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

Yongjun Wu, Yiming Wu, Jing Wang, Zhen Yan, Lingbo Qu, Bingren Xiang, Yiguo Zhang

    Research output: Contribution to journalArticle

    24 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)11329-11334
    Number of pages6
    JournalExpert Systems with Applications
    Volume38
    Issue number9
    DOIs
    Publication statusPublished - Sep 2011

    Cite this

    Wu, Yongjun ; Wu, Yiming ; Wang, Jing ; Yan, Zhen ; Qu, Lingbo ; Xiang, Bingren ; Zhang, Yiguo. / An optimal tumor marker group-coupled artificial neural network for diagnosis of lung cancer. In: Expert Systems with Applications. 2011 ; Vol. 38, No. 9. pp. 11329-11334.
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    title = "An optimal tumor marker group-coupled artificial neural network for diagnosis of lung cancer",
    abstract = "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.",
    author = "Yongjun Wu and Yiming Wu and Jing Wang and Zhen Yan and Lingbo Qu and Bingren Xiang and Yiguo Zhang",
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    An optimal tumor marker group-coupled artificial neural network for diagnosis of lung cancer. / Wu, Yongjun; Wu, Yiming; Wang, Jing; Yan, Zhen; Qu, Lingbo; Xiang, Bingren; Zhang, Yiguo.

    In: Expert Systems with Applications, Vol. 38, No. 9, 09.2011, p. 11329-11334.

    Research output: Contribution to journalArticle

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    AU - Wu, Yongjun

    AU - Wu, Yiming

    AU - Wang, Jing

    AU - Yan, Zhen

    AU - Qu, Lingbo

    AU - Xiang, Bingren

    AU - Zhang, Yiguo

    PY - 2011/9

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    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.

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