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Related Experiment Video

Updated: Mar 15, 2026

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

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Correlating mammographic and pathologic findings in clinical decision support using natural language processing and

Tejal A Patel1,2,3, Mamta Puppala4,5, Richard O Ogunti4,5

  • 1Houston Methodist Cancer Center, Houston, Texas.

Cancer
|August 30, 2016
PubMed
Summary

Natural language processing (NLP) effectively extracts mammographic features from unstructured text, correlating imaging characteristics with breast cancer subtypes. This automated approach enhances data mining for clinical decision support in mammography research.

Keywords:
data miningimaging characteristicsmammographic to pathologic correlationnatural language processingsubtypes of breast cancer

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Area of Science:

  • Radiology and Medical Imaging
  • Oncology
  • Biomedical Informatics

Background:

  • Electronic health records contain valuable mammography data, but unstructured text hinders research.
  • Standardized parameters for data mining breast cancer imaging characteristics are lacking.

Purpose of the Study:

  • To develop and apply natural language processing (NLP) for automated extraction of mammographic features from unstructured text.
  • To correlate extracted mammographic features with breast cancer subtypes.
  • To validate NLP's utility in large-scale data analysis for mammography research.

Main Methods:

  • Utilized a data warehouse for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammograms and pathology reports.
  • Developed NLP algorithms to automatically extract mammographic and pathologic findings from free-text reports.
  • Analyzed correlations using one-way ANOVA and Fisher exact test.

Main Results:

  • NLP successfully extracted key characteristics for 543 patients.
  • Estrogen receptor-positive tumors showed a higher likelihood of spiculated margins (P=.0008).
  • HER2-overexpressing tumors were more frequently associated with heterogeneous and pleomorphic calcifications (P=.0078, P=.0002).

Conclusions:

  • Automated extraction of mammographic features using NLP correlates with pathologic breast cancer subtypes.
  • NLP findings validate trends previously identified through manual data collection.
  • NLP offers an automated solution for scalable data extraction and analysis, supporting clinical decision-making.