Longitudinal Study of Mammographic Density Measures That Predict Breast Cancer Risk

Associations of stem cell markers CD44, CD24 and ALDH1A1 with mammographic breast density in women with benign breast biopsies

BACKGROUND

We examined associations of CD44, CD24 and ALDH1A1 breast stem cell markers with mammographic breast density (MBD), a well-established breast cancer (BCa) risk factor.

METHODS

We included 218 cancer-free women with biopsy-confirmed benign breast disease within the Nurses' Health Study (NHS) and NHSII. The data on BCa risk factors were obtained from biennial questionnaires. Immunohistochemistry (IHC) was done on tissue microarrays. For each core, the IHC expression was assessed using a semi-automated platform and expressed as percent of positively stained cells for each marker out of the total cell count. MBD was assessed with computer-assisted techniques. Generalised linear regression was used to examine the associations of each marker with square root-transformed percent density (PD), absolute dense and non-dense areas (NDA), adjusted for BCa risk factors.

RESULTS

Stromal CD44 and ALDH1A1 expression was positively associated with PD (≥ 10% vs. <10% β = 0.56, 95% confidence interval [CI] [0.06; 1.07] and β = 0.81 [0.27; 1.34], respectively) and inversely associated with NDA (β per 10% increase = -0.17 [-0.34; -0.01] and β for ≥10% vs. <10% = -1.17 [-2.07; -0.28], respectively). Epithelial CD24 expression was inversely associated with PD (β per 10% increase = -0.14 [-0.28; -0.01]. Stromal and epithelial CD24 expression was positively associated with NDA (β per 10% increase = 0.35 [0.2 × 10-2; 0.70] and β per 10% increase = 0.34 [0.11; 0.57], respectively).

CONCLUSION

Expression of stem cell markers is associated with MBD.

Modeling correlated pairs of mammogram images

Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.

Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature

PURPOSE

It may be important for women to have mammograms at different points in time to track changes in breast density, as fluctuations in breast density can affect breast cancer risk. This systematic review aimed to assess methods used to relate repeated mammographic images to breast cancer risk.

METHODS

The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021. Eligibility criteria included published articles in English describing the relationship of change in mammographic features with risk of breast cancer. Risk of bias was assessed using the Quality in Prognostic Studies tool.

RESULTS

Twenty articles were included. The Breast Imaging Reporting and Data System and Cumulus were most commonly used for classifying mammographic density and automated assessment was used on more recent digital mammograms. Time between mammograms varied from 1 year to a median of 4.1, and only nine of the studies used more than two mammograms. Several studies showed that adding change of density or mammographic features improved model performance. Variation in risk of bias of studies was highest in prognostic factor measurement and study confounding.

CONCLUSION

This review provided an updated overview and revealed research gaps in assessment of the use of texture features, risk prediction, and AUC. We provide recommendations for future studies using repeated measure methods for mammogram images to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

Causal relationships between breast cancer risk factors based on mammographic features

BACKGROUND

Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology.

METHODS

We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method.

RESULTS

The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively).

CONCLUSIONS

In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.

Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China

BACKGROUND

Mammographic density (MD) is a strong risk factor for breast cancer. We aimed to evaluate the association between MD and breast cancer related risk factors among average-risk women in rural China.

METHODS

This is a population-based screening study. 12518 women aged 45-64 years with complete MD data from three maternal and childcare hospitals in China were included in the final analysis. ORs and 95%CIs were estimated using generalized logit model by comparing each higher MD (BI-RADS b, c, d) to the lowest group (BI-RADS a). The cumulative logistic regression model was used to estimate the ORtrend (95%CI) and Ptrend by treating MD as an ordinal variable.

RESULTS

Older age (ORtrend = 0.81, 95%CI: 0.79-0.81, per 2-year increase), higher BMI (ORtrend = 0.73, 95%CI: 0.71-0.75, per 2 kg/m2), more births (ORtrend = 0.47, 95%CI: 0.41-0.54, 3 + vs. 0-1), postmenopausal status (ORtrend = 0.42, 95%CI: 0.38-0.46) were associated with lower MD. For parous women, longer duration of breastfeeding was found to be associated with higher MD when adjusting for study site, age, BMI, and age of first full-term birth (ORtrend = 1.53, 95%CI: 1.27-1.85, 25 + months vs. no breastfeeding; ORtrend = 1.45, 95%CI: 1.20-1.75, 19-24 months vs. no breastfeeding), however, the association became non-significant when adjusting all covariates. Associations between examined risk factors and MD were similar in premenopausal and postmenopausal women except for level of education and oral hormone drug usage. Higher education was only found to be associated with an increased proportion of dense breasts in postmenopausal women (ORtrend = 1.08, 95%CI: 1.02-1.15). Premenopausal women who ever used oral hormone drug were less likely to have dense breasts, though the difference was marginally significant (OR = 0.54, P = 0.045). In postmenopausal women, we also found the proportion of dense breasts increased with age at menopause (ORtrend = 1.31, 95%CI: 1.21-1.43).

