Prognostic models in patients with non-small-cell lung cancer using artificial neural networks in comparison with logistic regression.
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.
Hanai
Taizo
T
Department of Biotechnology, Graduate School of Engineering, Nagoya University, Chikusa-ku, Nagoya 469-8603, Japan. taizo@brs.kyushu-u.ac.jp
Yatabe
Yasushi
Y
Nakayama
Yusuke
Y
Takahashi
Takashi
T
Honda
Hiroyuki
H
Mitsudomi
Tetsuya
T
Kobayashi
Takeshi
T
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
D014408:Biomarkers, Tumor