Quantitative Assessment of Breast Density: Transmission Ultrasound is Comparable to Mammography with Tomosynthesis.

Elevated breast density is among the strongest independent predictors of breast cancer. Breast density scores are critical inputs in models used to calculate a patient's lifetime risk of developing breast cancer. Today, the only FDA-cleared technology for assessing breast density uses mammography. An alternative modality for breast density quantification is 3D transmission ultrasound (TU). In this retrospective study, we compared automated breast density calculations derived from TU using quantitative breast density (QBD) and mammography with tomosynthesis using VolparaDensity 3.1 for 225 breasts. Pearson correlation coefficients (r) and intraclass correlation coefficients were compared. Subset analyses of extremely dense breasts, premenopausal, and postmenopausal breasts were also performed. Comparative analysis between radiologist-derived density assessment and objective automated scores was performed. Calculations from TU and mammography with tomosynthesis for breast density, total breast volume (TBV), and fibroglandular volume (FGV) were strongly correlated (r = 0.91, 0.92, and 0.67, respectively). We observed moderate absolute agreement for FGV and breast density, and strong absolute agreement for TBV. A subset of 56 extremely dense breasts showed similar trends, however with lower breast density agreement in the subset than in the full study. No significant difference existed in density correlation between premenopausal and postmenopausal breasts across modalities. QBD calculations from TU were strongly correlated with breast density scores from VolparaDensity. TU systematically measured higher FGV and breast density compared with mammography, and the difference increased with breast density. IMPACT: TU of the breast can accurately quantify breast density comparable with mammography with tomosynthesis.

Quantitative assessment of breast density using transmission ultrasound tomography.

PURPOSE: Breast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two-dimensional projections from mammography with or without tomosynthesis using either (a) subjective assessment or (b) a computer-aided approach. The purpose of this work is twofold: (a) to establish an algorithm for quantitative assessment of breast density using quantitative three-dimensional transmission ultrasound imaging; and (b) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density. METHODS: We described and verified a threshold-based segmentation algorithm to give a quantitative breast density (QBD) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating fibroglandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments. RESULTS: Quantitative breast density (QBD) measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between QBD and both subjective mammographic assessments of Breast Imaging - Reporting and Data System (BI-RADS) breast composition categories and Volpara density scores - the Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI: 0.71-0.96) and 0.96 (95% CI: 0.92-0.98), respectively. CONCLUSIONS: The calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and Food and Drug Administration (FDA)-cleared objective assessments of breast density.

The Impact of Patient Demographics on the Selection of Breast Imaging Centers.

OBJECTIVE: Studies show that health care tailored to patient preferences results in significant improvements in physician performance, patient satisfaction, and health outcomes. Limited information in the literature exists on the factors driving patient preferences for establishing care at specific breast imaging centers. In this study, we identified factors that drive cohort preferences in the selection of a breast imaging center. MATERIALS AND METHODS: An 18-question survey was deployed in a large metropolitan area to gather information on patient demographics and preferences for breast imaging center location and radiologist training level. Cluster analysis and the K-means method were used to classify patients into groups on the basis of their answers about preference. Clusters were tested for significant differences by location, reason for visit, age, education, marital status, ethnicity, insurance, history of cancer, and income. RESULTS: A total of 1682 survey responses (18% of total patient visits) were obtained. Four distinct cohorts (comprising 876 patients) based on patient care preferences were identified: convenience optimizers (n = 109, 12.4%), ambivalent patients (n = 237, 27.1%), medical center seekers (n = 324, 37.0%), and expertise seekers (n = 206, 23.5%). Each cohort showed distinct preferences for imaging center location and radiologist training. Cohorts were differentiated on the basis of patient education level, ethnicity, and patient cancer history. Across the cohorts, there were no significant differences in age, marital status, insurance, income, and other demographic factors. CONCLUSION: Patient preferences for breast imaging care and location vary and are correlated with specific demographic characteristics. An understanding of these population characteristics can shape organizational strategies for improving patient-centered care and outcomes.