CONCLUSIONS

In Chinese women with average risk for breast cancer, we found MD was associated with age, BMI, menopausal status, lactation, and age at menopausal. This finding may help to understand the etiology of breast cancer and have implications for breast cancer prevention in China.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening

PURPOSE

Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort.

MATERIALS AND METHODS

We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman.

RESULTS

PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical.

CONCLUSIONS

Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.

Soy isoflavones decrease fibroglandular breast tissue measured by magnetic resonance imaging in premenopausal women: A 2-year randomized double-blind placebo controlled clinical trial

BACKGROUND & AIMS

Populations consuming soy have reduced risk for breast cancer, but the mechanisms are unclear. We tested the hypothesis that soy isoflavones, which have ovarian hormone-like effects, can reduce fibroglandular breast tissue (FGBT, 'breast density'), a strong risk marker for breast cancer.

METHODS

Premenopausal women (age 30-42 years) were randomized to consume isoflavones (136.6 mg as aglycone equivalents, n = 99) or placebo (n = 98) for 5 days per week up to 2 years, and changes in breast composition measured by magnetic resonance imaging at baseline and yearly intervals were compared after square root transformation using linear mixed effects regression models.

RESULTS

By intention-to-treat analyses (n = 194), regression coefficients (β estimates) of the interaction of time and isoflavone treatment were -0.238 (P = 0.06) and -0.258 (P < 0.05) before and after BMI adjustment, respectively for FGBT, 0.620 (P < 0.05) and 0.248 (P = 0.160), respectively for fatty breast tissue (FBT), and -0.155 (P < 0.05) and -0.107 (P < 0.05), respectively for FGBT as percent of total breast (FGBT%). β Estimates for interaction of treatment with serum calcium were -2.705 for FBT, and 0.588 for FGBT% (P < 0.05, before but not after BMI adjustment). BMI (not transformed) was related to the interaction of treatment with time (β = 0.298) or with calcium (β = -1.248) (P < 0.05). Urinary excretion of isoflavones in adherent subjects (n = 135) significantly predicted these changes in breast composition. Based on the modeling results, after an average of 1.2, 2.2 and 3.3 years of supplementation, a mean decrease of FGBT by 5.3, 12.1, and 19.3 cc, respectively, and a mean decrease of FGBT% by 1.37, 2.43, and 3.50%, respectively, were estimated for isoflavone exposure compared to placebo treatment. Subjects with maximum isoflavone excretion were estimated to have 38 cc less FGBT (or ∼3.13% less FGBT%) than subjects without isoflavone excretion. Decrease in FGBT and FGBT% was more precise with daidzein than genistein.

CONCLUSIONS

Soy isoflavones can induce a time- and concentration-dependent decrease in FGBT, a biomarker for breast cancer risk, in premenopausal women, and moderate effects of calcium on BMI and breast fat, suggesting a beneficial effect of soy consumption.

TRIAL REGISTRATION

www.

CLINICALTRIALS

gov identifier: NCT00204490.

TRIAL REGISTRATION

www.

CLINICALTRIALS

gov identifier: NCT00204490.

Deep learning of longitudinal mammogram examinations for breast cancer risk prediction

Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings.

Associations of Oral Contraceptives with Mammographic Breast Density in Premenopausal Women

BACKGROUND

We investigated the associations of oral contraceptives (OC) with percent breast density (PD), absolute dense area (DA), nondense area (NDA), and a novel image intensity variation (V) measure in premenopausal women.

METHODS

This study included 1,233 controls from a nested case-control study within Nurses' Health Study II cohort. Information on OCs was collected in 1989 and updated biennially. OC use was defined from the questionnaire closest to the mammogram date. PD, DA, and NDA were measured from digitized film mammograms using a computer-assisted thresholding technique; the V measure was obtained with a previously developed algorithm measuring the SD of pixel values in the eroded breast region. Generalized linear regression was used to assess associations between OCs and density measures (square root-transformed PD, DA, and NDA, and -untransformed V).

