Solitary pulmonary nodules: determining the likelihood of malignancy with neural network analysis.

Abstract:

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.

journal_name

Radiology

journal_title

Radiology

authors

Gurney JW,Swensen SJ

doi

10.1148/radiology.196.3.7644650

subject

Has Abstract

pub_date

1995-09-01 00:00:00

pages

823-9

issue

3

eissn

0033-8419

issn

1527-1315

journal_volume

196

pub_type

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