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Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a

Gretchen L Gierach1, Hui Li, Jennifer T Loud

  • 1Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rm, 7-E108, Bethesda 20892-9774, MD, USA. gierachg@mail.nih.gov.

Breast Cancer Research : BCR
|August 28, 2014
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Summary

Computer analysis of mammograms can help identify women with BRCA1/2 mutations. Radiographic texture analysis (RTA) shows promise in distinguishing BRCA1/2 carriers from non-carriers, aiding in risk stratification.

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Area of Science:

  • Medical Imaging
  • Genetics
  • Artificial Intelligence

Background:

  • Mammographic density is similar in women at risk for sporadic or BRCA1/2-related breast cancer.
  • Digitized mammograms may contain computer-extractable parenchymal pattern information.
  • This information could potentially differentiate BRCA1/2 mutation carriers from non-carriers.

Purpose of the Study:

  • To compare mammographic texture pattern features between BRCA1/2 mutation carriers and non-carriers.
  • To develop and evaluate a radiographic texture analysis (RTA) classifier for distinguishing BRCA1/2 carriers.
  • To assess the potential of texture features in improving mammographic interpretation for risk stratification.

Main Methods:

  • Compared texture features in digitized mammograms from 137 BRCA1/2 mutation carriers and 100 non-carriers.
  • Utilized a training dataset (107 carriers, 70 non-carriers) for classifier development.
  • Employed stepwise linear regression and a Bayesian Artificial Neural Network (BANN) algorithm to create an RTA classifier.
  • Evaluated classifier performance on an independent testing dataset (30 carriers, 30 non-carriers).

Main Results:

  • The BANN-trained classifier showed a two-fold increase in the odds of predicting BRCA1/2 mutation status per one standard deviation increase in probability score (OR=1.93, P=0.03).
  • Mammographic density adjustment minimally impacted the odds ratio.
  • The area under the curve (AUC) for the classifier was 0.68 for texture features alone and 0.72 when including percent mammographic density.

Conclusions:

  • Computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations.
  • These texture features appear more informative than percent mammographic density for distinguishing carriers.
  • The developed RTA classifier shows early potential for real-time risk stratification in screening mammography.