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

Publication ,  Journal Article
Lo, JY; Baker, JA; Kornguth, PJ; Iglehart, JD; Floyd, CE
Published in: Radiology
April 1997

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.

Duke Scholars

Published In

Radiology

DOI

ISSN

0033-8419

Publication Date

April 1997

Volume

203

Issue

1

Start / End Page

159 / 163

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Neoplasm Invasiveness
  • Mammography
  • Humans
  • Female
 

Citation

APA
Chicago
ICMJE
MLA
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Lo, J. Y., Baker, J. A., Kornguth, P. J., Iglehart, J. D., & Floyd, C. E. (1997). Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. Radiology, 203(1), 159–163. https://doi.org/10.1148/radiology.203.1.9122385
Lo, J. Y., J. A. Baker, P. J. Kornguth, J. D. Iglehart, and C. E. Floyd. “Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.Radiology 203, no. 1 (April 1997): 159–63. https://doi.org/10.1148/radiology.203.1.9122385.
Lo JY, Baker JA, Kornguth PJ, Iglehart JD, Floyd CE. Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. Radiology. 1997 Apr;203(1):159–63.
Lo, J. Y., et al. “Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.Radiology, vol. 203, no. 1, Apr. 1997, pp. 159–63. Pubmed, doi:10.1148/radiology.203.1.9122385.
Lo JY, Baker JA, Kornguth PJ, Iglehart JD, Floyd CE. Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. Radiology. 1997 Apr;203(1):159–163.

Published In

Radiology

DOI

ISSN

0033-8419

Publication Date

April 1997

Volume

203

Issue

1

Start / End Page

159 / 163

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Neoplasm Invasiveness
  • Mammography
  • Humans
  • Female