A PRACTICAL APPLICATION OF NEURAL NETWORK ANALYSIS FOR PREDICTING OUTCOME OF INDIVIDUAL BREAST-CANCER PATIENTS
A PRACTICAL APPLICATION OF NEURAL NETWORK ANALYSIS FOR PREDICTING OUTCOME OF INDIVIDUAL BREAST-CANCER PATIENTS
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DOI：
10.1007/bf01840841
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发表时间：
1992-01-01
影响因子：
3.8
通讯作者：
CLARK, GM
中科院分区：
文献类型：
--
作者：
RAVDIN, PM;CLARK, GM
之前的研究表明，神经网络可以通过训练来识别个体乳腺癌患者复发和死亡风险的高低。本文扩展了最初的研究，并表明通过将时间编码为预后变量之一，神经网络可以使用经过审查的生存数据来预测患者随时间的预后。在这个演示中，神经网络被训练，测试，并使用来自1373名淋巴结阳性乳腺癌患者的生存数据进行验证。神经网络方法预测患者预后与Cox回归模型一样准确。最终的神经网络模型可以呈现患者的预后信息，并对不同随访时间的复发概率进行一系列预测，从而绘制个体患者的生存概率曲线。
It has been previously shown that Neural Networks can be trained to recognize individual breast cancer patients at high and low risk for recurrent disease and death. This paper expands on the initial investigation and shows that by coding time as one of the prognostic variables, a Neural Network can use censored survival data to predict patient outcome over time. In this demonstration a Neural Network was trained, tested, and validated using censored survival data from a group of 1373 patients with node-positive breast cancer. The Neural Network method predicted patient outcome as accurately as Cox Regression modeling. The final Neural Network model can be presented with a patient's prognostic information and make a series of predictions about probability of relapse at different times of follow-up, allowing one to draw survival probability curves for individual patients.