Time-specific impact of trace metals on breast density of adolescent girls in Santiago, Chile.

Whether trace metals modify breast density, the strongest predictor for breast cancer, during critical developmental stages such as puberty remains understudied. Our study prospectively evaluated the association between trace metals at Tanner breast stage B1 (n = 291) and at stages both B1 and B4 (n = 253) and breast density at 2 years post-menarche among Chilean girls from the Growth and Obesity Cohort Study. Dual-energy x-ray absorptiometry assessed the volume of dense breast tissue (absolute fibroglandular volume [FGV]) and percent breast density (%FGV). Urine trace metals included arsenic, barium, cadmium, cobalt, cesium, copper, magnesium, manganese, molybdenum, nickel, lead, antimony, selenium, tin, thallium, vanadium, and zinc. At B1, a doubling of thallium concentration resulted in 13.69 cm3 increase in absolute FGV (ß: 13.69, 95% confidence interval [CI]: 2.81, 24.52), while a doubling of lead concentration resulted in a 7.76 cm3 decrease in absolute FGV (ß: -7.76, 95%CI: -14.71, -0.73). At B4, a doubling of barium concentration was associated with a 10.06 cm3 increase (ß: 10.06, 95% CI: 1.44, 18.60), copper concentration with a 12.29 cm3 increase (ß: 12.29, 95% CI: 2.78, 21.56), lead concentration with a 9.86 cm3 increase (ß: 9.86, 95% CI: 0.73, 18.98), antimony concentration with a 12.97 cm3 increase (ß: 12.97, 95% CI: 1.98, 23.79) and vanadium concentration with a 13.14 cm3 increase in absolute FGV (ß: 13.14, 95% CI: 2.73, 23.58). Trace metals may affect pubertal breast density at varying developmental stages with implications for increased susceptibility for breast cancer.

Predictors of visceral and subcutaneous adipose tissue and muscle density: The ShapeUp! Kids study.

BACKGROUND AND AIMS: Body fat distribution, i.e., visceral (VAT), subcutaneous adipose tissue (SAT) and intramuscular fat, is important for disease prevention, but sex and ethnic differences are not well understood. Our aim was to identify anthropometric, demographic, and lifestyle predictors for these outcomes. METHODS AND RESULTS: The cross-sectional ShapeUp!Kids study was conducted among five ethnic groups aged 5-18 years. All participants completed questionnaires, anthropometric measurements, and abdominal MRI scans. VAT and SAT areas at four lumbar levels and muscle density were assessed manually. General linear models were applied to estimate coefficients of determination (R2) and to compare the fit of VAT and SAT prediction models. After exclusions, the study population had 133 male and 170 female participants. Girls had higher BMI-z scores, waist circumference (WC), and SAT than boys but lower VAT/SAT and muscle density. SAT, VAT, and VAT/SAT but not muscle density differed significantly by ethnicity. R2 values were higher for SAT than VAT across groups and improved slightly after adding WC. For SAT, R2 increased from 0.85 to 0.88 (girls) and 0.62 to 0.71 (boys) when WC was added while VAT models improved from 0.62 to 0.65 (girls) and 0.57 to 0.62 (boys). VAT values were significantly lower among Blacks than Whites with little difference for the other groups. CONCLUSION: This analysis in a multiethnic population identified BMI-z scores and WC as the major predictors of MRI-derived SAT and VAT and highlights the important ethnic differences that need to be considered in diverse populations.

Performance of Progressive Generations of GPT on an Exam Designed for Certifying Physicians as Certified Clinical Densitometrists.

