Grumett Simon, Snow Pete, Kerr David
Institute for Cancer Studies, University of Birmingham, UK.
Clin Colorectal Cancer. 2003 Feb;2(4):239-44. doi: 10.3816/CCC.2003.n.005.
It is important to predict outcome for colorectal cancer patients following surgery, as almost 50% of patients undergoing a potentially curative resection will experience relapse. It is clear that present prognostic categories such as Dukes or TNM staging are too broad, and further refining is required to prognosticate for high-risk subgroups. One approach is to determine a phenotype associated with recurrence. We compared 2 methods of analyzing such data. Pathologic data from a large clinical trial was analyzed for 403 patients. The outcome modeled was disease recurrence. The results from logistic regression analysis and a neural network approach are compared with respect to receiver operator characteristic plots, which estimate the fit of the model. The best logistic regression model gives a result of 66%, and the neural network approach 78%. The conclusion from this study is that the neural network approach is superior to regression analysis. Further analyses are in progress using a larger patient sample size (n > 1000), improved statistical models, and a more refined neural network.
预测结直肠癌患者术后的预后很重要，因为近50%接受潜在根治性切除术的患者会出现复发。显然，目前的预后分类，如杜克斯分期或TNM分期过于宽泛，需要进一步细化以对高危亚组进行预后评估。一种方法是确定与复发相关的表型。我们比较了两种分析此类数据的方法。对一项大型临床试验中403例患者的病理数据进行了分析。建模的结果是疾病复发。逻辑回归分析和神经网络方法的结果根据受试者工作特征曲线进行比较，该曲线估计模型的拟合度。最佳逻辑回归模型的结果为66%，神经网络方法为78%。这项研究的结论是，神经网络方法优于回归分析。目前正在使用更大的患者样本量（n>1000）、改进的统计模型和更精细的神经网络进行进一步分析。
