Automatic classification of mammography reports by BI-RADS breast tissue composition class

J Am Med Inform Assoc. Sep-Oct 2012;19(5):913-6. doi: 10.1136/amiajnl-2011-000607. Epub 2012 Jan 29.

Abstract

Because breast tissue composition partially predicts breast cancer risk, classification of mammography reports by breast tissue composition is important from both a scientific and clinical perspective. A method is presented for using the unstructured text of mammography reports to classify them into BI-RADS breast tissue composition categories. An algorithm that uses regular expressions to automatically determine BI-RADS breast tissue composition classes for unstructured mammography reports was developed. The algorithm assigns each report to a single BI-RADS composition class: 'fatty', 'fibroglandular', 'heterogeneously dense', 'dense', or 'unspecified'. We evaluated its performance on mammography reports from two different institutions. The method achieves >99% classification accuracy on a test set of reports from the Marshfield Clinic (Wisconsin) and Stanford University. Since large-scale studies of breast cancer rely heavily on breast tissue composition information, this method could facilitate this research by helping mine large datasets to correlate breast composition with other covariates.

Publication types

  • Multicenter Study
  • Research Support, N.I.H., Extramural
  • Validation Study

MeSH terms

  • Algorithms
  • Breast / pathology*
  • Data Mining / methods*
  • Female
  • Humans
  • Mammography / classification*
  • Natural Language Processing*
  • Radiology Information Systems / classification*
  • Risk Assessment
  • Sensitivity and Specificity
  • United States