Jerez J M, Franco L, Alba E, Llombart-Cussac A, Lluch A, Ribelles N, Munárriz B, Martín M
Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain.
Breast Cancer Res Treat. 2005 Dec;94(3):265-72. doi: 10.1007/s10549-005-9013-y.
The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the 'El Alamo' Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p < 0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1-10 and 11-20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.
本研究的目的是比较神经网络（NN）模型与标准Cox比例风险模型的预测准确性。本研究纳入的3811例患者的数据是在“埃尔阿拉莫”项目中收集的，该项目是西班牙最大的乳腺癌（BC）数据集。由神经网络生成的最佳预后模型包含年龄、肿瘤大小、淋巴结状态、肿瘤分级和治疗类型作为协变量。在Cox模型分析中，这些相同的变量被认为具有预后意义。然而，神经网络做出的预测在统计学上比Cox模型的预测更准确（p < 0.0001）。还分析了七个不同的时间间隔，发现神经网络的预测比Cox模型的预测更准确，特别是在1至10个月和11至20个月的早期间隔，以及在从61个月到最大随访时间（MFT）的后期间隔。有趣的是，这些间隔包含在不同研究中观察到的高复发风险区域，并且在所分析的数据集中也存在。