RESULTS

OC use was not associated with PD [current vs. never: β = -0.06; 95% confidence interval (CI), -0.37-0.24; past vs. never: β = 0.10; 95% CI, -0.09-0.29], DA (current vs. never: β = -0.20; 95% CI -0.59-0.18; past vs. never: β = 0.13; 95% CI, -0.12-0.39), and NDA (current vs. never: β = -0.19; 95% CI, -0.56-0.18; past vs. never: β = -0.01; 95% CI, -0.28-0.25). Women with younger age at initiation had significantly greater V-measure (<20 years vs. never: β = 26.88; 95% CI, 3.18-50.58; 20-24 years vs. never: β = 20.23; 95% CI, -4.24-44.71; 25-29 years vs. never: β = 2.61; 95% CI -29.00-34.23; ≥30 years vs. never: β = 0.28; 95% CI, -34.16-34.72, P _trend = 0.03).

CONCLUSIONS

Our findings suggest that an earlier age at first OC use was associated with significantly greater V.

IMPACT

These findings could guide decisions about the age for OC initiation.

Less Is More-Ways to Move Forward for Improved Breast Cancer Risk Stratification

Breast cancer risk models increasingly are including mammographic density (MD) and polygenic risk scores (PRS) to improve identification of higher-risk women who may benefit from genetic screening, earlier and supplemental breast screening, chemoprevention, and other targeted interventions. Here, we present additional considerations for improved clinical use of risk prediction models with MD, PRS, and questionnaire-based risk factors. These considerations include whether changing risk factor patterns, including MD, can improve risk prediction and management, and whether PRS could help inform breast cancer screening without MD measures and prior to the age at initiation of population-based mammography. We further argue that it may be time to reconsider issues around breast cancer risk models that may warrant a more comprehensive head-to-head comparison with other methods for risk factor assessment and risk prediction, including emerging artificial intelligence methods. With the increasing recognition of limitations of any single mathematical model, no matter how simplified, we are at an important juncture for consideration of these different approaches for improved risk stratification in geographically and ethnically diverse populations.See related article by Rosner et al., p. 600.

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.

Association between menopausal hormone therapy, mammographic density and breast cancer risk: results from the E3N cohort study

BACKGROUND

Menopausal hormone therapy (MHT) is a risk factor for breast cancer (BC). Evidence suggests that its effect on BC risk could be partly mediated by mammographic density. The aim of this study was to investigate the relationship between MHT, mammographic density and BC risk using data from a prospective study.

METHODS

We used data from a case-control study nested within the French cohort E3N including 453 cases and 453 matched controls. Measures of mammographic density, history of MHT use during follow-up and information on potential confounders were available for all women. The association between MHT and mammographic density was evaluated by linear regression models. We applied mediation modelling techniques to estimate, under the hypothesis of a causal model, the proportion of the effect of MHT on BC risk mediated by percent mammographic density (PMD) for BC overall and by hormone receptor status.

RESULTS

Among MHT users, 4.2% used exclusively oestrogen alone compared with 68.3% who used exclusively oestrogens plus progestogens. Mammographic density was higher in current users (for a 60-year-old woman, mean PMD 33%; 95% CI 31 to 35%) than in past (29%; 27 to 31%) and never users (24%; 22 to 26%). No statistically significant association was observed between duration of MHT and mammographic density. In past MHT users, mammographic density was negatively associated with time since last use; values similar to those of never users were observed in women who had stopped MHT at least 8 years earlier. The odds ratio of BC for current versus never MHT users, adjusted for age, year of birth, menopausal status at baseline and BMI, was 1.67 (95% CI, 1.04 to 2.68). The proportion of effect mediated by PMD was 34% for any BC and became 48% when the correlation between BMI and PMD was accounted for. These effects were limited to hormone receptor-positive BC.

CONCLUSIONS

Our results suggest that, under a causal model, nearly half of the effect of MHT on hormone receptor-positive BC risk is mediated by mammographic density, which appears to be modified by MHT for up to 8 years after MHT termination.

Gut microbiome, body weight, and mammographic breast density in healthy postmenopausal women

PURPOSE

We examined gut microbiome (GM) profiles in relation to mammographic breast density (BD) and body mass index (BMI) in healthy postmenopausal women.

