Open AccessPrediction of survival in surgical unresectable lung cancer by artificial neural networks including genetic polymorphisms and clinical parameters
Author(s) -
Hsia TeChun,
Chiang HungChih,
Chiang David,
Hang LiangWen,
Tsai FuuJen,
Chen WenChi
Publication year - 2003
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.10102
Subject(s) - lung cancer , artificial neural network , malignancy , stage (stratigraphy) , oncology , cancer , survival analysis , medicine , disease , biology , computer science , artificial intelligence , paleontology
Lung cancer, a common malignancy in Taiwan, involves multiple factors, including genetics and environmental factors. The survival time is very short once cancer is diagnosed as being in advanced stage and surgically unresectable. Therefore, a good model of prediction of disease outcome is important for a treatment plan. We investigated the survival time in advanced lung cancer by using computer science from the genetic polymorphism of the p21 and p53 genes in conjunction with patients' general data. We studied 75 advanced and surgical unresectable lung cancer patients. The prediction of survival time was made by comparing real data obtained from follow‐up periods with data generated by an artificial neural network (ANN). The most important input variable was the clinical staging of lung cancer patients. The second and third most important variables were pathological type and responsiveness to treatment, respectively. There were 25 neurons in the input layer, four neurons in the hidden layer‐1, and one neuron in the output layer. The predicted accuracy was 86.2%. The average survival time was 12.44 ± 7.95 months according to real data and 13.16 ± 1.77 months based on the ANN results. ANN provides good prediction results when clinical parameters and genetic polymorphisms are considered in the model. It is possible to use computer science to integrate the genetic polymorphisms and clinical parameters in the prediction of disease outcome. Data mining provides a promising approach to the study of genetic markers for advanced lung cancer. J. Clin. Lab. Anal. 17:229–234, 2003. © 2003 Wiley‐Liss, Inc.