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

Jud W. Gurney, Stephen J. Swensen

Research output: Contribution to journalArticle

65 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)823-829
Number of pages7
JournalRadiology
Volume196
Issue number3
DOIs
StatePublished - Sep 1995

Keywords

  • Computers, diagnostic aid
  • Computers, neural network
  • Lung neoplasms, diagnosis
  • Lung, nodule

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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