There’s not a lot of difference between automated and clinical assessments of dense breasts when it comes to estimating a given woman’s risk of getting breast cancer. However, breast density classification may vary substantially between the assessment methods—a finding that could impact decisionmaking around supplemental imaging.

Kathleen Brandt, MD, of the Mayo Clinic, and colleagues uncovered the classification gap when they compared BI-RADS clinical classifications with two commercially available automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass).

Their study is published ahead of print in Radiology.

The researchers looked at close to 2,000 patients with breast cancer and around 4,200 control subjects matched for age, race, exam date and mammography machine.

Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012.

Brandt and team found differences of up to 14 percent in dense tissue classification.

Specifically, Volpara classified 51 percent of women as having dense breasts, Quantra classified 37 percent as such and clinical BI-RADS classified 43 percent.

Clinical and automated measures showed similar associations with breast cancer. Odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (Volpara) 1.9 (Quantra) and 2.3 (BI-RADS).

As for case status, clinical BI-RADS assessment showed better discrimination than both Volpara and Quantra.

In their study discussion, Brandt et al. state that their key finding—automated methods and clinical/visual assessment are both associated with breast cancer risk, but the methods produce different proportions of women defined as having dense breasts—has two important implications for clinical practice.

First, the variance will likely skew the numbers of patients who receive information about having dense breasts and who are steered toward supplemental screening studies such as ultrasound, MRI and molecular breast imaging.

Second, on the basis of results from the study, “it would be essential to use the same automated density measurement method to assess individual longitudinal changes,” the authors write. “This will be especially important if breast density is used as a surrogate marker for treatment response,” as, for example, in cases where tamoxifen therapy is used to reduce density.