结肠癌治疗后5年生存率的神经网络和回归预测。
Neural network and regression predictions of 5-year survival after colon carcinoma treatment.
作者信息
Snow P B, Kerr D J, Brandt J M, Rodvold D M
机构信息
Xaim, Inc., Colorado Springs, Colorado, USA.
出版信息
Cancer. 2001 Apr 15;91(8 Suppl):1673-8. doi: 10.1002/1097-0142(20010415)91:8+<1673::aid-cncr1182>3.0.co;2-t.
BACKGROUND
The Commission on Cancer data from the National Cancer Data Base (NCDB) for patients with colon carcinoma was used to develop several artificial neural network and regression-based models. These models were designed to predict the likelihood of 5-year survival after primary treatment for colon carcinoma.
METHODS
Two modeling methods were used in the study. Artificial neural networks were used to select the more important variables from the NCDB database and model 5-year survival. A standard parametric logistic regression also was used to model survival and the two methods compared on a prospective set of patients not used in model development.
RESULTS
The neural network yielded a receiver operating characteristic (ROC) area of 87.6%. At a sensitivity to mortality of 95% the specificity was 41%. The logistic regression yielded a ROC area of 82% and at a sensitivity to mortality of 95% gave a specificity of 27%.
CONCLUSIONS
The neural network found a strong pattern in the database predictive of 5-year survival status. The logistic regression produced somewhat less accurate, but good, results.
背景
利用国家癌症数据库（NCDB）中结肠癌患者的癌症委员会数据开发了几种基于人工神经网络和回归的模型。这些模型旨在预测结肠癌初次治疗后5年生存的可能性。
方法
本研究使用了两种建模方法。人工神经网络用于从NCDB数据库中选择更重要的变量并对5年生存情况进行建模。还使用标准参数逻辑回归对生存情况进行建模，并在模型开发中未使用的一组前瞻性患者中对这两种方法进行比较。
结果
神经网络的受试者工作特征（ROC）曲线下面积为87.6%。在对死亡率的敏感性为95%时，特异性为41%。逻辑回归的ROC曲线下面积为82%，在对死亡率的敏感性为95%时，特异性为27%。
结论
神经网络在数据库中发现了预测5年生存状态的强大模式。逻辑回归产生的结果准确性稍低，但也较好。
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