Area-based Density Research Articles

Abstract A119: Comparing the variability of two validated, quantitative breast density measures used in a community-based mammography registry across race and ethnicity

Abstract Background: Breast density, an independent predictor of breast cancer risk, can be measured in multiple ways. Area-based measures quantify breast tissue in a 2-dimensional (2D) space, whereas volumetric, 3-dimensional (3D) measures incorporate information on the spatial relationship of dense and non-dense tissues that is not captured by 2D measures. Prior studies have shown that both types of measures vary by race/ethnicity. We compared the consistency of the relationship between validated 2D and 3D breast density measures across racial and ethnic groups. Methods: This study used data from 920 women aged 18+ who received a full-field digital screening mammogram at one of 6 breast imaging facilities in central North Carolina between 2017-2018. Race and ethnicity were abstracted from electronic medical records. Quantitative breast density was measured from mammograms and included (1) Area-based density measured using the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA), an algorithm-based software package that produces 2D estimates of the amount of dense breast tissue and total breast area; and (2) Volumetric density measured using Volpara, which produces 3D estimates of breast fibroglandular tissue volume and total breast volume. Percent density was calculated as the ratio of dense or fibroglandular tissue to total breast area or volume. Spearman correlation coefficients (R) and 95% confidence intervals (CI) were used to quantify associations between 2D and 3D measures. Results: Participants were 33-86 years old (mean=57.9, SD=11.2) and identified as White (73%), Black (19%), Asian (3%), and Other race (6%). 5% identified as Hispanic. Percent density measures were highly correlated, with strong correlations across racial groups (White: R=0.88, 95% CI: 0.86, 0.90; Black: R=0.81, 95% CI: 0.75, 0.85; Asian: R=0.91, 95% CI: 0.81, 0.96; Other: R=0.86, 95% CI: 0.77, 0.92), and ethnic groups (Hispanic: R=0.82, 95% CI: 0.71, 0.90; non-Hispanic: R=0.88, 95% CI: 0.87, 0.90). Correlations between dense area and fibroglandular volume were lower, but were also consistent across racial groups (White: R=0.41, 95% CI: 0.35, 0.47; Black: R=0.42, 95% CI: 0.29, 0.54; Asian: R=0.33, 95% CI: -0.07, 0.64; Other: R=0.55, 95% CI: 0.32, 0.72). The correlation between dense area and fibroglandular volume was slightly stronger among Hispanic vs. non-Hispanic women (R=0.62, 95% CI: 0.41, 0.76 vs. R=0.39, 95% CI: 0.33, 0.44), although confidence intervals for the two estimates overlapped suggesting the difference was not statistically significant. Conclusions: Correlations between 2D and 3D percent density measures were consistent across racial and ethnic groups. These data suggest that use of 2D vs. 3D estimation tools is unlikely to contribute to between-study heterogeneity in racial/ethnic differences for studies of breast density in diverse populations. Generalizability for Asian and Hispanic women may be limited by the low prevalence of these groups in our sample. Studies with greater representation are needed for precise estimates in these groups. Citation Format: Brianna D. Taffe, Louise Henderson, Cherie Kuzmiak, Despina Kontos, Sarah Nyante. Comparing the variability of two validated, quantitative breast density measures used in a community-based mammography registry across race and ethnicity [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A119.

(Invited) Stepping in the Dark - Active Site Density and Turn over Frequency in PGM-Free Metal-Nitrogen Doped Carbon (M-N-C) ORR Fuel Cell Electrocatalysts

We honor Shimshon Gottesfeld’s several decade-long pioneering contributions to the science of fuel cells by sharing some past work on PGM catalysts, but also some very recent work on PGM-free metal-nitrogen doped carbon ORR electrocatalysts with a focus on the evaluation of the number and surface area-based density of metal (ion) surface sites using probe gas molecule adsorption and thermal desorption. We show how correlations of site density values with turn-over-frequency data can advance our understanding of PGM-free M-N-C ORR electrocatalysts and aid in a rational search for and synthesis of improved PGM-free fuel cell catalysts.

Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds

BackgroundThe percentage of mammographic dense tissue (PD) defined by pixel value threshold is a well-established risk factor for breast cancer. Recently there has been some evidence to suggest that an increased threshold based on visual assessment could improve risk prediction. It is unknown, however, whether this also applies to volumetric density using digital raw mammograms.MethodTwo case-control studies nested within a screening cohort (ages of participants 46–73 years) from Manchester UK were used. In the first study (317 cases and 947 controls) cases were detected at the first screen; whereas in the second study (318 cases and 935 controls), cases were diagnosed after the initial mammogram. Volpara software was used to estimate dense tissue height at each pixel point, and from these, volumetric and area-based PD were computed at a range of thresholds. Volumetric and area-based PDs were evaluated using conditional logistic regression, and their predictive ability was assessed using the Akaike information criterion (AIC) and matched concordance index (mC).ResultsThe best performing volumetric PD was based on a threshold of 5 mm of dense tissue height (which we refer to as VPD5), and the best areal PD was at a threshold level of 6 mm (which we refer to as APD6), using pooled data and in both studies separately. VPD5 showed a modest improvement in prediction performance compared to the original volumetric PD by Volpara with ΔAIC = 5.90 for the pooled data. APD6, on the other hand, shows much stronger evidence for better prediction performance, with ΔAIC = 14.52 for the pooled data, and mC increased slightly from 0.567 to 0.577.ConclusionThese results suggest that imposing a 5 mm threshold on dense tissue height for volumetric PD could result in better prediction of cancer risk. There is stronger evidence that area-based density with a 6 mm threshold gives better prediction than the original volumetric density metric.

A methodology for combining multiple commercial data sources to improve measurement of the food and alcohol environment: applications of geographical information systems.

Commercial data sources have been increasingly used to measure and locate community resources. We describe a methodology for combining and comparing the differences in commercial data of the food and alcohol environment. We used commercial data from two commercial databases (InfoUSA and Dun&Bradstreet) for 2003 and 2009 to obtain information on food and alcohol establishments and developed a matching process using computer algorithms and manual review by applying ArcGIS to geocode addresses, standard industrial classification and North American industry classification taxonomy for type of establishment and establishment name. We constructed population and area-based density measures (e.g. grocery stores) and assessed differences across data sources and used ArcGIS to map the densities. The matching process resulted in 8,705 and 7,078 unique establishments for 2003 and 2009, respectively. There were more establishments captured in the combined dataset than relying on one data source alone, and the additional establishments captured ranged from 1,255 to 2,752 in 2009. The correlations for the density measures between the two data sources was highest for alcohol outlets (r = 0.75 and 0.79 for per capita and area, respectively) and lowest for grocery stores/supermarkets (r = 0.32 for both). This process for applying geographical information systems to combine multiple commercial data sources and develop measures of the food and alcohol environment captured more establishments than relying on one data source alone. This replicable methodology was found to be useful for understanding the food and alcohol environment when local or public data are limited.

Fish abundances in shoreline habitats and submerged aquatic vegetation in a tidal freshwater embayment of the Potomac River

Submerged aquatic vegetation (SAV) is considered an important habitat for juvenile and small forage fish species, but many long-term recruitment surveys do not effectively monitor fish communities in SAV. To better understand the impact of recent large increases of SAV on the fish community in tidal freshwater reaches of the Potomac River, we compared traditional seine sampling from shore with drop ring sampling of SAV beds (primarily Hydrilla) in a shallow water (depths, <1.5m) embayment, Gunston Cove. To accomplish this, we developed species-specific catch efficiency values for the seine gear and calculated area-based density in both shoreline and SAV habitats in late summer of three different years (2007, 2008, and 2009). For the dominant species (Fundulus diaphanus, Lepomis macrochirus, Etheostoma olmstedi, Morone americana, Lepomis gibbosus, and Fundulus heteroclitus), density was nearly always higher in SAV, but overall, species richness was highest in shoreline habitats sampled with seines. Although historical monitoring of fish in Gunston Cove (and throughout Chesapeake Bay) is based upon seine sampling (and trawl sampling in deeper areas), the high densities of fish and larger areal extent of SAV indicated that complementary sampling of SAV habitats would produce more accurate trends in abundances of common species. Because drop ring samples cover much less area than seines and may miss rare species, a combination of methods that includes seine sampling is needed for biodiversity assessment. The resurgence of SAV in tidal freshwater signifies improving water quality, and methods we evaluated here support improved inferences about population trends and fish community structure as indicators of ecosystem condition.