Abstract
IntroductionBreast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA).MethodsDigital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided.ResultsAll automated density measures had a significant association with breast cancer (OR = 1.47–2.23, AUC = 0.59–0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96–2.64, AUC = 0.82–0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80).ConclusionsOur study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0626-8) contains supplementary material, which is available to authorized users.
Highlights
Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor
The trial recruited a total of 901 women, each of whom met at least one of the following inclusion criteria: they were presenting for staging with newly diagnosed breast cancer, or they had a mammographically detected suspicious finding (BI-RADS of at least 4) after screening or diagnostic evaluation and were directed to biopsy, or they had an otherwise-suspicious palpable mass directed to biopsy, or they were evaluated to be at a high risk for developing breast cancer as determined by an estimated high lifetime risk of more than 25 %, or they had a recently diagnosed contralateral breast cancer
In terms of mammographic density estimated via automated software, higher absolute density estimates were significantly associated with cancer status (P < 0.001), as were the percent density estimates (P ≤ 0.002), regardless of whether area or volumetric density measures were considered, whereas Breast Imaging-Reporting and Data System (BI-RADS) density estimates were not significant (P = 0.65)
Summary
Commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. Estimated via visual assessment either qualitatively by using the American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) density categories [13] or quantitatively as percent density (PD %) [14], mammographic density has been consistently shown to be an independent risk factor for breast cancer [14,15,16,17,18,19,20,21,22], potentially the strongest after age [14] This has led to the development of several fully automated breast density algorithms such as the automated ImageJ method [23] and the standardized measures of area density proposed by Heine et al [24]. Measures of the volumetric amount of dense tissue have been proposed as more accurate representations of the underlying fibroglandular tissue content and, as such, potentially better predictors of risk [21, 25,26,27,28,29]
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