Gurney J W, Swensen S J
Department of Radiology, University of Nebraska Medical Center, Omaha 68198-1045, USA.
Radiology. 1995 Sep;196(3):823-9. doi: 10.1148/radiology.196.3.7644650.
To test a neural network in differentiation of benign from malignant solitary pulmonary nodules.
Neural networks were trained and tested on the characteristics of 318 nodules. Predictive accuracy of the network was judged for calibration and discrimination. Network results were compared with those with a simpler Bayesian method.
The Brier score was 0.142 (calibration, 0.003; discrimination, 0.139) for the neural network and 0.133 for the Bayesian analysis (calibration, 0.012; discrimination, 0.121). Analysis of the calibration curve revealed no significant difference (P < .05) between the slope (b = 1.09) and the line of identity (b = 1) for the neural network or the Bayesian analysis. The area under the receiver operating characteristic curve was 0.871 for the neural network and 0.894 for the Bayesian analysis (P < .05). There were 23 and 21 false-positive predictions and 18 and six false-negative predictions for the neural network and Bayesian analysis, respectively.
The Bayesian method was better than the neural network in prediction of probability of malignancy in solitary pulmonary nodules.
测试神经网络在鉴别孤立性肺结节的良恶性方面的性能。
基于318个结节的特征对神经网络进行训练和测试。通过校准和区分来判断网络的预测准确性。将网络结果与一种更简单的贝叶斯方法的结果进行比较。
神经网络的布里尔评分是0.142（校准，0.003；区分，0.139），贝叶斯分析的布里尔评分为0.133（校准，0.012；区分，0.121）。校准曲线分析显示，神经网络或贝叶斯分析的斜率（b = 1.09）与恒等线（b = 1）之间无显著差异（P <.05）。神经网络的受试者操作特征曲线下面积为0.871，贝叶斯分析为0.894（P <.05）。神经网络和贝叶斯分析的假阳性预测分别为23例和21例，假阴性预测分别为18例和6例。
在预测孤立性肺结节的恶性概率方面，贝叶斯方法优于神经网络。