BACKGROUND: Artificial intelligence (AI) large language models (LLMs) such as ChatGPT have demonstrated the ability to pass standardized exams. These models are not trained for a specific task, but instead trained to predict sequences of text from large corpora of documents sourced from the internet. It has been shown that even models trained on this general task can pass exams in a variety of domain-specific fields, including the United States Medical Licensing Examination. We asked if large language models would perform as well on a much narrower subdomain tests designed for medical specialists. Furthermore, we wanted to better understand how progressive generations of GPT (generative pre-trained transformer) models may be evolving in the completeness and sophistication of their responses even while generational training remains general. In this study, we evaluated the performance of two versions of GPT (GPT 3 and 4) on their ability to pass the certification exam given to physicians to work as osteoporosis specialists and become a certified clinical densitometrists. The CCD exam has a possible score range of 150 to 400. To pass, you need a score of 300. METHODS: A 100-question multiple-choice practice exam was obtained from a 3rd party exam preparation website that mimics the accredited certification tests given by the ISCD (International Society for Clinical Densitometry). The exam was administered to two versions of GPT, the free version (GPT Playground) and ChatGPT+, which are based on GPT-3 and GPT-4, respectively (OpenAI, San Francisco, CA). The systems were prompted with the exam questions verbatim. If the response was purely textual and did not specify which of the multiple-choice answers to select, the authors matched the text to the closest answer. Each exam was graded and an estimated ISCD score was provided from the exam website. In addition, each response was evaluated by a rheumatologist CCD and ranked for accuracy using a 5-level scale. The two GPT versions were compared in terms of response accuracy and length. RESULTS: The average response length was 11.6 ±19 words for GPT-3 and 50.0±43.6 words for GPT-4. GPT-3 answered 62 questions correctly resulting in a failing ISCD score of 289. However, GPT-4 answered 82 questions correctly with a passing score of 342. GPT-3 scored highest on the "Overview of Low Bone Mass and Osteoporosis" category (72 % correct) while GPT-4 scored well above 80 % accuracy on all categories except "Imaging Technology in Bone Health" (65 % correct). Regarding subjective accuracy, GPT-3 answered 23 questions with nonsensical or totally wrong responses while GPT-4 had no responses in that category. CONCLUSION: If this had been an actual certification exam, GPT-4 would now have a CCD suffix to its name even after being trained using general internet knowledge. Clearly, more goes into physician training than can be captured in this exam. However, GPT algorithms may prove to be valuable physician aids in the diagnoses and monitoring of osteoporosis and other diseases.

Positions of The International Society for Clinical Densitometry and Their Etiology: A Scoping Review.

The International Society for Clinical Densitometry convenes a Position Development Conference (PDC) every 2 to 3 years to make recommendations for guidelines and standards in the field of musculoskeletal measurement and assessment. The recommendations pertain to clinically relevant issues regarding the acquisition, quality control, interpretation, and reporting of measures of various aspects of musculoskeletal health. These PDCs have been meeting since 2002 and have generated 214 Adult, 26 FRAX, 41 pediatric, and 9 general nomenclature consideration positions, for a total of 290 positions. All positions are justified by detailed documents that present the background and rationale for each position. However, the linkage to these publications is not maintained by the ISCD or any other publication such that physicians cannot easily understand the etiology of the positions. Further, the wording of many positions has changed over the years after being reviewed by subsequent PDCs. This scoping review captures the references, changes, and timeline associated with each position through the 2019 PDC. It is meant to serve as a guide to clinicians and researchers for intelligent use and application of the positions.

Agreement and Precision of Deuterium Dilution for Total Body Water and Multicompartment Body Composition Assessment in Collegiate Athletes.

BACKGROUND: Deuterium oxide (D2O) dilution is the criterion method for total body water (TBW) measurement, but results may vary depending on the specimen type, analysis method, and analyzing laboratory. Bioelectrical impedance (BIA) estimates TBW, but results may vary by device make and model. OBJECTIVES: We investigated the accuracy and precision of TBW estimates and how measurement conditions affected the accuracy of body composition using multicompartment body composition models. METHODS: Eighty collegiate athletes received duplicate TBW measures acquired from 3 BIA devices (S10, SFB7, and SOZO) and from unique D2O combinations of specimen type (saliva, urine), analysis methodology [Fourier transform infrared spectrophotometry (FTIR), isotope-ratio mass spectrometry (IRMS)], and 3 different laboratories. TBW measures were substituted into 2-compartment (2C) and 5-compartment (5C) body composition models. Criterion measures were compared using Lin's concordance correlation coefficient cutoff of poor (<0.90), moderate (0.90-0.95), substantial (0.95-0.99), and almost perfect (>0.99). RESULTS: Fifty-one participants (26 female) completed the protocol. Using IRMS saliva as the criterion TBW, all other measures produced a substantial or almost perfect agreement, except for SFB7 (poor) and SOZO (moderate). The 2C body composition measures using D2O and BIA produced poor agreement except for moderate agreement for lab 3 FTIR saliva. The 5C body composition measures using D2O produced a substantial agreement, whereas the BIA device S10 and SOZO had a moderate agreement, while the SFB7 had a poor agreement to the criterion. Test-retest precision varied between techniques from 0.3% to 1.2% for TBW. CONCLUSIONS: Small differences in TBW measurement led to significant differences in 2C models. The 5C models partially mitigate differences seen in 2C models when different TBW measures are used. Interchanging TBW measures in multicompartment models can be problematic and should be performed with these considerations.

