Artificial neural network: improving the quality of breast biopsy recommendations.

Journal Article

Purpose

To evaluate the performance and inter- and intraobserver variability of an artificial neural network (ANN) for predicting breast biopsy outcome.

Materials and methods

Five radiologists described 60 mammographically detected lesions with the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) nomenclature. A previously programmed ANN used the BI-RADS descriptors and patient histories to predict biopsy results. ANN predictive performance was compared with the clinical decision to perform biopsy. Inter- and intraobserver variability of radiologists' interpretations and ANN predictions were evaluated with Cohen kappa analysis.

Results

The ANN maintained 100% sensitivity (23 of 23 cancers) while improving the positive predictive value of biopsy results from 38% (23 of 60 lesions) to between 58% (23 of 40 lesions) and 66% (23 of 35 lesions; P < .001). Interobserver variability for interpretation of the lesions was significantly reduced by the ANN (P < .001); there was no statistically significant effect on nearly perfect intraobserver reproducibility.

Conclusion

Use of an ANN with radiologists' descriptions of abnormal findings may improve interpretation of mammographic abnormalities.

Full Text

Duke Authors

Cited Authors

  • Baker, JA; Kornguth, PJ; Lo, JY; Floyd, CE

Published Date

  • January 1996

Published In

Volume / Issue

  • 198 / 1

Start / End Page

  • 131 - 135

PubMed ID

  • 8539365

Pubmed Central ID

  • 8539365

Electronic International Standard Serial Number (EISSN)

  • 1527-1315

International Standard Serial Number (ISSN)

  • 0033-8419

Digital Object Identifier (DOI)

  • 10.1148/radiology.198.1.8539365

Language

  • eng