Pubertal mammary gland development is a key determinant of adult mammographic density.

Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.

Mechanical Pressure Driving Proteoglycan Expression in Mammographic Density: a Self-perpetuating Cycle?

Regions of high mammographic density (MD) in the breast are characterised by a proteoglycan (PG)-rich fibrous stroma, where PGs mediate aligned collagen fibrils to control tissue stiffness and hence the response to mechanical forces. Literature is accumulating to support the notion that mechanical stiffness may drive PG synthesis in the breast contributing to MD. We review emerging patterns in MD and other biological settings, of a positive feedback cycle of force promoting PG synthesis, such as in articular cartilage, due to increased pressure on weight bearing joints. Furthermore, we present evidence to suggest a pro-tumorigenic effect of increased mechanical force on epithelial cells in contexts where PG-mediated, aligned collagen fibrous tissue abounds, with implications for breast cancer development attributable to high MD. Finally, we summarise means through which this positive feedback mechanism of PG synthesis may be intercepted to reduce mechanical force within tissues and thus reduce disease burden.

Biomarkers of mammographic density in premenopausal women.

BACKGROUND: While mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal women. Then, we aimed to identify potential correlates of these biomarkers to guide future research on potential modifiable determinants of mammographic density. METHODS: A total of 132 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, hexose) were measured by tandem liquid chromatography/mass spectrometry in plasma samples from 573 premenopausal participants in the Mexican Teachers' Cohort. Associations between metabolites and percent mammographic density were assessed using linear regression models, adjusting for breast cancer risk factors and accounting for multiple tests. Mean concentrations of metabolites associated with percent mammographic density were estimated across levels of several lifestyle and metabolic factors. RESULTS: Sphingomyelin (SM) C16:1 and phosphatidylcholine (PC) ae C30:2 were inversely associated with percent mammographic density after correction for multiple tests. Linear trends with percent mammographic density were observed for SM C16:1 only in women with body mass index (BMI) below the median (27.4) and for PC ae C30:2 in women with a BMI over the median. SM C16:1 and PC ae C30:2 concentrations were positively associated with cholesterol (total and HDL) and inversely associated with number of metabolic syndrome components. CONCLUSIONS: We identified new biomarkers associated with mammographic density in young women. The association of these biomarkers with mammographic density and metabolic parameters may provide new perspectives to support future preventive actions for breast cancer.

Benign breast disease and changes in mammographic breast density.

BACKGROUND: Mammographic breast density (MBD) and benign breast disease (BBD) are two of the strongest risk factors for breast cancer. Understanding trends in MBD by age and parity in women with BBD is essential to the clinical management and prevention of breast cancer. METHODS: Using data from the Early Determinants of Mammographic Density (EDMD) study, a prospective follow-up study of women born in 1959-1967, we evaluated MBD in 676 women. We used linear regression with generalized estimating equations to examine associations between self-reported BBD and MBD (percent density, dense area, and non-dense area), assessed through a computer-assisted method. RESULTS: A prior BBD diagnosis (median age at diagnosis 32 years) was reported by 18% of our cohort. The median time from BBD diagnosis to first available study mammogram was 9.4 years (range 1.1-27.6 years). Women with BBD had a 3.44% higher percent MBD (standard error (SE) = 1.56, p-value = 0.03) on their first available mammogram than women without BBD. Compared with parous women without BBD, nulliparous women with BBD and women with a BBD diagnosis prior to first birth had 7-8% higher percent MBD (ß = 7.25, SE = 2.43, p-value< 0.01 and ß = 7.84, SE = 2.98, p-value = 0.01, respectively), while there was no difference in MBD in women with a BBD diagnosis after the first birth (ß = -0.22, SE = 2.40, p-value = 0.93). CONCLUSION: Women with self-reported BBD had higher mammographic breast density than women without BBD; the association was limited to women with BBD diagnosed before their first birth.

Adolescent and early adulthood inflammation-associated dietary patterns in relation to premenopausal mammographic density.

