The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer

Abstract

To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.