Automatic classification of mammography reports by BI-RADS breast tissue composition class
- PMID: 22291166
- PMCID: PMC3422822
- DOI: 10.1136/amiajnl-2011-000607
Automatic classification of mammography reports by BI-RADS breast tissue composition class
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.
Conflict of interest statement
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References
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- Boyd NF, Rommens JM, Vogt K, et al. Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol 2005;6:798–808 - PubMed
-
- Martin LJ, Melnichouk O, Guo H, et al. Family history, mammographic density, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 2010;19:456–63 - PubMed
-
- Carney PA, Miglioretti DL, Yankaskas BC, et al. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med 2003;138:168–75 - PubMed
-
- American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS). 3rd edn Reston, VA: American College of Radiology, 2003
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