人工神经网络技术与肿瘤标志物联合检测在肺癌组织分型中的价值(英文)

Clinical value of determination of tumor markers in patients with lung carcinoma for histological type based on artificial neural network

目的 :通过 4项肿瘤标志物联合检测 ,运用人工神经网络技术 ,提高小细胞肺癌 (SCLC)与非小细胞肺癌(NSCLC)正确判别率。方法 :用放射免疫法测定了 5 1例肺癌患者血清癌胚抗原 (CEA)、糖类抗原 12 5 (CA12 5 )、促胃液素、神经元特异性烯醇化酶 (NSE)水平 ,采用人工神经网络技术 ,探讨了 4项肿瘤标志物在肺癌组织分型中的应用价值。结果 :SCLC患者促胃液素、NSE水平明显高于NSCLC患者 ,而CEA、CA12 5水平却低于非小细胞肺癌患者。人工神经网络技术在判别SCLC与NSCLC类型中 ,总的符合率为 87.5 %。结论 :该 4项肿瘤标志物联合检测在肺癌组织分型方面可为临床提供有价值的参考资料 ,同时表明人工神经网络技术在肺癌组织分型中具有一定的实用价值。

Aim: The early diagnosis of lung cancer is very important, but this is still difficult. The aim of this study was to improve the rate of correct classification of small cell and non small cell lung cancer. Methods: A panel of four tumor biomarkers, including serum CEA、CA125、gastrin and NSE were determined with radioimmunoassay in 30 patients with non small cell lung cancer (NSCLC) and 21 patients with small cell lung cancer (SCLC). We explored the usefulness of artificial neural network (ANN) in this discrimination. Results: The concentrations of CEA 、CA125、gastrin and NSE had significantly difference between the two groups. The levels of gastrin and NSE in SCLC were significantly higher than those in NSCLC, but levels of CEA andCA125 in SCLC were significantly lower than those in NSCLC. ANN was able to correctly identify all but two cases. The total accurate rate was 87.5% in distinguishing SCLC from NSCLC. Conclusions: The results revealed that CEA and CA125 had specificity in NSCLC, gastrin and NSE in SCLC. And so combined determination of the four tumor markers may be useful in the histological type diagnosis of lung cancer before operation. ANN was useful method in the prediction of histological type of lung cancer.