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Related Experiment Video

Updated: Apr 17, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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Using natural language processing to extract mammographic findings.

Hongyuan Gao1, Erin J Aiello Bowles1, David Carrell1

  • 1Group Health Research Institute, Seattle, WA, USA.

Journal of Biomedical Informatics
|February 10, 2015
PubMed
Summary
This summary is machine-generated.

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A new natural language processing (NLP) system efficiently extracts mammographic findings like masses and calcifications from reports. This automated approach improves data collection for mammography analysis.

Area of Science:

  • Radiology and Medical Imaging
  • Natural Language Processing (NLP)
  • Health Informatics

Background:

  • Manual review of mammography reports is time-consuming and hinders structured data acquisition.
  • Extracting specific mammographic findings (mass, calcification, asymmetry, architectural distortion) from free-text reports is challenging.

Purpose of the Study:

  • To develop and evaluate a rule-based NLP system for automated extraction of mammographic findings from unstructured reports.
  • To assess the accuracy and feasibility of using NLP for structured data extraction in mammography.

Main Methods:

  • A dictionary look-up method was employed to identify four key mammographic findings in 93,705 reports.
  • Association rules were used to link findings with status and location annotations, excluding negated or historical mentions.
Keywords:
EvaluationMammographic findingsNatural language processingSAS-based

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  • Confidence flags were implemented to indicate the reliability of NLP results, with a subset of 100 reports manually reviewed for accuracy assessment.

    • Main Results:

    • The NLP system demonstrated high accuracy, correctly coding 96-99% of reports in a sample of 100.
    • Sensitivity, specificity, and negative predictive values exceeded 0.92 for all extracted mammographic findings.
    • Positive predictive values varied due to the low prevalence of certain findings.

    Conclusions:

    • The developed NLP system, implemented in SAS Base, successfully extracts clinically valuable information from mammography reports.
    • The system's portability and ease of implementation in SAS make it a practical tool for improving manual review efficiency.
    • Further refinements and testing on diverse datasets are recommended to enhance performance.