Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.

Journal Article (Journal Article)

PURPOSE: To evaluate whether an artificial neural network (ANN) can predict breast cancer invasion on the basis of readily available medical findings (ie, mammographic findings classified according to the American College of Radiology Breast Imaging Reporting and Data System and patient age). MATERIALS AND METHODS: In 254 adult patients, 266 lesions that had been sampled at biopsy were randomly selected for the study. There were 96 malignant and 170 benign lesions. On the basis of nine mammographic findings and patient age, a three-layer backpropagation network was developed to predict whether the malignant lesions were in situ or invasive. RESULTS: The ANN predicted invasion among malignant lesions with an area under the receiver operating characteristic curve (Az) of .91 +/- .03. It correctly identified all 28 in situ cancers (specificity, 100%) and 48 of 68 invasive cancers (sensitivity, 71%). CONCLUSION: The ANN used mammographic features and patient age to accurately classify invasion among breast cancers, information that was previously available only by means of biopsy. This knowledge may assist in surgical planning and may help reduce the cost and morbidity of unnecessary biopsy.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • April 1997

Published In

Volume / Issue

  • 203 / 1

Start / End Page

  • 159 - 163

PubMed ID

  • 9122385

Pubmed Central ID

  • 9122385

International Standard Serial Number (ISSN)

  • 0033-8419

Digital Object Identifier (DOI)

  • 10.1148/radiology.203.1.9122385


  • eng

Conference Location

  • United States