Prognostic models in patients with non-small-cell lung cancer using artificial neural networks in comparison with logistic regression

Taizo Hanai, Yasushi Yatabe, Yusuke Nakayama, Takashi Takahashi, Hiroyuki Honda, Tetsuya Mitsudomi, Takeshi Kobayashi

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14 Citations (Scopus)

Abstract

It is difficult to precisely predict the outcome of each individual patient with non-small-cell lung cancer (NSCLC) by using conventional statistical methods and ordinary clinico-pathological variables. We applied artificial neural networks (ANN) for this purpose. We constructed a prognostic model for 125 NSCLC patients with 17 potential input variables, including 12 clinico-pathological variables (age, sex, smoking index, tumor size, p factor, pT, pN, stage, histology) and 5 immunohistochemical variables (p27 percentage, p27 intensity, p53, cyclin D1, retinoblastoma (RB)), by using the parameter-increasing method (PIM). Using the resultant ANN model, prediction was possible in 104 of 125 patients (83%, judgment ratio (JR)) and accuracy for prediction of survival at 5 years was 87%. On the other hand, JR and survival prediction accuracy in the logistic regression (LR) model were 37% and 78%, respectively. In addition, ANN out-performed LR for prediction of survival at 1 or 3 years. In these cases, PIM selected p27 intensity and cyclin D1 for the 3-year survival model and p53 for the 1-year survival model in addition to clinico-pathological variables. Finally, even in an independent validation data set of 48 patients, who underwent surgery 10 years later, the present ANN model could predict outcome of patients at 5 years with the JR and accuracy of 81% and 77%, respectively. This study demonstrates that ANN is a potentially more useful tool than conventional statistical methods for predicting survival of patients with NSCLC and that inclusion of relevant molecular markers as input variables enhances its predictive ability.

Original languageEnglish
Pages (from-to)473-477
Number of pages5
JournalCancer Science
Volume94
Issue number5
DOIs
Publication statusPublished - May 1 2003
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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