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Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

Keller BM, Chen J, Daye D, Conant EF, Kontos D - Breast Cancer Res. (2015)

Bottom Line: Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA. brad.keller@uphs.upenn.edu.

ABSTRACT

Introduction: Breast 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).

Methods: Digital 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.

Results: All 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).

Conclusions: Our 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.

No MeSH data available.


Related in: MedlinePlus

Relationship between BMI and breast density measures. The association between area-based and volumetric breast density versus BMI is provided for (a) area percent density (PD %), (b) volume percent density, (c) absolute dense area and (d) absolute dense volume. Cancer cases are demarcated by ‘x’; controls by ‘o’. Regression lines, equations, and Spearman correlations are also provided for reference. BMI body mass index
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Fig3: Relationship between BMI and breast density measures. The association between area-based and volumetric breast density versus BMI is provided for (a) area percent density (PD %), (b) volume percent density, (c) absolute dense area and (d) absolute dense volume. Cancer cases are demarcated by ‘x’; controls by ‘o’. Regression lines, equations, and Spearman correlations are also provided for reference. BMI body mass index

Mentions: Statistically significant (P < 0.05) correlations were observed between the different quantitative density estimates and BMI (Spearman correlation: ρ = −0.31–0.42). Absolute dense volume and BMI had the strongest correlation (ρ = 0.42, 95 % CI 0.38–0.53), whereas absolute dense area and BMI had the weakest correlation (ρ = 0.10, 95 % CI 0.00–0.21). Figure 3 shows correlation and linear regression plots for the different area and volumetric breast density measures versus BMI.Fig. 3


Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.

Keller BM, Chen J, Daye D, Conant EF, Kontos D - Breast Cancer Res. (2015)

Relationship between BMI and breast density measures. The association between area-based and volumetric breast density versus BMI is provided for (a) area percent density (PD %), (b) volume percent density, (c) absolute dense area and (d) absolute dense volume. Cancer cases are demarcated by ‘x’; controls by ‘o’. Regression lines, equations, and Spearman correlations are also provided for reference. BMI body mass index
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4549121&req=5

Fig3: Relationship between BMI and breast density measures. The association between area-based and volumetric breast density versus BMI is provided for (a) area percent density (PD %), (b) volume percent density, (c) absolute dense area and (d) absolute dense volume. Cancer cases are demarcated by ‘x’; controls by ‘o’. Regression lines, equations, and Spearman correlations are also provided for reference. BMI body mass index
Mentions: Statistically significant (P < 0.05) correlations were observed between the different quantitative density estimates and BMI (Spearman correlation: ρ = −0.31–0.42). Absolute dense volume and BMI had the strongest correlation (ρ = 0.42, 95 % CI 0.38–0.53), whereas absolute dense area and BMI had the weakest correlation (ρ = 0.10, 95 % CI 0.00–0.21). Figure 3 shows correlation and linear regression plots for the different area and volumetric breast density measures versus BMI.Fig. 3

Bottom Line: Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA. brad.keller@uphs.upenn.edu.

ABSTRACT

Introduction: Breast 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).

Methods: Digital 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.

Results: All 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).

Conclusions: Our 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.

No MeSH data available.


Related in: MedlinePlus