METHODS

Eligible women were postmenopausal, had a BMI ≤ 35 kg/m², and had not recently taken oral/IV antibiotics. All women provided a fecal sample and information on breast cancer risk factors. Mammographic BD was classified with the American College of Radiology's BI-RADS BD classification system. Bacterial DNA was isolated from fecal samples and the V1-V2 hypervariable regions of 16S rRNA were sequenced on the Illumina MiSeq platform. We examined associations of GM with indices of within-sample (alpha) diversity and the ratio of the two main phyla (Firmicutes and Bacteroidetes; F/B ratio) with BD and BMI.

RESULTS

Among 69 women with BD data, 39 had low BD (BI-RADS I/II) and 30 had high BD (BI-RADS III/IV). BMI was inversely associated with BD (mean BMI = 23.8 and 28.0 in women with high and low BD, respectively, p = 1.07 × 10^-5). Similar levels of GM diversity were found across weight groups according to Shannon (p = 0.83); Inverse Simpson (p = 0.97); and Chao1 (p = 0.31) indices. F/B ratio and microbiota diversity were suggestively greater in women with high vs. low BD (p = 0.35, 0.14, 0.15, and 0.17 for F/B ratio, Shannon, Inverse Simpson and Chao1, respectively).

CONCLUSION

Suggestive differences observed in women with high and low BD with respect to GM alpha diversity and prevalence of specific GM taxa need to be confirmed in larger studies.

Breast cancer risk factors by mode of detection among screened women in the Cancer Prevention Study-II

BACKGROUND

Identifying risk factors for women at high risk of symptom-detected breast cancers that were missed by screening would enable targeting of enhanced screening regimens. To this end, we examined associations of breast cancer risk factors by mode of detection in screened women from the Cancer Prevention Study (CPS)-II Nutrition Cohort.

METHODS

Among 77,206 women followed for a median of 14.8 years, 2711 screen-detected and 1281 symptom-detected breast cancer cases were diagnosed. Multivariable-adjusted associations were estimated using joint Cox proportional hazards regression models with person-time calculated contingent on screening.

RESULTS

Factors associated with higher risks of symptom-detected and screen-detected breast cancer included current combined hormone therapy (HT) use (HR 2.07, 95% CI 1.72-2.48 and 1.45, 1.27-1.65, respectively) and history of benign breast disease (1.85, 1.64-2.08 and 1.43, 1.31-1.55, respectively). Current estrogen-only HT use was associated with symptom-detected (1.40, 1.15-1.71) but not screen-detected (0.95, 0.83-1.09) breast cancer. Higher risk of screen-detected but not symptom-detected breast cancer was observed for obese vs. normal body mass index (1.22, 1.01-1.48 and 0.76, 0.56-1.01, respectively), per 3 h/day sitting time (1.10, 1.04-1.16 and 0.97, 0.89-1.06, respectively), and ≥ 2 drinks per day vs. nondrinker (1.40, 1.16-1.69 and 1.27, 0.97-1.66, respectively).

CONCLUSIONS

Differences in risk factors for symptom-detected vs. screen-detected breast cancer were observed and most notably, use of combined and estrogen-only HT and a history of benign breast disease were associated with increased risk of symptomatic detected breast cancer.

IMPACT

If confirmed, these data suggest that such women may benefit from more intensive screening to facilitate early detection.

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.

Simplified Breast Risk Tool Integrating Questionnaire Risk Factors, Mammographic Density, and Polygenic Risk Score: Development and Validation

BACKGROUND

Clinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS).

METHODS

We derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up.

RESULTS

In the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: OR_{per SD} = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, OR_{per SD} = 1.37 (95% CI: 1.21-1.55) and QS, OR_{per SD} = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls.

CONCLUSION

A simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk.

IMPACT

This simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.See related commentary by Tehranifar et al., p. 587.

Meat intake and cancer risk: prospective analyses in UK Biobank

BACKGROUND

Red and processed meat have been consistently associated with colorectal cancer risk, but evidence for other cancer sites and for poultry intake is limited. We therefore examined associations between total, red and processed meat and poultry intake and incidence for 20 common cancers.

METHODS

We analyzed data from 474 996 participants (54% women) in UK Biobank. Participants were aged 37-73 years and cancer-free at baseline (2006-10). Multivariable-adjusted Cox proportional hazards models were used to determine associations between baseline meat intake and cancer incidence. Trends in risk across the baseline categories were calculated, assigning re-measured intakes from a subsample.

