Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories

Objective:

To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging–Reporting and Data System (BI-RADS) categories, using automated software.

Methods:

Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity^©, developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists'' visual BI-RADS density classification.

Results:

The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively).

Conclusion:

Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk.

Advances in knowledge:

On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.To tailor screening and diagnosis protocols, it is important to identify females with an increased risk of breast cancer [1–3]. It has been estimated that females with dense breasts (breast densities of >75%) have 4–6 times higher risk of breast cancer than females with low breast densities [4] and that breast density is increasingly recognised as an independent determinant of breast cancer risk and possibly in prognosis [5]. Assessment of breast density is becoming crucial in epidemiological studies, including the estimation of breast cancer risk and assessing breast density-related risk over time, radiation dose monitoring and monitoring drug-related response [6,7].Different methods and classifications have been reported to assess breast density: the Tabar classification [8], Wolfe''s parenchymal patterns [9], and both semi-quantitative and quantitative computer-aided techniques [10–16]. The Breast Imaging–Reporting and Data System (BI-RADS) classification, considered as the additional quantitative scheme, is routinely used in the USA and was introduced to standardise reporting. Initially, it was based on four qualitative categories but an additional quantitative scheme was added in 2003, based on the extent of fibroglandular tissue [17]. Mammographic breast density estimation may be limited by the two-dimensional (2D) nature of the imaging technique, whereas a three-dimensional (3D) imaging modality, such as digital breast tomosynthesis (DBT), reduces the appearance of the overlapping parenchymal tissue and may therefore influence or alter density assessments [13,14]. In DBT, high-spatial-resolution tomographic images of the breast are reconstructed from multiple low-dose projection images acquired within a limited range of X-ray tube angles [15]. It has been demonstrated in a few studies that the automated estimation of breast density eliminates subjectivity between comparisons of full-field digital mammography (2D FFDM) and DBT and is more reproducible than a quantitative BI-RADS evaluation [14,16]. However, previous research mainly considered patients with relatively high breast density, with the possibility of the results not being applicable across all density categories and showing whether published percentage breast density differences between 2D FFDM and DBT apply to less dense or non-dense breasts. The purpose of our study was to compare the breast tissue density estimated using 2D FFDM and DBT among patients in a balanced data set of the four BI-RADS categories, using fully automated software.