SOLITARY PULMONARY NODULES - DETERMINING THE LIKELIHOOD OF MALIGNANCY WITH NEURAL-NETWORK ANALYSIS

被引:61
作者
GURNEY, JW [1 ]
SWENSEN, SJ [1 ]
机构
[1] MAYO CLIN,ROCHESTER,MN
关键词
COMPUTERS; DIAGNOSTIC AID; NEURAL NETWORK; LUNG; NODULE; LUNG NEOPLASMS; DIAGNOSIS;
D O I
10.1148/radiology.196.3.7644650
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PURPOSE: To test a neural network in differentiation of benign from malignant solitary pulmonary nodules. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: The Bayesian method was better than the neural network in prediction of probability of malignancy in solitary pulmonary nodules.
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页码:823 / 829
页数:7
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