RESULTS

During a mean follow-up of 6.9 years, 28 955 participants were diagnosed with malignant cancer. After correction for multiple testing, red and processed meat combined, and processed meat, were each positively associated with colorectal cancer risk [hazard ratio (HR) per 70 g/day higher intake of red and processed meat 1.32, 95% confidence interval 1.14-1.53; HR per 20 g/day higher intake of processed meat 1.18, 1.03-1.31] and red meat was associated with colon cancer risk (HR per 50 g/day higher intake of red meat 1.36, 1.13-1.64). Positive associations of red meat intake with colorectal and prostate cancer, processed meat intake with rectal cancer and poultry intake with cancers of the lymphatic and haematopoietic tissues did not survive multiple testing.

CONCLUSIONS

Higher intake of red and processed meat was specifically associated with a higher risk of colorectal cancer; there was little evidence that meat intake was associated with risk of other cancers.

"Are changes in breast density during the menstrual cycle relevant? To what?"

PURPOSE

Cancers can be hidden by high breast density (BDen)- the masking effect (ME). BDen is also a modifiable and highly prevalent breast cancer risk (BCR) factor. The purposes of this study were to determine how much glandular volume (GVol), breast volume (BVol) and their ratio: BDen change during the menstrual cycle, and if these changes could affect ME or be relevant to results of interventional studies aiming to diminish BCR using these parameters as surrogates.

METHODS

We retrieved GVol, BVol and BDen data values obtained from 39,997 right mammograms performed with photon counting technique of 19,904 premenopausal women who reported their first day of last menses (FDLM). Many women had more than one study included over the years (with a different FDLM) but were not studied longitudinally. We segregated women by age (yearly), divided the menstrual cycle in 4 weeks, and assigned results with respect to the FDLM.

RESULTS

All parameters vary cyclically, with higher values in week 4 (GVol and BDen) or week 1 (BVol). Mean inter-week differences were very small for the three parameters, and diminished with age. However, especially in the youngest women, inter-week differences could be more than 10% for BDen, 15% for GVol, and 50% for BVol.

CONCLUSION

Small inter-week mean differences almost certainly rule out relevant changes to ME directly attributable to BDen. However, the possibility of large differences during the menstrual cycle in younger women, who are the ideal targets of interventional studies to diminish BCR, might distort results and should be accounted for.

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

A review of the influence of mammographic density on breast cancer clinical and pathological phenotype

PURPOSE

It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context.

METHODS

With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival.

RESULTS AND CONCLUSIONS

There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.

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: β = - 1.27, 95% CI - 4.62; 2.09, p-trend = 0.55 for percent density; β = - 0.21, 95% CI - 0.76, 0.34, p-trend = 0.65 for absolute dense area, and β = 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.

Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds

BACKGROUND

Case-control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers.

METHOD

We conducted a nested case-control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC).

RESULTS

For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85-2.92); all ΔBIC > 14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P > 0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC > 6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P > 0.07).

CONCLUSION

The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.

Mammographic Density Distribution of Healthy Taiwanese Women and its Naturally Decreasing Trend with Age

We analysed typical mammographic density (MD) distributions of healthy Taiwanese women to augment existing knowledge, clarify cancer risks, and focus public health efforts. From January 2011 to December 2015, 88,193 digital mammograms were obtained from 69,330 healthy Taiwanese women (average, 1.27 mammograms each). MD measurements included dense volume (DV) and volumetric density percentage (VPD) and were quantified by fully automated volumetric density estimation and Box-Cox normalization. Prediction of the declining MD trend was estimated using curve fitting and a rational model. Normalized DV and VPD Lowess curves demonstrated similar but non-identical distributions. In high-density grade participants, the VPD increased from 12.45% in the 35-39-year group to 13.29% in the 65-69-year group but only from 5.21% to 8.47% in low-density participants. Regarding the decreased cumulative VPD percentage, the mean MD declined from 12.79% to 19.31% in the 45-50-year group versus the 50-55-year group. The large MD decrease in the fifth decade in this present study was similar to previous observations of Western women. Obtaining an MD distribution model with age improves the understanding of breast density trends and age variations and provides a reference for future studies on associations between MD and cancer risk.

Density and tailored breast cancer screening: practice and prediction - an overview

Mammography, as the primary screening modality, has facilitated a substantial decrease in breast cancer-related mortality in the general population. However, the sensitivity of mammography for breast cancer detection is decreased in women with higher breast densities, which is an independent risk factor for breast cancer. With increasing public awareness of the implications of a high breast density, there is an increasing demand for supplemental screening in these patients. Yet, improvements in breast cancer detection with supplemental screening methods come at the expense of increased false-positives, recall rates, patient anxiety, and costs. Therefore, breast cancer screening practice must change from a general one-size-fits-all approach to a more personalized, risk-based one that is tailored to the individual woman’s risk, personal beliefs, and preferences, while accounting for cost, potential harm, and benefits.

