An optimal tumor marker group-coupled artificial neural network for diagnosis of lung cancer

TY - JOUR

T1 - An optimal tumor marker group-coupled artificial neural network for diagnosis of lung cancer

AU - Wu, Yongjun

AU - Wu, Yiming

AU - Wang, Jing

AU - Yan, Zhen

AU - Qu, Lingbo

AU - Xiang, Bingren

AU - Zhang, Yiguo

PY - 2011/9

Y1 - 2011/9

N2 - Background: Epidemiological statistics has shown that there are approximately 1.2 million new cases of lung cancer diagnosed every year and the death rate of these patients is 17.8%. Earlier diagnosis is key to promote the five-year survival rate of these cancer patients. Some tumor markers have been found to be valuable for earlier diagnosis, but a single marker has limitation in its sensitivity and specificity of cancer diagnosis. To improve the efficiency of diagnosis, several distinct tumor marker groups are combined together using a mathematical evaluation model, called artificial neural network (ANN). Lung cancer markers have been identified to include carcinoembryonic antigen, carcinoma antigen 125, neuron specific enolase, beta(2)-microglobulin, gastrin, soluble interleukin-6 receptor, sialic acid, pseudouridine, nitric oxide, and some metal ions.Methods: These tumor markers were measured through distinct experimental procedures in 50 patients with lung cancer, 40 patients with benign lung diseases, and 50 cases for a normal control group. The most valuable were selected into an optimal tumor marker group by multiple logistic regression analysis. The optimal marker group-coupled ANN model was employed as an intelligent diagnosis system.Results: We have presented evidence that this system is superior to a traditional statistical method, its diagnosis specificity significantly improved from 72.0% to 100.0% and its accuracy increased from 71.4% to 92.8%.Conclusions: The ANN-based system may provide a rapid and accurate diagnosis tool for lung cancer. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

AB - Background: Epidemiological statistics has shown that there are approximately 1.2 million new cases of lung cancer diagnosed every year and the death rate of these patients is 17.8%. Earlier diagnosis is key to promote the five-year survival rate of these cancer patients. Some tumor markers have been found to be valuable for earlier diagnosis, but a single marker has limitation in its sensitivity and specificity of cancer diagnosis. To improve the efficiency of diagnosis, several distinct tumor marker groups are combined together using a mathematical evaluation model, called artificial neural network (ANN). Lung cancer markers have been identified to include carcinoembryonic antigen, carcinoma antigen 125, neuron specific enolase, beta(2)-microglobulin, gastrin, soluble interleukin-6 receptor, sialic acid, pseudouridine, nitric oxide, and some metal ions.Methods: These tumor markers were measured through distinct experimental procedures in 50 patients with lung cancer, 40 patients with benign lung diseases, and 50 cases for a normal control group. The most valuable were selected into an optimal tumor marker group by multiple logistic regression analysis. The optimal marker group-coupled ANN model was employed as an intelligent diagnosis system.Results: We have presented evidence that this system is superior to a traditional statistical method, its diagnosis specificity significantly improved from 72.0% to 100.0% and its accuracy increased from 71.4% to 92.8%.Conclusions: The ANN-based system may provide a rapid and accurate diagnosis tool for lung cancer. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

U2 - 10.1016/j.eswa.2011.02.183

DO - 10.1016/j.eswa.2011.02.183

M3 - Article

SN - 0957-4174

VL - 38

SP - 11329

EP - 11334

JO - Expert Systems with Applications

JF - Expert Systems with Applications

IS - 9

ER -