Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening

Abstract

Purpose:To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials andMethods:In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted k statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results:Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with k values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion:Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns.q RSNA, 2015

Family History of Breast Cancer, Breast Density, and Breast Cancer Risk in a U.S. Breast Cancer Screening Population

“…Nonetheless, the contrast between the lowest and highest BI-RADS categories should be relatively robust to inter-observer variation. Furthermore, clinical BI-RADS density and commercially available automated measures of breast density show similar, strong associations with breast cancer (29). …”

Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures

“…First, we evaluated only a single area-based breast density estimation method by using data from a single institution. While area-based density is the most widely used type of density measure, future studies should also examine volume-based breast density measures (27), including three-dimensional measures developed for DBT (38). In addition, we evaluated this software using a single vendor.…”

Longitudinal Changes in Volumetric Breast Density with Tamoxifen and Aromatase Inhibitors

“…Full-field digital mammography (FFDM) has advanced the development of automated software that measures volumetric breast density in three-dimensions, and early studies confirm that volumetric breast density is predictive of breast cancer risk. (19–21) Research has not assessed response to treatment with tamoxifen and AI using volumetric density measures on FFDM, though a few studies using MRI suggest volumetric measures may more accurately measure density changes. (22,23) If volumetric density measures from FFDM provide precise estimates of longitudinal change in breast density, they may be used clinically to provide important prognostic information.…”