BACKGROUND: Adolescence and early adulthood has been identified as a critical time window for establishing breast cancer risk. Mammographic density is an independent risk factor for breast cancer that may be influenced by diet, but there has been limited research conducted on the impact of diet on mammographic density. Thus, we sought to examine the association between adolescent and early adulthood inflammatory dietary patterns, which have previously been associated with breast cancer risk, and premenopausal mammographic density among women in the Nurses' Health Study II (NHSII). METHODS: This study included control participants with premenopausal mammograms from an existing breast cancer case-control study nested within the NHSII who completed a Food Frequency Questionnaire in 1998 about their diet during high school (HS-FFQ) (n = 685) and/or a Food Frequency Questionnaire in 1991 (Adult-FFQ) when they were 27-44 years old (n = 1068). Digitized analog film mammograms were used to calculate the percent density, absolute dense, and non-dense areas. Generalized linear models were fit to evaluate the associations of a pro-inflammatory dietary pattern and the Alternative Healthy Eating Index (AHEI, an anti-inflammatory dietary pattern) with each breast density measure. RESULTS: Significant associations were observed between an adolescent pro-inflammatory dietary pattern and mammographic density in some age-adjusted models; however, these associations did not remain after adjustment for BMI and other breast cancer risk factors. No associations were observed with the pro-inflammatory pattern or with the AHEI pattern in adolescence or early adulthood in fully adjusted models. CONCLUSIONS: To our knowledge, this is the first study to evaluate the dietary patterns during adolescence and early adulthood in relation to mammographic density phenotypes. Our findings do not support an association between adolescent and early adulthood diet and breast density in mid-adulthood that is independent of BMI or other breast cancer risk factors.

Outcomes of Return to Routine Screening for BI-RADS 3 Lesions Detected at Supplemental Automated Whole-Breast Ultrasound in Women With Dense Breasts: A Prospective Study.

BACKGROUND. Supplemental screening breast ultrasound (US) detects additional cancers in women with dense breasts but identifies many BI-RADS 3 lesions that result in short-term follow-up and biopsies. OBJECTIVE. The purpose of this study was to evaluate outcomes in patients recommended for return to routine screening for lesions assessed as BI-RADS 3 on supplemental automated whole-breast US. METHODS. This prospective study invited patients with BI-RADS 1 or 2 on screening mammography and breast density C or D to undergo supplemental automated breast US (ABUS). ABUS was interpreted as BI-RADS 1, 2, 3, or 0. Return to routine screening was recommended for ABUS BI-RADS 1, 2, or 3. ABUS BI-RADS 0 lesions underwent targeted handheld US. Remaining patients were followed for 2 years. Malignancy rates were compared using Fisher exact tests. RESULTS. A total of 2257 women (mean age, 58.0 ± 11.2 [SD] years) were included. Supplemental ABUS was scored as BI-RADS 1 in 1186 (52.5%) women, BI-RADS 2 in 591 (26.2%), BI-RADS 3 in 395 (17.5%), and BI-RADS 0 in 85 (3.8%). A total of 394 patients with ABUS BI-RADS 3 had 2-year follow-up, during which no cancer (0%; 95% CI, 0.0-0.9%) was diagnosed in the quadrant of the lesion. Among patients with 2-year follow-up, breast cancer was diagnosed in 4/1117 (0.4%) with ABUS BI-RADS 1, 2/556 (0.4%) with ABUS BI-RADS 2, and 2/394 (0.5%) with ABUS BI-RADS 3 (cancer in other quadrant than the lesion). Malignancy rates were not different between ABUS BI-RADS 1, 2, and 3 (p = .28). The ABUS recall rate was 3.8% (85/2257; 95% CI, 3.6-4.0%). If short-term follow-up had been recommended for ABUS BI-RADS 3, the ABUS recall rate would have been 21.3% (480/2257, 95% CI 19.6-23.0%). The biopsy rate was 0.5% (12/2257; 95% CI, 0.3-0.9%); the positive biopsy rate was 58.3% (7/12). One of seven cancers diagnosed by initial supplemental ABUS and none of eight cancers diagnosed during subsequent follow-up were node-positive cancer. CONCLUSION. Return to routine screening for ABUS BI-RADS 3 lesions results in a substantial decrease in recall rate and is unlikely to result in an adverse outcome. CLINICAL IMPACT. This prospective study supports a recommendation for routine annual follow-up for BI-RADS 3 lesions at supplemental ABUS. TRIAL REGISTRATION. ClinicalTrials.gov NCT02650778.

