Hanai Taizo, Yatabe Yasushi, Nakayama Yusuke, Takahashi Takashi, Honda Hiroyuki, Mitsudomi Tetsuya, Kobayashi Takeshi
Department of Biotechnology, Graduate School of Engineering, Nagoya University, Chikusa-ku, Nagoya 469-8603, Japan.
Cancer Sci. 2003 May;94(5):473-7. doi: 10.1111/j.1349-7006.2003.tb01467.x.
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.
使用传统统计方法和普通临床病理变量很难精确预测每一位非小细胞肺癌（NSCLC）患者的预后。我们为此应用了人工神经网络（ANN）。我们使用参数增加法（PIM），为125例NSCLC患者构建了一个预后模型，该模型有17个潜在输入变量，包括12个临床病理变量（年龄、性别、吸烟指数、肿瘤大小、p因子、pT、pN、分期、组织学）和5个免疫组化变量（p27百分比、p27强度、p53、细胞周期蛋白D1、视网膜母细胞瘤（RB））。使用所得的ANN模型，125例患者中有104例（83%，判断率（JR））可以进行预测，5年生存率预测的准确率为87%。另一方面，逻辑回归（LR）模型的JR和生存预测准确率分别为37%和78%。此外，在预测1年或3年生存率方面，ANN的表现优于LR。在这些情况下，除临床病理变量外，PIM为3年生存模型选择了p27强度和细胞周期蛋白D1，为1年生存模型选择了p53。最后，即使在10年后接受手术的48例患者的独立验证数据集中，目前的ANN模型也能够分别以81%的JR和77%的准确率预测患者5年的预后。这项研究表明，对于预测NSCLC患者的生存率，ANN是一种可能比传统统计方法更有用的工具，并且纳入相关分子标志物作为输入变量可增强其预测能力。
