Although breast density is considered a strong predictor of breast cancer risk, its quantitative assessment is difficult. The aim of this study is to demonstrate that breast density assessment with a fully automated software is feasible and correlates with the semi-automated evaluation and the quantitative BI-RADS standards. A data set of 160 mammograms was evaluated by three blinded radiologists. Intra-observer (reader 1: k=0.71; reader 2: k=0.76; reader 3: k=0.62) and inter-observer (reader 1 vs reader 2: k=0.72; reader 2 vs reader 3: k=0.80; reader 3 vs reader 1: k=0.72) variability for the semi-automated software were good on a four-grade scale (D1/D2/D3/D4) and correlated with BI-RADS evaluation made by other two blinded radiologists (r=0.65, p<0.01). Inter-observer (reader 1 vs reader 2: k=0.85; reader 2 vs reader 3: k=0.91; reader 3 vs reader 1: k=0.85) variability for the semi-automated software was very good on a two-grade scale (D1-D2/D3-D4). The use of the fully automated software eliminated intra- and inter-observer differences, correlated with BI-RADS categories (r=0.62, p<0.01) and can replace the semi-automated one (Bland-Altman statistics). Our study demonstrates that automated estimation of breast density is feasible and eliminates subjectivity. Furthermore both the semi-automated and the fully automated density estimation are more accurate than BI-RADS quantitative evaluation and could also be used in the daily clinical practice.

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Given the increasingly important role of breast density as an independent risk factor for breast cancer, and the variable breast imaging tests that potentially provide measures for density. We compared breast tissue density on digital mammography (FFDM), digital breast tomosynthesis (DBT), and magnetic resonance imaging (MRI) using semi-automated automated software. These three imaging modalities have not been previously directly compared for estimating breast tissue density. Following informed consent from all participating women, FFDM, DBT, and MRI were performed. Breast percentage density was calculated with semi-automated software, and compared, for all three imaging modalities. 48 patients (mean age, 41 years; range, 35-67 years) underwent FFDM, DBT, and MRI. Percent FFDM, DBT, and MRI breast density measures showed a positive linear correlation, (r = 0.95 for MRI and DBT, P < 0.0001; r = 0.97, P < 0.0001 for FFDM and DBT; r = 0.87 for FFDM and MRI). Linear regression analysis related to MRI and DBT had a high r (2) = 0.89 (95 % CI = 0.88-0.99, P < 0.001). FFDM overestimated breast density in 15.1 % in comparison to DBT and in 16.2 % in comparison to MRI, or conversely each of DBT and MRI underestimated density (relative to FFDM) by 15.1 or 16.2 %, respectively. Differences in percentage breast density between FFDM and DBT, and between FFDM and MRI, were highly significant (P < 0.0001). Differences in percentage breast density between DBT and MRI were not significant (P > 0.05). Breast density measures using FFDM, DBT, or MRI were generally well-correlated, although differences were noted between estimates when comparing FFDM and DBT, and for estimates comparing FFDM and MRI. No signficant differences in percentage density were observed when comparing DBT and MRI. Our work highlight that differences between FFDM, DBT, and MRI should be considered when measuring percentage breast density.

Breast density is considered to be an independent risk factor for cancer Density can be assessed on full-field digital mammography and digital breast tomosynthesis Objective automated estimation of breast density eliminates subjectivity Automated estimation is more accurate than BI-RADS quantitative evaluation Breast density may be significantly underestimated on digital breast tomosynthesis.

PurposeTo compare MRI sequences for breast density measurements on a 3T MRI system using IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation) as possible physiology-like reference.Materials and MethodsMRI examination was performed in 48 consecutive patients (mean age 41, years; range, 35–67 years) on a 3.0T scanner and 46 were included. All (fertile) women, were examined between days 5 and 15 of their menstrual cycle. MRI protocol included: T1-turbo spin-echo (T1-tSE), T2-turbo spin-echo (T2-tSE), VIBRANT (Volume Imaging for Breast Assessment) before and after injection of contrast media and IDEAL. Breast density was calculated with semi-automated software. Statistical analysis was performed with non-parametric tests.ResultsMean percentage of breast density calculated in each sequence was: T1-tSE  = 56%; T2-tSE  = 52%; IDEAL FatOnly  = 55%; IDEAL WaterOnly  = 53%, VIBRANT  = 55%. Significant differences were observed between T2-tSE and both T1-tSE (p<0.001), VIBRANT sequences (p = 0.009), T1-tSE and both IDEAL WaterOnly (p = 0.007) and IDEAL FatOnly (p = 0.047). Breast density percentage showed a positive linear correlation among different sequences: r≥0.93.ConclusionsDifferences exist between MRI sequences used to assess breast density percentage. T1-weighted sequences values were similar to IDEAL sequences.

• The BI-RADS classification of MC differs for FFDM and DBT in 11/107 cases • DBT assigned lower BI-RADS classes compared to FFDM in 11 clusters • In 4/107 DBT may have missed some malignant and high-risk lesions • In 7/107 the 'underclassification' on DBT was correct, potentially avoiding unnecessary biopsies • DBT may miss a small proportion of malignant lesions.

Computerized BPE quantitative evaluation is feasible with both semi-automatic and automatic software. Computerized BPE quantitative scores correlate with radiologists' estimation.

On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.