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Updated: Mar 18, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Using automatically extracted information from mammography reports for decision-support.

Selen Bozkurt1, Francisco Gimenez2, Elizabeth S Burnside3

  • 1Akdeniz University Faculty of Medicine, Department of Biostatistics and Medical Informatics, Antalya, Turkey.

Journal of Biomedical Informatics
|July 9, 2016
PubMed
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This summary is machine-generated.

A new system using natural language processing (NLP) and Bayesian networks accurately supports breast cancer diagnosis from mammography reports. This technology improves diagnostic consistency and decision-making in radiology workflows.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Radiology
  • Computational Linguistics

Background:

  • Mammography reports contain crucial information for breast cancer diagnosis.
  • Interpreting these reports can involve variability in clinical practice.
  • Automated systems can aid in standardizing diagnostic assessments.

Purpose of the Study:

  • To evaluate an integrated system combining natural language processing (NLP) and Bayesian networks (BN) for breast cancer diagnosis decision support.
  • To assess the system's ability to extract information from mammography reports and predict malignancy.
  • To provide decision support within the radiology report generation workflow.

Main Methods:

  • Developed an NLP system to extract BI-RADS descriptors and clinical information from mammography reports.
Keywords:
Breast Imaging Reporting and Data System (BI-RADS)Decision support systemsInformation extractionNatural language processing

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  • Integrated the NLP system with a BN decision support system (DSS) to estimate lesion malignancy.
  • Compared the performance of the DSS using NLP-derived inputs (NLP-DSS) versus reference standard inputs (RS-DSS) on 300 mammography reports.

    • Main Results:

    • The NLP-DSS and RS-DSS showed closely matched probabilities of malignancy (concordance correlation coefficient of 0.95).
    • The mean paired difference between NLP-DSS and RS-DSS probabilities was minimal (0.004±0.025).
    • The NLP-DSS achieved 97.58% accuracy in predicting the correct BI-RADS final assessment category.

    Conclusions:

    • The NLP system's information extraction accuracy is sufficient for reliable decision support.
    • The integrated system demonstrates potential to reduce practice variation in mammography assessment.
    • This technology can enhance management decisions for malignant breast lesions.