Prior breast density awareness, knowledge, and communication in a health system-embedded behavioral intervention trial.

BACKGROUND: Breast density is an important breast cancer risk factor and a focus of recent national and state health policy efforts. This article describes breast density awareness, knowledge, and communication among participants in a health system-embedded trial with clinically elevated breast cancer risk 1 year before state-mandated density disclosure. METHODS: Trial participants' demographics and prior health history were ascertained from electronic health records. The proportions of women reporting prior breast density awareness, knowledge of density's masking effect, and communication with a provider about their own breast density were calculated using baseline interview data collected from 2017 to 2018. Multiple logistic regression was used to estimate associations between women's characteristics and density awareness, knowledge, and communication. RESULTS: Although the overwhelming majority of participants had heard of breast density (91%) and were aware of breast density's masking effect (87%), only 60% had ever discussed their breast density with a provider. Annual mammography screening was associated with prior breast density awareness (odds ratio [OR], 2.97; 95% confidence interval [CI], 1.29-6.81), knowledge (OR, 2.83; 95% CI, 1.20-6.66), and communication (OR, 2.87; 95% CI, 1.34-6.16) compared with an infrequent or unknown screening interval. Receipt of breast biopsy was also associated with prior knowledge (OR, 1.60; 95% CI, 1.04-2.45) and communication (OR, 1.36; 95% CI, 1.00-1.85). CONCLUSIONS: Breast density awareness and knowledge are high among insured women participating in clinical research, even in the absence of mandated density disclosure. Patient-provider communication about personal density status is less common, particularly among women with fewer interactions with breast health specialists.

Tumor microenvironment and breast cancer survival: combined effects of breast fat, M2 macrophages and hyaluronan create a dismal prognosis.

PURPOSE: Tumor microenvironment, including inflammatory cells, adipocytes and extracellular matrix constituents such as hyaluronan (HA), impacts on cancer progression. Systemic metabolism also influences tumor growth e.g. obesity and type 2 diabetes (T2D) are risk factors for breast cancer. Here, in 262 breast cancer cases, we explored the combined impacts on survival of M2-like tumor associated macrophages (TAMs), the abundance of breast fat visualized as low density in mammograms, and tumor HA, and their associations with T2D. METHODS: Mammographic densities were assessed visually from the diagnostic images and dichotomized into very low density (VLD, density ≤ 10%, "fatty breast") and mixed density (MID, density > 10%). The amounts of TAMs (CD163+ and CD68+) and tumor HA were determined by immunohistochemistry. The data of T2D was collected from the patient records. Statistical differences between the parameters were calculated with Chi square or Mann-Whitney test and survival analyses with Cox's model. RESULTS: A combination of fatty breasts (VLD), abundance of M2-like TAMs (CD163+) and tumor HA associated with poor survival, as survival was 88-89% in the absence of these factors but only 40-47% when all three factors were present (p < 0.001). Also, an association between T2D and fatty breasts was found (p < 0.01). Furthermore, tumors in fatty breasts contained more frequently high levels of M2-like TAMs than tumors in MID breasts (p = 0.01). CONCLUSIONS: Our results demonstrate a dramatic effect of the tumor microenvironment on breast cancer progression. We hypothesize that T2D as well as obesity increase the fat content of the breasts, subsequently enhancing local pro-tumoral inflammation.

Serum Phospholipid Fatty Acids and Mammographic Density in Premenopausal Women.

