血清自体荧光光谱联合肿瘤标志物群在肺癌诊断中的价值

血清自体荧光光谱可以反映血清中癌细胞在代谢过程中发生的异常改变而导致的血清中荧光物质的成分、含量及微环境的变化，可作为癌症辅助诊断的一种新方法。利用荧光光谱分析技术，探讨了肺癌、肺良性疾病以及正常人血清的荧光光谱的异同，建立了血清荧光光谱检测的方法。同时联合肿瘤标志物群CEA, NSE, SCC-Ag, CYFRA21-1和p16甲基化，并运用人工神经网络技术和Fisher线性判别分析法分别建立了肺癌的诊断预测模型，并用ROC判别法对其预测结果进行比较。结果表明，荧光光谱联合肿瘤标志物建立的人工神经网络模型的预测效果优于单纯的荧光光谱神经网络模型，判别效果优于Fisher线性判别分析。

Value of Auto-Fluorescence Spectrum Combined with Tumor Markers in Diagnosis of Lung Cancer

To improve the diagnostic efficiency of cancer, serum fluorescence spectrum combined with tumor marker groups was proved more powerful, especially when used with mathematical evaluation model, that is, artificial neural network (ANN) modeling. ANN modeling is very suitable for the discrimination of lung cancer. ANN has evident superiority in solving nonlinear, multi-parameter and uncertain complicated problems. In the present paper, serum fluorescence spectrum was applied to study the difference among normal, benign and malignant groups and develop the relevant method of determination. On the other hand, combined with tumor markers, CEA, NSE, SCC-Ag, CYFRA21-1 and p16 methylation, artificial neural network and Fisher linear discriminatory analysis were used to develop the prediction models of diagnosis of lung cancer, and compared by ROC. It was shown that the result of the fluorescence spectrum combined with tumor markers based on ANN model is superior to that of the fluorescence spectrum ANN model. The performance of ANN model is superior to that of Fisher linear discriminatory analysis.