Τίτλος:
gamma-H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of
Patients with Early Operable Non-Small Cell Lung Cancer
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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 of gamma-H2AX-a new DNA damage response
marker-for the prediction of prognosis in patients with early operable
non-small cell lung cancer by comparing the gamma-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 and gamma-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 as
gamma-H2AX, enhance their predictive ability.
Συγγραφείς:
Chatzimichail, E.
Matthaios, D.
Bouros, D.
Karakitsos, P.
and Romanidis, K.
Kakolyris, S.
Papashinopoulos, G.
Rigas,
A.
Περιοδικό:
International Journal of Genomics
