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Acad Radiol. 2014 Aug;21(8):958-70. doi: 10.1016/j.acra.2014.04.006.

Automated Percentage of Breast Density Measurements for Full-field Digital Mammography Applications.

Author information

1
Department of Cancer Epidemiology, Division of Population Sciences, H. Lee Moffitt Cancer Center, Tampa, Florida.
2
Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota.
3
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center, 12901 Magnolia Drive, Tampa, FL 33612. Electronic address: john.heine@moffitt.org.

Abstract

RATIONALE AND OBJECTIVES:

Increased mammographic breast density is a significant risk factor for breast cancer. A reproducible, accurate, and automated breast density measurement is required for full-field digital mammography (FFDM) to support clinical applications. We evaluated a novel automated percentage of breast density measure (PDa) and made comparisons with the standard operator-assisted measure (PD) using FFDM data.

METHODS:

We used a nested breast cancer case-control study matched on age, year of mammogram and diagnosis with images acquired from a specific direct x-ray conversion FFDM technology. PDa was applied to the raw and clinical display (or processed) representation images. We evaluated the transformation (pixel mapping) of the raw image, giving a third representation (raw-transformed), to improve the PDa performance using differential evolution optimization. We applied PD to the raw and clinical display images as a standard for measurement comparison. Conditional logistic regression was used to estimate the odd ratios (ORs) for breast cancer with 95% confidence intervals (CI) for all measurements; analyses were adjusted for body mass index. PDa operates by evaluating signal-dependent noise (SDN), captured as local signal variation. Therefore, we characterized the SDN relationship to understand the PDa performance as a function of data representation and investigated a variation analysis of the transformation.

RESULTS:

The associations of the quartiles of operator-assisted PD with breast cancer were similar for the raw (OR: 1.00 [ref.]; 1.59 [95% CI, 0.93-2.70]; 1.70 [95% CI, 0.95-3.04]; 2.04 [95% CI, 1.13-3.67]) and clinical display (OR: 1.00 [ref.]; 1.31 [95% CI, 0.79-2.18]; 1.14 [95% CI, 0.65-1.98]; 1.95 [95% CI, 1.09-3.47]) images. PDa could not be assessed on the raw images without preprocessing. However, PDa had similar associations with breast cancer when assessed on 1) raw-transformed (OR: 1.00 [ref.]; 1.27 [95% CI, 0.74-2.19]; 1.86 [95% CI, 1.05-3.28]; 3.00 [95% CI, 1.67-5.38]) and 2) clinical display (OR: 1.00 [ref.]; 1.79 [95% CI, 1.04-3.11]; 1.61 [95% CI, 0.90-2.88]; 2.94 [95% CI, 1.66-5.19]) images. The SDN analysis showed that a nonlinear relationship between the mammographic signal and its variation (ie, the biomarker for the breast density) is required for PDa. Although variability in the transform influenced the respective PDa distribution, it did not affect the measurement's association with breast cancer.

CONCLUSIONS:

PDa assessed on either raw-transformed or clinical display images is a valid automated breast density measurement for a specific FFDM technology and compares well against PD. Further work is required for measurement generalization.

KEYWORDS:

Breast density; automated measure; breast cancer risk; differential evolution; full field digital mammography

PMID:
25018067
PMCID:
PMC4166439
DOI:
10.1016/j.acra.2014.04.006
[Indexed for MEDLINE]
Free PMC Article

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