Journal
RADIOLOGY
Volume 214, Issue 3, Pages 823-830Publisher
RADIOLOGICAL SOC NORTH AMERICA
DOI: 10.1148/radiology.214.3.r00mr22823
Keywords
computers, neural network; computers, diagnostic aid; diagnostic radiology, observer performance; lung neoplasms, diagnosis; lung, nodule; receiver operating characteristic (ROC) curve
Funding
- NCI NIH HHS [CA24806, CA62625] Funding Source: Medline
- NATIONAL CANCER INSTITUTE [R01CA062625, R01CA024806] Funding Source: NIH RePORTER
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PURPOSE: To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) to assist radiologists in the distinction of benign and malignant pulmonary nodules. MATERIALS AND METHODS:: Fifty-six chest radiographs of 34 primary lung cancers and 22 benign nodules were digitized with a 0.175-mm pixel size and a 10-bit gray scale. Eight subjective image features were evaluated and recorded by radiologists in each case. A computerized method was developed to extract objective features that could be correlated with the subjective features. An ANN was used to distinguish benign from malignant nodules on the basis of subjective or objective features The performance of the ANN was compared with that of the radiologists by means of receiver operating characteristic (ROC) analysis. RESULTS: Performance of the ANN was considerably greater with objective features (area under the ROC curve, A(z) = 0.854) than with subjective features (A(z) = 0.761). Performance of the ANN was also greater than that of the radiologists (A(z) = 0.752). CONCLUSION: The computerized scheme has the potential to improve the diagnostic accuracy of radiologists in,the distinction of benign and malignant solitary pulmonary nodules.
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