<abstract xmlns="http://www.w3.org/1999/xhtml"> Background: There is a challenge in diagnosing cancer in patients with exudative plural effusion using a noninvasive and accurate method.Objective: We developed artificial neural network (ANN), as a nonlinear model, to discriminate malignant exudative plural effusion from nonmalignant based on routine pleural fluid findings.Methods: The plural fluid parameters including total and differential cell counts, total proteins, lactate dehydrogenase (LDH), glucose, adenosine deaminase (ADA), as well as age and sex of 114 patients with exudative plural effusion were applied by models as input. The output was supposed to be the presence or absence of the cancer.Results: The accuracy, sensitivity and specificity of ANN for predicting malignancy were 89.7%, 86.7%, and 91.7%, respectively. In addition, the neural network significantly outperformed the logistic regression model, as a linear model, (AUC: 0.892 vs. 0.633, respectively, p &lt; 0.001).Conclusion: The ANN is a novel accurate and noninvasive method that can be used clinically to diagnose malignancy in patients with exudative plural effusion.</abstract>

Background: There is a challenge in diagnosing cancer in patients with exudative plural effusion using a noninvasive and accurate method.Objective: We developed artificial neural network (ANN), as a nonlinear model, to discriminate malignant exudative plural effusion from nonmalignant based on routine pleural fluid findings.Methods: The plural fluid parameters including total and differential cell counts, total proteins, lactate dehydrogenase (LDH), glucose, adenosine deaminase (ADA), as well as age and sex of 114 patients with exudative plural effusion were applied by models as input. The output was supposed to be the presence or absence of the cancer.Results: The accuracy, sensitivity and specificity of ANN for predicting malignancy were 89.7%, 86.7%, and 91.7%, respectively. In addition, the neural network significantly outperformed the logistic regression model, as a linear model, (AUC: 0.892 vs. 0.633, respectively, p < 0.001).Conclusion: The ANN is a novel accurate and noninvasive method that can be used clinically to diagnose malignancy in patients with exudative plural effusion.

A nonlinear model for diagnosing malignancy in patients with exudative plural effusion using routine plural fluid findings

Department of Physiology/Physiology in Unusual States Research Center, School of Medicine, Aja University of Medical Sciences, Tehran,

Department of Infectious Diseases, School of Medicine, Aja University of Medical Sciences, Tehran,

Asian Biomedicine

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Background: There is a challenge in diagnosing cancer in patients with exudative plural effusion using a noninvasive and accurate method.Objective: We...

A nonlinear model for diagnosing malignancy in patients with exudative plural effusion using routine plural fluid findings

Article Category: Brief communication (Original)

Published Online: Feb 04, 2017

Page range: 841 - 846

DOI: https://doi.org/10.5372/1905-7415.0706.248

Keywords
Artificial neural network, exudative plural effusion, malignancy

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A nonlinear model for diagnosing malignancy in patients with exudative plural effusion using routine plural fluid findings

Article Category: Brief communication (Original)

Published Online: Feb 04, 2017

Page range: 841 - 846

DOI: https://doi.org/10.5372/1905-7415.0706.248

KeywordsArtificial neural network, exudative plural effusion, malignancy

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Artificial neural network, exudative plural effusion, malignancy