This overview will provide an overview of the available breast density assessment modalities, the current breast density screening recommendations for women at average risk of breast cancer, and supplemental methods for breast cancer screening. In addition, we will provide a look at the possibilities for a risk-adapted breast cancer screening.

Intake of dietary carbohydrates in early adulthood and adolescence and breast density among young women

PURPOSE

Carbohydrate intake increases postprandial insulin secretion and may affect breast density, a strong risk factor for breast cancer, early in life. We examined associations of adolescent and early adulthood intakes of total carbohydrates, glycemic index/load, fiber, and simple sugars with breast density among 182 young women.

METHODS

Diet was assessed using three 24-h recalls at each of five Dietary Intervention Study in Children (DISC) clinic visits when participants were age 10-19 years and at the DISC06 Follow-Up Study clinic visit when participants were age 25-29 years. Associations between energy-adjusted carbohydrates and MRI-measured percent dense breast volume (%DBV) and absolute dense breast volume (ADBV) at 25-29 years were quantified using multivariable-adjusted mixed-effects linear models.

RESULTS

Adolescent sucrose intakes and premenarcheal total carbohydrates intakes were modestly associated with higher %DBV (mean %DBVQ1 vs Q4, 16.6 vs 23.5% for sucrose; and 17.2 vs 22.3% for premenarcheal total carbohydrates, all Ptrend ≤ 0.02), but not with ADBV. However, adolescent intakes of fiber and fructose were not associated with %DBV and ADBV. Early adulthood intakes of total carbohydrates, glycemic index/load, fiber, and simple sugars were not associated with %DBV and ADBV.

CONCLUSIONS

Insulinemic carbohydrate diet during puberty may be associated with adulthood breast density, but our findings need replication in larger studies. Clinical Trials Registration ClinicalTrials.gov Identifier, NCT00458588 April 9, 2007; NCT00000459 October 27, 1999.

Growth Trajectories, Breast Size, and Breast-Tissue Composition in a British Prebirth Cohort of Young Women

Mammographic percent density, the proportion of fibroglandular tissue in the breast, is a strong risk factor for breast cancer, but its determinants in young women are unknown. We examined associations of magnetic resonance imaging (MRI) breast-tissue composition at age 21 years with prospectively collected measurements of body size and composition from birth to early adulthood and markers of puberty (all standardized) in a sample of 500 nulliparous women from a prebirth cohort of children born in Avon, United Kingdom, in 1991-1992 and followed up to 2011-2014. Linear models were fitted to estimate relative change in MRI percent water, which is equivalent to mammographic percent density, associated with a 1-standard-deviation increase in the exposure of interest. In mutually adjusted analyses, MRI percent water was positively associated with birth weight (relative change (RC) = 1.03, 95% confidence interval (CI): 1.00, 1.06) and pubertal height growth (RC = 1.07, 95% CI: 1.02, 1.13) but inversely associated with pubertal weight growth (RC = 0.86, 95% CI: 0.84, 0.89) and changes in dual-energy x-ray absorptiometry percent body fat mass (e.g., for change between ages 11 years and 13.5 years, RC = 0.96, 95% CI: 0.93, 0.99). Ages at thelarche and menarche were positively associated with MRI percent water, but these associations did not persist upon adjustment for height and weight growth. These findings support the hypothesis that growth trajectories influence breast-tissue composition in young women, whereas puberty plays no independent role.

A comprehensive tool for measuring mammographic density changes over time

Background

Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time.

Method

Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment.

Results

The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3–1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60–0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001.

Conclusions

The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up.

Electronic supplementary material

The online version of this article (10.1007/s10549-018-4690-5) contains supplementary material, which is available to authorized users.

Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide

BACKGROUND

Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known.

METHODS AND FINDINGS

We examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35-85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (-0.46 cm [95% CI: -0.53, -0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I2) was 16.5%. Among premenopausal women, the √PD difference per 10-year increase in age was -0.24 cm (95% CI: -0.34, -0.14; I2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in √PD (-0.38 cm [95% CI: -0.44, -0.33]; I2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature.

CONCLUSIONS

Declines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction.