Marchevsky A M, Patel S, Wiley K J, Stephenson M A, Gondo M, Brown R W, Yi E S, Benedict W F, Anton R C, Cagle P T
Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA.
Mod Pathol. 1998 Jul;11(7):618-25.
The prognosis of patients with Stage I and II non-small cell lung cancer (NSCLC) can be estimated but cannot be definitively ascertained by use of current clinicopathologic criteria and tumor marker studies. The potential value of probabilistic neural networks (NNs) with genetic algorithms and multivariate logistic regression to predict the survival of NSCLC patients has not been previously evaluated. Multiple prognostic factors (age, sex, cell type, stage, tumor grade, smoking history, and immunoreactivity to c-erbB-3, bcl-2, Glut1, Glut3, retinoblastoma gene and p53 were correlated with 5-year survival in 63 patients with Stage I or II NSCLC, treated solely by surgical excision at Baylor Medical College, Houston, Texas. Several probabilistic NNs with genetic algorithm models were developed using the prognostic features as input neurons and survival at 5 years (free of disease/dead of disease) as output neurons. The probabilistic NN yielded excellent classification rates for dependent variable survival. The best model was trained with 52 cases and classified all 11 "unknown" test cases correctly. Several statistically significant logistic regression models were fitted using 50 cases to build the models and 13 cases as "hold-out" test cases. These multivariate statistical models provide various cutoff values that predict/classify the probability of survival at 5 years. In conclusion, probabilistic NNs and logistic regression models can be useful in estimating the prognosis of patients with Stage I and II NSCLC using multiple clinicopathologic and molecular variables. These multivariate predictive models need to be validated with much larger groups of patients to assess their potential clinical value.
I期和II期非小细胞肺癌（NSCLC）患者的预后可以进行估计，但无法通过目前的临床病理标准和肿瘤标志物研究来确切确定。此前尚未评估过具有遗传算法的概率神经网络（NNs）和多变量逻辑回归预测NSCLC患者生存率的潜在价值。在德克萨斯州休斯顿贝勒医学院仅接受手术切除治疗的63例I期或II期NSCLC患者中，多个预后因素（年龄、性别、细胞类型、分期、肿瘤分级、吸烟史以及对c-erbB-3、bcl-2、Glut1、Glut3、视网膜母细胞瘤基因和p53的免疫反应性）与5年生存率相关。使用这些预后特征作为输入神经元，5年生存率（无病生存/因病死亡）作为输出神经元，开发了几种具有遗传算法模型的概率神经网络。概率神经网络对因变量生存率产生了优异的分类率。最佳模型用52个病例进行训练，并正确分类了所有11个“未知”测试病例。使用50个病例构建模型，并将13个病例作为“留出”测试病例，拟合了几个具有统计学意义的逻辑回归模型。这些多变量统计模型提供了各种截止值，用于预测/分类5年生存率的概率。总之，概率神经网络和逻辑回归模型可用于使用多个临床病理和分子变量估计I期和II期NSCLC患者的预后。这些多变量预测模型需要在更大规模的患者群体中进行验证，以评估其潜在的临床价值。
