title: Prognostic models in patients with non-small-cell lung cancer using artificial neural networks in comparison with logistic regression.
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 outperformed 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.
authors.affiliation: Department of Biotechnology, Graduate School of Engineering, Nagoya University, Chikusa-ku, Nagoya 469-8603, Japan. taizo@brs.kyushu-u.ac.jp
authors.forename: Taizo
authors.initials: T
authors.lastname: Hanai
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Yatabe
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Nakayama
authors.forename: Takashi
authors.initials: T
authors.lastname: Takahashi
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Honda
authors.forename: Tetsuya
authors.initials: T
authors.lastname: Mitsudomi
authors.forename: Takeshi
authors.initials: T
authors.lastname: Kobayashi
mesh_terms: D000230:Adenocarcinoma / Q000188:drug therapy*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000032:analysis; D018287:Carcinoma, Large Cell; D002289:Carcinoma, Non-Small-Cell Lung / Q000737:chemistry / Q000175:diagnosis* / Q000473:pathology; D002294:Carcinoma, Squamous Cell; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000737:chemistry / Q000175:diagnosis* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor