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Generalized breast density metrics.

Erin E E Fowler1, Autumn Smallwood, Cassandra Miltich

  • 1Cancer Epidemiology Department, Moffitt Cancer Center & Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL 33612, United States of America.

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|December 8, 2018
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Summary
This summary is machine-generated.

Mammogram data analysis reveals that specific texture features, beyond overall breast density, can predict breast cancer risk. These findings apply to both indirect and direct x-ray conversion mammography technologies.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Mammography is crucial for breast cancer risk assessment.
  • Breast density (BD) is a known risk factor.
  • The potential of mammogram texture analysis for risk prediction requires further investigation.

Purpose of the Study:

  • To evaluate the association between mammographic texture features in the Fourier domain and breast cancer risk.
  • To compare the predictive performance of texture features with traditional breast density measurements.
  • To assess these associations across different mammography technologies.

Main Methods:

  • Analysis of two case-control study populations (N=180 and N=319 pairs) with mammograms from indirect and direct x-ray conversion units.
  • Decomposition of mammograms into radial spatial frequency bands using Fourier analysis.
  • Quantification of power in each frequency band and comparison with Cumulus-derived breast density.
  • Conditional logistic regression adjusted for BMI and ethnicity to assess breast cancer associations.

Main Results:

  • A specific low-frequency Fourier measure (0.083-0.166 cycles/mm) was significantly associated with breast cancer risk in both populations across raw, processed, and calibrated image representations.
  • Breast density (BD) also showed significant associations with risk in both populations.
  • Odds ratios (ORs) for the Fourier measure ranged from 1.38 to 1.76, and for BD from 1.43 to 1.72, indicating increased risk with higher values.

Conclusions:

  • Mammographic texture features, analyzed via Fourier domain decomposition, provide significant associations with breast cancer risk.
  • These texture-based risk predictors are effective across different mammography technologies (indirect and direct x-ray conversion).
  • Texture analysis offers complementary information to overall breast density for enhanced breast cancer risk assessment.