Body Fat Distribution, Glucose Metabolism, and Diabetes Status Among Older Adults: The Multiethnic Cohort Adiposity Phenotype Study.

BACKGROUND: As the proportion of visceral (VAT) to subcutaneous adipose tissue (SAT) may contribute to type 2 diabetes (T2D) development, we examined this relation in a cross-sectional design within the Multiethnic Cohort that includes Japanese Americans known to have high VAT. The aim was to understand how ectopic fat accumulation differs by glycemic status across ethnic groups with disparate rates of obesity, T2D, and propensity to accumulate VAT. METHODS: In 2013-2016, 1,746 participants aged 69.2 (standard deviation, 2.7) years from five ethnic groups completed questionnaires, blood collections, and whole-body dual X-ray absorptiometry and abdominal magnetic resonance imaging scans. Participants with self-reported T2D and/or medication were classified as T2D, those with fasting glucose >125 and 100-125 mg/dL as undiagnosed cases (UT2D) and prediabetes (PT2D), respectively. Using linear regression, we estimated adjusted means of adiposity measures by T2D status. RESULTS: Overall, 315 (18%) participants were classified as T2D, 158 (9%) as UT2D, 518 (30%) as PT2D, and 755 (43%) as normoglycemic (NG), with significant ethnic differences (P < 0.0001). In fully adjusted models, VAT, VAT/SAT, and percent liver fat increased significantly from NG, PT2D, UT2D, to T2D (P < 0.001). Across ethnic groups, the VAT/SAT ratio was lowest for NG participants and highest for T2D cases. Positive trends were observed in all groups except African Americans, with highest VAT/SAT in Japanese Americans. CONCLUSION: These findings indicate that VAT plays an important role in T2D etiology, in particular among Japanese Americans with high levels of ectopic adipose tissue, which drives the development of T2D to a greater degree than in other ethnic groups.

Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.

Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of DL models to estimate the risk of interval and screening-detected breast cancers with and without clinical risk factors. Materials and Methods This study was performed on 25 096 digital screening mammograms obtained from January 2006 to December 2013. The mammograms were obtained in 6369 women without breast cancer, 1609 of whom developed screening-detected breast cancer and 351 of whom developed interval invasive breast cancer. A DL model was trained on the negative mammograms to classify women into those who did not develop cancer and those who developed screening-detected cancer or interval invasive cancer. Model effectiveness was evaluated as a matched concordance statistic (C statistic) in a held-out 26% (1669 of 6369) test set of the mammograms. Results The C statistics and odds ratios for comparing patients with screening-detected cancer versus matched controls were 0.66 (95% CI: 0.63, 0.69) and 1.25 (95% CI: 1.17, 1.33), respectively, for the DL model, 0.62 (95% CI: 0.59, 0.65) and 2.14 (95% CI: 1.32, 3.45) for the clinical risk factors with the Breast Imaging Reporting and Data System (BI-RADS) density model, and 0.66 (95% CI: 0.63, 0.69) and 1.21 (95% CI: 1.13, 1.30) for the combined DL and clinical risk factors model. For comparing patients with interval cancer versus controls, the C statistics and odds ratios were 0.64 (95% CI: 0.58, 0.71) and 1.26 (95% CI: 1.10, 1.45), respectively, for the DL model, 0.71 (95% CI: 0.65, 0.77) and 7.25 (95% CI: 2.94, 17.9) for the risk factors with BI-RADS density (b rated vs non-b rated) model, and 0.72 (95% CI: 0.66, 0.78) and 1.10 (95% CI: 0.94, 1.29) for the combined DL and clinical risk factors model. The P values between the DL, BI-RADS, and combined model's ability to detect screen and interval cancer were .99, .002, and .03, respectively. Conclusion The deep learning model outperformed in determining screening-detected cancer risk but underperformed for interval cancer risk when compared with clinical risk factors including breast density. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Bae and Kim in this issue.

Diet and Liver Adiposity in Older Adults: The Multiethnic Cohort Adiposity Phenotype Study.

