Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population

Objective: Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic’s Food and Drug Administration–approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories. Methods: Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR’s BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density–based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities. Results: The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%. Conclusions: Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase