摘要
目的观察胃肠肿瘤标志物联合人工神经网络对大肠癌的预警效果。方法将4项胃肠肿瘤标志物进行联合检测,应用人工神经网络技术建立大肠癌肿瘤标志物预警模型(即大肠癌-大肠息肉-正常人的人工神经网络模型),应用该模型和4项胃肠肿瘤标志物联合检测,分别对大肠癌患者进行预测并构建ROC曲线。结果大肠癌-大肠息肉-正常人的人工神经网络模型对大肠癌预测的灵敏度为80.03%,特异度为87.01%,准确度为81.77%,优于肿瘤标志物的联合检测,两者的ROC曲线下面积相比较,差异有统计学意义(P<0.05)。结论 4项胃肠肿瘤标志物检测联合人工神经网络模型,提高了对大肠癌的预测准确性,解决了大量复杂、繁琐的数据分析工作,其操作简便,易于推广和应用。
Objective To observe the value of an artificial neural network model based on tumor markers in serum for predicting colorectal carcinoma. Methods A predicting model for colorectal carcinoma based on combined detection of 4 tumor markers was constructed with artificial neural network（ a colon carcinoma- colorectal polyps- normal ANN model）. Then the patients with colorectal cancer were predicted by using the model and 4 gastrointestinal tumor markers combined detection separately,receiver operating characteristic（ ROC） curve was plotted. Results The sensitivity,specificity,accuracy of the colon carcinoma-colorectal polyps- normal ANN model for colorectal carcinoma were 80. 03%,87. 01% and 81. 77%,respectively. It was superior than that of tumor markers combined detection. There was a statistically significance on difference between the area under the ROC curve of these two compared groups（ P〈0. 05）. Conclusion The artificial neural network model based on combined detection of 4 tumor markers in serum can increase the accuracy markedly for predicting colorectal carcinoma,besides,settle the problem of volume and complex data analysis. It is simple,and easy for popularization and application.
出处
《中国卫生检验杂志》
CAS
2015年第3期371-373,377,共4页
Chinese Journal of Health Laboratory Technology
基金
2012年浙江省医学会正大青春宝肿瘤科研专项(2012-ZYC-A67)
关键词
大肠癌
肿瘤标志物
人工神经网络
预警
ROC曲线
Colorectal carcinoma
Tumor marker
Artificial neural network
Prediction
ROC curve