BACKGROUND: Diet plays a key role in the pathogenesis of nonalcoholic fatty liver disease. Limited data exist regarding specific nutrients and food groups and liver fat continuously, particularly among different ethnicities. OBJECTIVES: We aimed to determine the relationship between usual dietary intake and accurately measured liver fat content in a multiethnic population. METHODS: Participants from the Multiethnic Cohort were recruited into the cross-sectional Adiposity Phenotype Study including women and men aged 60-77 y and 5 race/ethnic groups (African American, Japanese American, Latino, Native Hawaiian, and white). They filled out a detailed FFQ and underwent abdominal MRI for liver fat quantification and whole-body DXA for total adiposity. Intake of a priori-selected dietary factors (total and macronutrient energy, specific micronutrients, and food groups) was analyzed in relation to liver fat by estimating the mean percentage liver fat for quartiles of each dietary factor in a general linear model that adjusted for age, sex, race/ethnicity, percentage body fat, and daily energy intake (kcal/d). RESULTS: In total, 1682 participants (mean age: 69.2 y; 51% female) were included. Mean ± SD liver fat percentage was 5.7 ± 4.6. A significant positive association with liver fat was found across quartiles of percentage energy from fat, saturated fat, cholesterol, total red meat, red meat excluding processed red meat, and coffee (Bonferroni-adjusted P-trend < 0.05). A significant inverse association was observed for dietary fiber, vitamin C, and vitamin E (Bonferroni-adjusted P-trend < 0.05). CONCLUSIONS: This study of ethnically diverse older adults shows that certain dietary factors, in particular red meat and saturated fat from red meat, were strongly associated with liver fat, whereas dietary fiber was inversely associated with liver fat, replicating some of the previous studies conducted mostly in whites.

Maternal and Infant Bone Mineral Density 1 Year After Delivery in a Randomized, Controlled Trial of Maternal Tenofovir Disoproxil Fumarate to Prevent Mother-to-child Transmission of Hepatitis B Virus.

In a randomized, double-blind, placebo-controlled trial of tenofovir disoproxil fumarate (TDF) use from 28 weeks gestational age to 2 months postpartum to prevent mother-to-child transmission of hepatitis B virus, there was no significant effect of maternal TDF use on maternal or infant bone mineral density 1 year after delivery/birth. Clinical Trials Registration. NCT01745822.

Pubertal timing and breast density in young women: a prospective cohort study.

BACKGROUND: Earlier age at onset of pubertal events and longer intervals between them (tempo) have been associated with increased breast cancer risk. It is unknown whether the timing and tempo of puberty are associated with adult breast density, which could mediate the increased risk. METHODS: From 1988 to 1997, girls participating in the Dietary Intervention Study in Children (DISC) were clinically assessed annually between ages 8 and 17 years for Tanner stages of breast development (thelarche) and pubic hair (pubarche), and onset of menses (menarche) was self-reported. In 2006-2008, 182 participants then aged 25-29 years had their percent dense breast volume (%DBV) measured by magnetic resonance imaging. Multivariable, linear mixed-effects regression models adjusted for reproductive factors, demographics, and body size were used to evaluate associations of age and tempo of puberty events with %DBV. RESULTS: The mean (standard deviation) and range of %DBV were 27.6 (20.5) and 0.2-86.1. Age at thelarche was negatively associated with %DBV (p trend = 0.04), while pubertal tempo between thelarche and menarche was positively associated with %DBV (p trend = 0.007). %DBV was 40% higher in women whose thelarche-to-menarche tempo was 2.9 years or longer (geometric mean (95%CI) = 21.8% (18.2-26.2%)) compared to women whose thelarche-to-menarche tempo was less than 1.6 years (geometric mean (95%CI) = 15.6% (13.9-17.5%)). CONCLUSIONS: Our results suggest that a slower pubertal tempo, i.e., greater number of months between thelarche and menarche, is associated with higher percent breast density in young women. Future research should examine whether breast density mediates the association between slower tempo and increased breast cancer risk.

Body mass index, mammographic density, and breast cancer risk by estrogen receptor subtype.

