Jafi Lipson

Publication Details

  • Automatic classification of mammography reports by BI-RADS breast tissue composition class JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Percha, B., Nassif, H., Lipson, J., Burnside, E., Rubin, D. 2012; 19 (5): 913-916

    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.

    View details for DOI 10.1136/amiajnl-2011-000607

    View details for Web of Science ID 000307934600032

    View details for PubMedID 22291166

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