γ-H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of Patients with Early Operable Non-Small Cell Lung Cancer
Autor: | G. Papashinopoulos, Demosthenes Bouros, Stylianos Kakolyris, Dimitrios Matthaios, A. G. Rigas, E. Chatzimichail, Petros Karakitsos, Konstantinos Romanidis |
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Jazyk: | angličtina |
Rok vydání: | 2014 |
Předmět: |
Prognosis prediction
Artificial neural network lcsh:QH426-470 Article Subject business.industry Pharmaceutical Science Cancer medicine.disease Bioinformatics Logistic regression Biochemistry Data set lcsh:Genetics Genetics Medicine Non small cell business Lung cancer Molecular Biology Cause of death Research Article |
Zdroj: | International Journal of Genomics International Journal of Genomics, Vol 2014 (2014) |
ISSN: | 2314-436X |
DOI: | 10.1155/2014/160236 |
Popis: | Cancer is a leading cause of death worldwide and the prognostic evaluation of cancer patients is of great importance in medical care. The use of artificial neural networks in prediction problems is well established in human medical literature. The aim of the current study was to assess the prognostic value of a series of clinical and molecular variables with the addition ofγ-H2AX—a new DNA damage response marker—for the prediction of prognosis in patients with early operable non-small cell lung cancer by comparing theγ-H2AX-based artificial network prediction model with the corresponding LR one. Two prognostic models of 96 patients with 27 input variables were constructed by using the parameter-increasing method in order to compare the predictive accuracy of neural network and logistic regression models. The quality of the models was evaluated by an independent validation data set of 11 patients. Neural networks outperformed logistic regression in predicting the patient’s outcome according to the experimental results. To assess the importance of the two factors p53 andγ-H2AX, models without these two variables were also constructed. JR and accuracy of these models were lower than those of the models using all input variables, suggesting that these biological markers are very important for optimal performance of the models. This study indicates that neural networks may represent a potentially more useful decision support tool than conventional statistical methods for predicting the outcome of patients with non-small cell lung cancer and that some molecular markers, such asγ-H2AX, enhance their predictive ability. |
Databáze: | OpenAIRE |
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