BACKGROUND: Obesity and elevated breast density are common risk factors for breast cancer, and their effects may vary by estrogen receptor (ER) subtype. However, their joint effects on ER subtype-specific risk are unknown. Understanding this relationship could enhance risk stratification for screening and prevention. Thus, we assessed the association between breast density and ER subtype according to body mass index (BMI) and menopausal status. METHODS: We conducted a case-control study nested within two mammography screening cohorts, the Mayo Mammography Health Study and the San Francisco Bay Area Breast Cancer SPORE/San Francisco Mammography Registry. Our pooled analysis contained 1538 ER-positive and 285 ER-negative invasive breast cancer cases and 4720 controls matched on age, menopausal status at time of mammogram, and year of mammogram. Percent density was measured on digitized film mammograms using computer-assisted techniques. We used polytomous logistic regression to evaluate the association between percent density and ER subtype by BMI subgroup (normal/underweight, < 25 kg/m2 versus overweight/obese, ≥ 25 kg/m2). We used Wald chi-squared tests to assess for interactions between percent density and BMI. Our analysis was stratified by menopausal status and hormone therapy usage at the time of index mammogram. RESULTS: Percent density was associated with increased risk of overall breast cancer regardless of menopausal status or BMI. However, when analyzing breast cancer across ER subtype, we found a statistically significant (p = 0.008) interaction between percent density and BMI in premenopausal women only. Specifically, elevated percent density was associated with a higher risk of ER-negative than ER-positive cancer in overweight/obese premenopausal women [OR per standard deviation increment 2.17 (95% CI 1.50-3.16) vs 1.33 (95% CI 1.11-1.61) respectively, Pheterogeneity = 0.01]. In postmenopausal women, elevated percent density was associated with similar risk of ER-positive and ER-negative cancers, and no substantive differences were seen after accounting for BMI or hormone therapy usage. CONCLUSIONS: The combination of overweight/obesity and elevated breast density in premenopausal women is associated with a higher risk of ER-negative compared with ER-positive cancer. Eighteen percent of premenopausal women in the USA have elevated BMI and breast density and may benefit from lifestyle modifications involving weight loss and exercise.

Automated volumetric breast density measures: differential change between breasts in women with and without breast cancer.

BACKGROUND: Given that breast cancer and normal dense fibroglandular tissue have similar radiographic attenuation, we examine whether automated volumetric density measures identify a differential change between breasts in women with cancer and compare to healthy controls. METHODS: Eligible cases (n = 1160) had unilateral invasive breast cancer and bilateral full-field digital mammograms (FFDMs) at two time points: within 2 months and 1-5 years before diagnosis. Controls (n = 2360) were matched to cases on age and date of FFDMs. Dense volume (DV) and volumetric percent density (VPD) for each breast were assessed using Volpara™. Differences in DV and VPD between mammograms (median 3 years apart) were calculated per breast separately for cases and controls and their difference evaluated by using the Wilcoxon signed-rank test. To simulate clinical practice where cancer laterality is unknown, we examined whether the absolute difference between breasts can discriminate cases from controls using area under the ROC curve (AUC) analysis, adjusting for age, BMI, and time. RESULTS: Among cases, the VPD and DV between mammograms of the cancerous breast decreased to a lesser degree (- 0.26% and - 2.10 cm3) than the normal breast (- 0.39% and - 2.74 cm3) for a difference of 0.13% (p value < 0.001) and 0.63 cm3 (p = 0.002), respectively. Among controls, the differences between breasts were nearly identical for VPD (- 0.02 [p = 0.92]) and DV (0.05 [p = 0.77]). The AUC for discriminating cases from controls using absolute difference between breasts was 0.54 (95% CI 0.52, 0.56) for VPD and 0.56 (95% CI, 0.54, 0.58) for DV. CONCLUSION: There is a small relative increase in volumetric density measures over time in the breast with cancer which is not found in the normal breast. However, the magnitude of this difference is small, and this measure alone does not appear to be a good discriminator between women with and without breast cancer.

Fecal Microbial Diversity and Structure Are Associated with Diet Quality in the Multiethnic Cohort Adiposity Phenotype Study.

