### 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 language | English (US) |
---|---|

Pages (from-to) | 823-829 |

Number of pages | 7 |

Journal | Radiology |

Volume | 196 |

Issue number | 3 |

State | Published - 1995 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Radiological and Ultrasound Technology

### Cite this

*Radiology*,

*196*(3), 823-829.

**Solitary pulmonary nodules : Determining the likelihood of malignancy with neural network analysis.** / Gurney, J. W.; Swensen, S. J.

Research output: Contribution to journal › Article

*Radiology*, vol. 196, no. 3, pp. 823-829.

}

TY - JOUR

T1 - Solitary pulmonary nodules

T2 - Determining the likelihood of malignancy with neural network analysis

AU - Gurney, J. W.

AU - Swensen, S. J.

PY - 1995

Y1 - 1995

N2 - 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.

AB - 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.

KW - Computers, diagnostic aid

KW - Computers, neural network

KW - Lung neoplasms, diagnosis

KW - Lung, nodule

UR - http://www.scopus.com/inward/record.url?scp=0029102693&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029102693&partnerID=8YFLogxK

M3 - Article

C2 - 7644650

AN - SCOPUS:0029102693

VL - 196

SP - 823

EP - 829

JO - Radiology

JF - Radiology

SN - 0033-8419

IS - 3

ER -