BACKGROUND: The role of fatty acids (FAs) on mammographic density (MD) is unclear, and available studies are based on self-reported dietary intake. OBJECTIVES: This study assessed the association between specific serum phospholipid fatty acids (PLFAs) and MD in premenopausal women. METHODS: The cross-sectional study DDM-Madrid recruited 1392 Spanish premenopausal women, aged 39-50 y, who attended a screening in a breast radiodiagnosis unit of Madrid City Council. Women completed lifestyle questionnaires and FFQs. Percentage MD was estimated using a validated computer tool (DM-Scan), and serum PLFA percentages were measured by GC-MS. Multivariable linear regression models were used to quantify the association of FA tertiles with MD. Models were adjusted for age, education, BMI, waist circumference, parity, oral contraceptive use, previous breast biopsies, and energy intake, and they were corrected for multiple testing. RESULTS: Women in the third tertile of SFAs showed significantly higher MD compared with those in the first tertile (ßT3vsT1 = 7.53; 95% CI: 5.44, 9.61). Elevated relative concentrations of palmitoleic (ßT3vsT1 = 3.12; 95% CI: 0.99, 5.25) and gondoic (ßT3vsT1 = 2.67; 95% CI: 0.57, 4.77) MUFAs, as well as high relative concentrations of palmitelaidic (ßT3vsT1 = 5.22; 95% CI: 3.15, 7.29) and elaidic (ßT3vsT1 = 2.69; 95% CI: 0.59, 4.79) trans FAs, were also associated with higher MD. On the contrary, women with elevated relative concentrations of n-6 (ω-6) linoleic (ßT3vsT1 = -5.49; 95% CI; -7.62, -3.35) and arachidonic (ßT3vsT1 = -4.68; 95% CI: -6.79, -2.58) PUFAs showed lower MD. Regarding desaturation indices, an elevated palmitoleic to palmitic ratio and a low ratio of oleic to steric and arachidonic to dihomo-γ-linolenic acids were associated with higher MD. CONCLUSIONS: Spanish premenopausal women with high relative concentrations of most SFAs and some MUFAs and trans FAs showed an increased MD, whereas those with high relative concentrations of some n-6 PUFAs presented lower density. These results, which should be confirmed in further studies, underscore the importance of analyzing serum FAs individually.

Adolescent caffeine consumption and mammographic breast density in premenopausal women.

PURPOSE: Previous studies suggest that coffee and caffeine intake may be associated with reduced breast cancer risk. To date, there is limited and inconsistent epidemiologic evidence for associations of adolescent diet with mammographic breast density, a strong and consistent predictor of breast cancer. We investigated the association of adolescent caffeine intake with mammographic density in premenopausal women. METHODS: This study included 751 cancer-free women within the Nurses' Health Study II cohort. Percent breast density (PD), absolute dense (DA) and non-dense areas (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root-transformed. Energy-adjusted adolescent caffeine intake was estimated using the data from a food frequency questionnaire. Information regarding breast cancer risk factors was obtained from questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations of caffeine intake with breast density measures. RESULTS: In multivariable analyses, adolescent caffeine intake was not associated with any of the density phenotypes (caffeine 4th vs. 1st quartile: ß = - 1.27, 95% CI - 4.62; 2.09, p-trend = 0.55 for percent density; ß = - 0.21, 95% CI - 0.76, 0.34, p-trend = 0.65 for absolute dense area, and ß = 0.23, 95% CI - 0.28, 0.74, p-trend = 0.50 for non-dense area). Additional adjustment of the models for body mass index at age 18 resulted in attenuation of the risk estimates. CONCLUSIONS: Our findings do not support the hypothesis that adolescent caffeine intake is associated with premenopausal mammographic breast density.

Comparison of sonoelastographic values of breast tissue with mammographically and ultrasonically assessed density: a cross-sectional study.

AIM: To determine the relationship between breast stiffness assessed with sonoelastography (elasticity) and breast tissue density assessed with mammography (MG) and ultrasound (US). METHODS: This cross-sectional study involved 100 women who underwent MG, gray-scale US, and shear-wave sonoelastography during 2013. Mammographic density was categorized into four groups and sonographic density into three groups according to Breast Imaging-Reporting and Data System criteria. The stiffness of breast parenchymal and adipose tissue in all breast quadrants was quantified by shear-wave sonoelastography. Mean elastographic estimates were compared with MG- and US-derived density estimates. RESULTS: Parenchymal and adipose tissue elasticity positively correlated with MG- and US-derived breast density (for parenchyma: for MG Kendall's tau b 0.522; Jonckheere-Terpstra test P<0.001 and for US Kendall's tau b 0.533; Jonckheere-Terpstra test P<0.001); the higher was the breast density on MG and US, the higher was the elastographic stiffness. CONCLUSION: Sonoelastographic breast stiffness strongly positively correlated with breast density. Thus, sonoelastography may have a potential for estimating the breast cancer risk, which allows a novel application of this technique in routine clinical practice.

Do Birth Weight and Weight Gain During Infancy and Early Childhood Explain Variation in Mammographic Density in Women in Midlife? Results From Cohort and Sibling Analyses.

High birth weight is associated with increased breast cancer risk and, less consistently, with higher mammographic density. In contrast, adolescent body size has been consistently, negatively associated with both MD and breast cancer risk. It is unclear when the direction of these associations changes and whether weight gain in infancy is associated with MD. We evaluated the associations of birth weight and postnatal weight (measured at 4 months, 1 year, and 4 years) by absolute and velocity measures (relative within-cohort percentile changes) with adult mammographic density, assessed using a computer-assisted thresholding program (Cumulus), using linear regression models with generalized estimating equations to account for correlation between siblings in the Early Determinants of Mammographic Density study (1959-2008; n = 700 women with 116 sibling sets; mean age = 44.1 years). Birth weight was positively associated with dense area (per 1-kg increase, ß = 3.36, 95% confidence interval (CI): 0.06, 6.66). Weight gains from 0 months to 4 months and 1 year to 4 years were negatively associated with dense area (for 10-unit increase in weight percentile, ß = -0.65, 95% CI: -1.23, -0.07, and ß = -1.07, 95% CI: -1.98, -0.16, respectively). Findings were similar in the sibling subset. These results support the hypothesis that high birth weight is positively associated with increased breast density and suggest that growth spurts starting in early infancy reduce mammographic dense area in adulthood.

Reproductive Factors and Mammographic Density: Associations Among 24,840 Women and Comparison of Studies Using Digitized Film-Screen Mammography and Full-Field Digital Mammography.

Breast density is a modifiable factor that is strongly associated with breast cancer risk. We sought to understand the influence of newer technologies of full-field digital mammography (FFDM) on breast density research and to determine whether results are comparable across studies using FFDM and previous studies using traditional film-screen mammography. We studied 24,840 screening-age (40-74 years) non-Hispanic white women who were participants in the Research Program on Genes, Environment and Health of Kaiser Permanente Northern California and underwent screening mammography with either Hologic (Hologic, Inc., Marlborough, Massachusetts) or General Electric (General Electric Company, Boston, Massachusetts) FFDM machines between 2003 and 2013. We estimated the associations of parity, age at first birth, age at menarche, and menopausal status with percent density and dense area as measured by a single radiological technologist using Cumulus software (Canto Software, Inc., San Francisco, California). We found that associations between reproductive factors and mammographic density measured using processed FFDM images were generally similar in magnitude and direction to those from prior studies using film mammography. Estimated associations for both types of FFDM machines were in the same direction. There was some evidence of heterogeneity in the magnitude of the effect sizes by machine type, which we accounted for using random-effects meta-analysis when combining results. Our findings demonstrate the robustness of quantitative mammographic density measurements across FFDM and film mammography platforms.

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation.

Purpose To develop a deep learning (DL) algorithm to assess mammographic breast density. Materials and Methods In this retrospective study, a deep convolutional neural network was trained to assess Breast Imaging Reporting and Data System (BI-RADS) breast density based on the original interpretation by an experienced radiologist of 41 479 digital screening mammograms obtained in 27 684 women from January 2009 to May 2011. The resulting algorithm was tested on a held-out test set of 8677 mammograms in 5741 women. In addition, five radiologists performed a reader study on 500 mammograms randomly selected from the test set. Finally, the algorithm was implemented in routine clinical practice, where eight radiologists reviewed 10 763 consecutive mammograms assessed with the model. Agreement on BI-RADS category for the DL model and for three sets of readings-(a) radiologists in the test set, (b) radiologists working in consensus in the reader study set, and (c) radiologists in the clinical implementation set-were estimated with linear-weighted κ statistics and were compared across 5000 bootstrap samples to assess significance. Results The DL model showed good agreement with radiologists in the test set (κ = 0.67; 95% confidence interval [CI]: 0.66, 0.68) and with radiologists in consensus in the reader study set (κ = 0.78; 95% CI: 0.73, 0.82). There was very good agreement (κ = 0.85; 95% CI: 0.84, 0.86) with radiologists in the clinical implementation set; for binary categorization of dense or nondense breasts, 10 149 of 10 763 (94%; 95% CI: 94%, 95%) DL assessments were accepted by the interpreting radiologist. Conclusion This DL model can be used to assess mammographic breast density at the level of an experienced mammographer. © RSNA, 2018 Online supplemental material is available for this article . See also the editorial by Chan and Helvie in this issue.

Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma.

Background Previous studies have suggested that breast parenchymal texture features may reflect the biologic risk factors associated with breast cancer development. Therefore, combining the characteristics of normal parenchyma from the contralateral breast with radiomic features of breast tumors may improve the accuracy of digital mammography in the diagnosis of breast cancer. Purpose To determine whether the addition of radiomic analysis of contralateral breast parenchyma to the characterization of breast lesions with digital mammography improves lesion classification over that with radiomic tumor features alone. Materials and Methods This HIPAA-compliant, retrospective study included 182 patients (age range, 25-90 years; mean age, 55.9 years ± 14.9) who underwent mammography between June 2002 and July 2009. There were 106 malignant and 76 benign lesions. Automatic lesion segmentation and radiomic analysis were performed for each breast lesion. Radiomic texture analysis was applied in the normal regions of interest in the contralateral breast parenchyma to assess the mammographic parenchymal patterns. The classification performance of both individual features and the output from a Bayesian artificial neural network classifier was evaluated with the leave-one-patient-out method by using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of differentiating between malignant and benign lesions. Results The performance of the combined lesion and parenchyma classifier in the differentiation between malignant and benign mammographic lesions was better than that with the lesion features alone (AUC = 0.84 ± 0.03 vs 0.79 ± 0.03, respectively; P = .047). Overall, six radiomic features-spiculation, margin sharpness, size, circularity from the tumor feature set, and skewness and power law beta from the parenchymal feature set-were selected more than 50% of the time during the feature selection process on the combined feature set. Conclusion Combining quantitative radiomic data from tumors with contralateral parenchyma characterizations may improve diagnostic accuracy for breast cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Shaffer in this issue.

Digital Breast Tomosynthesis: Radiologist Learning Curve.

Background There is growing evidence that digital breast tomosynthesis (DBT) results in lower recall rates and higher cancer detection rates when compared with digital mammography. However, whether DBT interpretative performance changes with experience (learning curve effect) is unknown. Purpose To evaluate screening DBT performance by cumulative DBT volume within 2 years after adoption relative to digital mammography (DM) performance 1 year before DBT adoption. Materials and Methods This prospective study included 106 126 DBT and 221 248 DM examinations in 271 362 women (mean age, 57.5 years) from 2010 to 2017 that were interpreted by 104 radiologists from 53 facilities in the Breast Cancer Surveillance Consortium. Conditional logistic regression was used to estimate within-radiologist effects of increasing cumulative DBT volume on recall and cancer detection rates relative to DM and was adjusted for examination-level characteristics. Changes were also evaluated by subspecialty and breast density. Results Before DBT adoption, DM recall rate was 10.4% (95% confidence interval [CI]: 9.5%, 11.4%) and cancer detection rate was 4.0 per 1000 screenings (95% CI: 3.6 per 1000 screenings, 4.5 per 1000 screenings); after DBT adoption, DBT recall rate was lower (9.4%; 95% CI: 8.2%, 10.6%; P = .02) and cancer detection rate was similar (4.6 per 1000 screenings; 95% CI: 4.0 per 1000 screenings, 5.2 per 1000 screenings; P = .12). Relative to DM, DBT recall rate decreased for a cumulative DBT volume of fewer than 400 studies (odds ratio [OR] = 0.83; 95% CI: 0.78, 0.89) and remained lower as volume increased (400-799 studies, OR = 0.8 [95% CI: 0.75, 0.85]; 800-1199 studies, OR = 0.81 [95% CI: 0.76, 0.87]; 1200-1599 studies, OR = 0.78 [95% CI: 0.73, 0.84]; 1600-2000 studies, OR = 0.81 [95% CI: 0.75, 0.88]; P < .001). Improvements were sustained for breast imaging subspecialists (OR range, 0.67-0.85; P < .02) and readers who were not breast imaging specialists (OR range, 0.80-0.85; P < .001). Recall rates decreased more in women with nondense breasts (OR range, 0.68-0.76; P < .001) than in those with dense breasts (OR range, 0.86-0.90; P ≤ .05; P interaction < .001). Cancer detection rates for DM and DBT were similar, regardless of DBT volume (P ≥ .10). Conclusion Early performance improvements after digital breast tomosynthesis (DBT) adoption were sustained regardless of DBT volume, radiologist subspecialty, or breast density. © RSNA, 2019 See also the editorial by Hooley in this issue.

Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment.

Purpose To identify phenotypes of mammographic parenchymal complexity by using radiomic features and to evaluate their associations with breast density and other breast cancer risk factors. Materials and Methods Computerized image analysis was used to quantify breast density and extract parenchymal texture features in a cross-sectional sample of women screened with digital mammography from September 1, 2012, to February 28, 2013 (n = 2029; age range, 35-75 years; mean age, 55.9 years). Unsupervised clustering was applied to identify and reproduce phenotypes of parenchymal complexity in separate training (n = 1339) and test sets (n = 690). Differences across phenotypes by age, body mass index, breast density, and estimated breast cancer risk were assessed by using Fisher exact, χ2, and Kruskal-Wallis tests. Conditional logistic regression was used to evaluate preliminary associations between the detected phenotypes and breast cancer in an independent case-control sample (76 women diagnosed with breast cancer and 158 control participants) matched on age. Results Unsupervised clustering in the screening sample identified four phenotypes with increasing parenchymal complexity that were reproducible between training and test sets (P = .001). Breast density was not strongly correlated with phenotype category (R2 = 0.24 for linear trend). The low- to intermediate-complexity phenotype (prevalence, 390 of 2029 [19%]) had the lowest proportion of dense breasts (eight of 390 [2.1%]), whereas similar proportions were observed across other phenotypes (from 140 of 291 [48.1%] in the high-complexity phenotype to 275 of 511 [53.8%] in the low-complexity phenotype). In the independent case-control sample, phenotypes showed a significant association with breast cancer (P = .001), resulting in higher discriminatory capacity when added to a model with breast density and body mass index (area under the curve, 0.84 vs 0.80; P = .03 for comparison). Conclusion Radiomic phenotypes capture mammographic parenchymal complexity beyond conventional breast density measures and established breast cancer risk factors. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Pinker in this issue.

Effect of Mammographic Screening Modality on Breast Density Assessment: Digital Mammography versus Digital Breast Tomosynthesis.