BACKGROUND: Variation in gut microbial community structure is partly attributed to variations in diet. A priori dietary indexes capture diet quality and have been associated with chronic disease risk. OBJECTIVES: The aim of this study was to examine the association of diet quality, as assessed by the Healthy Eating Index, Alternative Healthy Eating Index-2010, alternate Mediterranean Diet, and the Dietary Approaches to Stop Hypertension Trial, with measures of fecal microbial community structure assessed in the Adiposity Phenotype Study (APS), an ethnically diverse study population with varied food intakes. METHODS: Multiethnic Cohort Study members completed a validated quantitative food frequency questionnaire (QFFQ) at cohort entry (1993-1996) and, for the APS subset, at clinic visit (2013-2015), when they also provided a stool sample. DNA was extracted from stool, and the V1-V3 region of the 16S rRNA gene was amplified and sequenced. Dietary index scores were computed based on the QFFQ and an extensive nutritional database. Using linear regression adjusted for relevant covariates, we estimated associations of dietary quality with microbiome measures and computed adjusted mean values of microbial measures by tertiles of dietary index scores. RESULTS: The 858 men and 877 women of white, Japanese American, Latino, Native Hawaiian, and African American ancestry had a mean age of 69.2 years at stool collection. Alpha diversity according to the Shannon index increased by 1-2% across tertiles of all 4 diet indexes measured at clinic visit. The mean relative abundance of the phylum Actinobacteria was 13-19% lower with higher diet quality across all 4 indexes (difference between tertile 3 and tertile 1 divided by tertile 1). Of the 104 bacterial genera tested, 21 (primarily from the phylum Firmicutes) were positively associated with at least 1 index after Bonferroni adjustment. CONCLUSION: Diet quality was strongly associated with fecal microbial alpha diversity and beta diversity and several genera previously associated with human health.

Executive Summary of the 2019 ISCD Position Development Conference on Monitoring Treatment, DXA Cross-calibration and Least Significant Change, Spinal Cord Injury, Peri-prosthetic and Orthopedic Bone Health, Transgender Medicine, and Pediatrics.

To answer important questions in the fields of monitoring with densitometry, dual-energy X-ray absorptiometry machine cross-calibration, monitoring, spinal cord injury, periprosthetic and orthopedic bone health, transgender medicine, and pediatric bone health, the International Society for Clinical Densitometry (ISCD) held a Position Development Conference from March 20 to 23, 2019. Potential topics requiring guidance were solicited from ISCD members in 2017. Following that, a steering committee selected, prioritized, and grouped topics into Task Forces. Chairs for each Task Force were appointed and the members were co-opted from suggestions by the Steering Committee and Task Force Chairs. The Task Forces developed key questions, performed literature searches, and came up with proposed initial positions with substantiating draft publications, with support from the Steering Committee. An invited Panel of Experts first performed a review of draft positions using a modified RAND Appropriateness Method with voting for appropriateness. Draft positions deemed appropriate were further edited and presented at the Position Development Conference meeting in an open forum. A second round of voting occurred after discussions to approve or reject the positions. Finally, a face-to-face closed session with experts and Task Force Chairs, and subsequent electronic follow-up resulted in 34 Official Positions of the ISCD approved by the ISCD Board on May 28, 2019. The Official Positions and the supporting evidence were submitted for publication on July 1, 2019. This paper provides a summary of the all the ISCD Adult and Pediatric Official Positions, with the new 2019 positions highlighted in bold.

Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

Background: In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead. Objective: To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures. Design: Case-control. Setting: San Francisco Mammography Registry and Mayo Clinic. Participants: 1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants. Measurements: Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity. Results: Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively. Limitation: Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method. Conclusion: Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density. Primary Funding Source: National Cancer Institute.

Does mammographic density mediate risk factor associations with breast cancer? An analysis by tumor characteristics.

BACKGROUND: Though mammographic density (MD) has been proposed as an intermediate marker of breast cancer risk, few studies have examined whether the associations between breast cancer risk factors and risk are mediated by MD, particularly by tumor characteristics. METHODS: Our study population included 3392 cases (1105 premenopausal) and 8882 (3192 premenopausal) controls from four case-control studies. For established risk factors, we estimated the percent of the total risk factor association with breast cancer that was mediated by percent MD (secondarily, by dense area and non-dense area) for invasive breast cancer as well as for subtypes defined by the estrogen receptor (ER+/ER-), progesterone receptor (PR+/PR-), and HER2 (HER2+/HER2-). Analyses were conducted separately in pre- and postmenopausal women. RESULTS: Positive associations between prior breast biopsy and risk of invasive breast cancer as well as all subtypes were partially mediated by percent MD in pre- and postmenopausal women (percent mediated = 11-27%, p ≤ 0.02). In postmenopausal women, nulliparity and hormone therapy use were positively associated with invasive, ER+ , PR+ , and HER2- breast cancer; percent MD partially mediated these associations (percent mediated ≥ 31%, p ≤ 0.02). Further, among postmenopausal women, percent MD partially mediated the positive association between later age at first birth and invasive as well as ER+ breast cancer (percent mediated = 16%, p ≤ 0.05). CONCLUSION: Percent MD partially mediated the associations between breast biopsy, nulliparity, age at first birth, and hormone therapy with risk of breast cancer, particularly among postmenopausal women, suggesting that these risk factors at least partially influence breast cancer risk through changes in breast tissue composition.

