tumor markers were evaluated with the area under curves. The optimal combination of serum tumor markers was selected and the diagnostic model with artificial neural network was established. Results: CEA,CA199,CA242,CA211,CA724 were selected for the optimal combination and the artificial neural network was built. The model was evaluated by a 5 cross validation approach. The model had a specificity of 95%, sensitivity of 83% and positive predictive value of 95% in diagnosis of colorectal carcinoma. Conclusion: The combination of optimal serum tumor markers has a high sensitivity and specificity in diagnosis of colorectal carcinoma.