Background Breast Imaging Reporting and Data System (BI-RADS) breast density categories assigned by interpreting radiologists often influence decisions surrounding supplemental breast cancer screening and risk assessment. The landscape of mammographic screening continuously evolves, and different mammographic screening modalities may result in different perception of density, reflected in different assignment of BI-RADS density categories. Purpose To investigate the effect of screening mammography modality on BI-RADS breast density assessments. Materials and Methods Data were retrospectively analyzed from 24 736 individual women (42.3% [10 455 of 24 736] white women, 57.7% [14 281 of 24 736] black women; mean age, 56.3 years; age range, 40.0-74.9 years) who underwent from one to seven mammographic screening examinations from September 2010 through February 2017 (60 766 examinations). Three screening modalities were used: digital mammography alone (8935 examinations); digital mammography with digital breast tomosynthesis (DBT; 30 779 examinations); and synthetic mammography with DBT (21 052 examinations). Random-effects logistic regression analysis was performed to estimate the likelihood of assignment to high versus low BI-RADS density category according to each modality, adjusted for ethnicity, age, body mass index (BMI), and radiologist. The interactions of modality with ethnicity and BMI on density categorization were also tested with the model. Results Women screened with DBT versus digital mammography alone had lower likelihood regarding categorization of high density breasts (digital mammography and DBT vs digital mammography: odds ratio, 0.69 [95% confidence interval: 0.61, 0.80], P < .001; synthetic mammography and DBT vs digital mammography: odds ratio, 0.43 [95% confidence interval: 0.37, 0.50], P < .001). Lower likelihood of high density was also observed at synthetic mammography and DBT compared with digital mammography and DBT (odds ratio, 0.62; 95% confidence interval: 0.56, 0.69; P < .001). There were interactions of modality with ethnicity (P = .007) and BMI (P = .003) on breast density assessment, with greater differences in density categorization according to modality observed for black women than for white women and groups with higher BMI. Conclusion Breast density categorization may vary by screening mammographic modality, and this effect appears to vary by ethnicity and body mass index. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Transverse relaxation-based assessment of mammographic density and breast tissue composition by single-sided portable NMR.

PURPOSE: Elevated mammographic density (MD) is an independent risk factor for breast cancer (BC) as well as a source of masking in X-ray mammography. High-frequency longitudinal monitoring of MD could also be beneficial in hormonal BC prevention, where early MD changes herald the treatment's success. We present a novel approach to quantification of MD in breast tissue using single-sided portable NMR. Its development was motivated by the low cost of portable-NMR instrumentation, the suitability for measurements in vivo, and the absence of ionizing radiation. METHODS: Five breast slices were obtained from three patients undergoing prophylactic mastectomy or breast reduction surgery. Carr-Purcell-Meiboom-Gill (CPMG) relaxation curves were measured from (1) regions of high and low MD (HMD and LMD, respectively) in the full breast slices; (2) the same regions excised from the full slices; and (3) excised samples after H2 O-D2 O replacement. T2 distributions were reconstructed from the CPMG decays using inverse Laplace transform. RESULTS: Two major peaks, identified as fat and water, were consistently observed in the T2 distributions of HMD regions. The LMD T2 distributions were dominated by the fat peak. The relative areas of the two peaks exhibited statistically significant (P < .005) differences between HMD and LMD regions, enabling their classification as HMD or LMD. The relative-area distributions exhibited no statistically significant differences between full slices and excised samples. CONCLUSION: T2 -based portable-NMR analysis is a novel approach to MD quantification. The ability to quantify tissue composition, combined with the low cost of instrumentation, make this approach promising for clinical applications.

Screening mammography: benefit of double reading by breast density.

PURPOSE: The currently recommended double reading of all screening mammography examinations is an economic burden for screening programs. The sensitivity of screening is higher for women with low breast density than for women with high density. One may therefore ask whether single reading could replace double reading at least for women with low density. We addressed this question using data from a screening program where the radiologists coded their readings independently. METHODS: Data include all screening mammography examinations in the Capital Region of Denmark from 1 November 2012 to 31 December 2013. Outcome of screening was assessed by linkage to the Danish Pathology Register. We calculated sensitivity, specificity, number of interval cancers, and false positive-tests per 1000 screened women by both single reader and consensus BI-RADS density code. RESULTS: In total 54,808 women were included. The overall sensitivity of double reading was 72%, specificity was 97.6%, 3 women per 1000 screened experienced an interval cancer, and 24 a false-positive test. Across all BI-RADS density codes, single reading consistently decreased sensitivity as compared with consensus reading. The same was true for specificity, apart from results across BI-RADS density codes set by reader 2. CONCLUSIONS: Single reading decreased sensitivity as compared with double reading across all BI-RADS density codes. This included results based on consensus BI-RADS density codes. This means that replacement of double with single reading would have negative consequences for the screened women, even if density could be assessed automatically calibrated to the usual consensus level.