Combining quantitative and qualitative breast density measures to assess breast cancer risk.

BACKGROUND: Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk. METHODS: We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume. RESULTS: Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%. CONCLUSIONS: Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

A trans-ethnic genome-wide association study identifies gender-specific loci influencing pediatric aBMD and BMC at the distal radius.

Childhood fractures are common, with the forearm being the most common site. Genome-wide association studies (GWAS) have identified more than 60 loci associated with bone mineral density (BMD) in adults but less is known about genetic influences specific to bone in childhood. To identify novel genetic factors that influence pediatric bone strength at a common site for childhood fractures, we performed a sex-stratified trans-ethnic genome-wide association study of areal BMD (aBMD) and bone mineral content (BMC) Z-scores measured by dual energy X-ray absorptiometry at the one-third distal radius, in a cohort of 1399 children without clinical abnormalities in bone health. We tested signals with P < 5 × 10(-6) for replication in an independent, same-age cohort of 486 Caucasian children. Two loci yielded a genome-wide significant combined P-value: rs7797976 within CPED1 in females [P = 2.4 × 10(-11), ß =- 0.30 standard deviations (SD) per T allele; aBMD-Z] and rs7035284 at 9p21.3 in males (P = 1.2 × 10(-8), ß = 0.28 SD per G allele; BMC-Z). Signals at the CPED1-WNT16-FAM3C locus have been previously associated with BMD at other skeletal sites in adults and children. Our result at the distal radius underscores the importance of this locus at multiple skeletal sites. The 9p21.3 locus is within a gene desert, with the nearest gene flanking each side being MIR31HG and MTAP, neither of which has been implicated in BMD or BMC previously. These findings suggest that genetic determinants of childhood bone accretion at the radius, a skeletal site that is primarily cortical bone, exist and also differ by sex.

Advanced Analysis Techniques Improve Infant Bone and Body Composition Measures by Dual-Energy X-Ray Absorptiometry.

OBJECTIVE: To evaluate a novel technique designed to reduce the negative impact of motion artifacts in infant dual-energy X-ray absorptiometry (DXA) scans. STUDY DESIGN: Using cross-sectional data from a large multicenter study, we developed and tested advanced methods for infant scan analysis. Newborns (n = 750) received spine and whole-body DXA scans with up to 3 attempts to acquire a motion free scan. Precision of infant DXA was estimated from visits with multiple valid scans. Accuracy of regional reflection, fusion, and omission techniques was estimated by comparing modified scans to unmodified valid scans. The effectiveness of the acquisition and analysis protocol was represented by the reduction in rate of failure to acquire valid results from infant visits. RESULTS: For infant whole-body DXA, arm reflection and all fusion techniques caused no significant changes to bone mineral content, bone mineral density, bone area, total mass, fat mass, lean mass, and percentage fat. Leg reflection and arm/leg dual-reflection caused significant changes to total mass, but the percentage change remained small. For infant spine DXA, fusion and omission caused no significant changes. Advanced analysis techniques reduced the failure rate of whole-body scanning from 20.8% to 9.3% and the failure rate of spine scanning from 8.9% to 2.4%. CONCLUSIONS: Advanced analysis techniques significantly reduced the impact of motion artifacts on infant DXA scans. We suggest this protocol be used in future infant DXA research and clinical practice.

Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status.

BACKGROUND: Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density. METHODS: This study combines five case-control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study. RESULTS: Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH_10 [corrected] and FD_TH_15) [corrected] were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH_60 to FD_TH_85) [corrected] were associated with a decreased risk. Increasing the FD_TH_75 [corrected] and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with an increased [corrected] risk of breast cancer. For example, 1 standard deviation increase of FD_TH_75 [corrected] was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79-0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar. CONCLUSION: Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.