Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon.

Published

Journal Article

PURPOSE: To determine if an artificial neural network (ANN) to categorize benign and malignant breast lesions can be standardized for use by all radiologists. MATERIALS AND METHODS: An ANN was constructed based on the standardized lexicon of the Breast Imaging Recording and Data System (BI-RADS) of the American College of Radiology. Eighteen inputs to the network included 10 BI-RADS lesion descriptors and eight input values from the patient's medical history. The network was trained and tested on 206 cases (133 benign, 73 malignant cases). Receiver operating characteristic curves for the network and radiologists were compared. RESULTS: At a specified output threshold, the ANN would have improved the positive predictive value (PPV) of biopsy from 35% to 61% with a relative sensitivity of 100%. At a fixed sensitivity of 95%, the specificity of the ANN (62%) was significantly greater than the specificity of radiologists (30%) (P < .01). CONCLUSION: The BI-RADS lexicon provides a standardized language between mammographers and an ANN that can improve the PPV of breast biopsy.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • September 1995

Published In

Volume / Issue

  • 196 / 3

Start / End Page

  • 817 - 822

PubMed ID

  • 7644649

Pubmed Central ID

  • 7644649

International Standard Serial Number (ISSN)

  • 0033-8419

Digital Object Identifier (DOI)

  • 10.1148/radiology.196.3.7644649

Language

  • eng

Conference Location

  • United States