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Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, there is a lack of comprehensive evidence to determine the relative strengths as risk predictor of textures and density and the reliability of texture-based measures. We searched the PubMed database for research published up to November 2023, which assessed breast cancer risk associations [odds ratios (OR)] with texture-based measures and percent mammographic density (PMD), and their discrimination [area under the receiver operating characteristics curve (AUC)], using same datasets. Of 11 publications, for textures, six found stronger associations (P < 0.05) with 11% to 508% increases on the log scale by study, and four found weaker associations (P < 0.05) with 14% to 100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications found greater AUCs for textures than PMD (P < 0.05); increases were 0.04 to 0.25 by study. Discrimination from PMD and these textures jointly was significantly higher than from PMD alone (P < 0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Some textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field., (©2024 American Association for Cancer Research.)

Women with high mammographic density have an increased risk of breast cancer. They may be offered contrast-enhanced mammography to improve breast cancer screening performance. Using a cohort of women receiving contrast-enhanced mammography, we evaluated whether conventional and modified mammographic density measures were associated with breast cancer. Sixty-six patients with newly diagnosed unilateral breast cancer were frequency matched on the basis of age to 133 cancer-free control individuals. On low-energy craniocaudal contrast-enhanced mammograms (equivalent to standard mammograms), we measured quantitative mammographic density using CUMULUS software at the conventional intensity threshold ("Cumulus") and higher-than-conventional thresholds ("Altocumulus," "Cirrocumulus"). The measures were standardized to enable estimation of odds ratio per adjusted standard deviation (OPERA). In multivariable logistic regression of case-control status, only the highest-intensity measure (Cirrocumulus) was statistically significantly associated with breast cancer (OPERA = 1.40, 95% confidence interval = 1.04 to 1.89). Conventional Cumulus did not contribute to model fit. For women receiving contrast-enhanced mammography, Cirrocumulus mammographic density may better predict breast cancer than conventional quantitative mammographic density., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus., Methods: We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants., Results: There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%)., Conclusion: Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins., Impact: Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens., (©2023 American Association for Cancer Research.)

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., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered., Development: We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors., Application: For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors., Conclusion: VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk., (© The Author(s) 2023; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Mammographic dense area (MDA) is an established predictor of future breast cancer risk. Recent studies have found that risk prediction might be improved by redefining MDA in effect at higher-than-conventional intensity thresholds. We assessed whether such higher-intensity MDA measures gave stronger prediction of subsequent contralateral breast cancer (CBC) risk using the Women's Environment, Cancer, and Radiation Epidemiology (WECARE) Study, a population-based CBC case-control study of ≥1 year survivors of unilateral breast cancer diagnosed between 1990 and 2008. Three measures of MDA for the unaffected contralateral breast were made at the conventional intensity threshold ("Cumulus") and at two sequentially higher-intensity thresholds ("Altocumulus" and "Cirrocumulus") using the CUMULUS software and mammograms taken up to 3 years prior to the first breast cancer diagnosis. The measures were fitted separately and together in multivariable-adjusted logistic regression models of CBC (252 CBC cases and 271 unilateral breast cancer controls). The strongest association with CBC was MDA defined using the highest intensity threshold, Cirrocumulus (odds ratio per adjusted SD [OPERA] 1.40, 95% CI 1.13-1.73); and the weakest association was MDA defined at the conventional threshold, Cumulus (1.32, 95% CI 1.05-1.66). In a model fitting the three measures together, the association of CBC with Cirrocumulus was unchanged (1.40, 95% CI 0.97-2.05), and the lower brightness measures did not contribute to the CBC model fit. These results suggest that MDA defined at a high-intensity threshold is a better predictor of CBC risk and has the potential to improve CBC risk stratification beyond conventional MDA measures., (© 2022 UICC.)

Background: Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants., Methods: We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia., Results: We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p < 0.05. TWAS further identified two novel genes (SHOX2 and CRISPLD2) whose genetically predicted expression was significantly associated with MD phenotypes., Conclusions: Our findings provided novel insight into the genetic background of MD phenotypes, and further demonstrated their shared genetic basis with breast cancer., (© 2022. The Author(s).)

We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk., Prevention Relevance: Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum., (©2021 The Authors; Published by the American Association for Cancer Research.)

In this issue, Kresovich and colleagues have published a hallmark paper in Molecular, Environmental, Genetic and Analytic Epidemiology. By applying artificial intelligence to the Sister Study they created a new methylation-based breast cancer risk score (mBCRS) based on blood DNA methylation. Using a prospective design and after accounting for age and questionnaire-based breast cancer risk factors, the Odds PER Adjusted standard deviation (OPERA) for mBCRS and polygenic risk score (PRS) was 1.58 (95% CI: 1.38, 1.81) and 1.58 (95% CI: 1.36, 1.83), respectively, and the corresponding area under the receiver operating curve was 0.63 for both. Therefore, mBCRS could be as powerful as the current best PRS in differentiating women of the same age in terms of their breast cancer risk. These risk scores are among the strongest known breast cancer risk-stratifiers, shaded only by new mammogram risk scores based on measures other than conventional mammographic density, such as Cirrocumulus and Cirrus, which when combined have an OPERA as high as 2.3. The combination of PRS and mBCRS with the other measured risk factors gave an OPERA of 2.2. OPERA has many advantages over changes in areas under the receiver operator curve because the latter depend on the order in which risk factors are considered. Although more replication is needed using prospective data to protect against reverse causation, there are many novel molecular and analytic aspects to this paper which uncovers a potential mechanism for how genetic and environmental factors combine to cause breast cancer., (© 2021 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Objectives: PET using F-fluorodeoxyglucose (FDG) has proven to be valuable in staging and monitoring of treatment response in breast cancer. We aimed to assess the prognostic value of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in patients with breast cancer., Methods: A systematic search of MEDLINE and EMBASE was performed using the keywords of breast cancer, PET, and volume. Inclusion criteria were F-FDG PET used as an initial imaging tool; studies limited to patients with breast cancer who had not undergone any treatment before PET scans; and studies reporting survival data. Event-free survival (EFS) and overall survival (OS) were considered markers of outcome., Results: Nine studies comprising 975 patients were included in this study. The pooled hazard ratio (HR) for adverse events was 33.73 (P < 0.00001; I = 0%) with MTV from primary tumor and 2.89 (P < 0.00001; I = 45%) with TLG from primary tumor, meaning that primary tumors with high volumetric parameters were associated with progression or recurrence. However, the combined HRs for EFS of MTV, and TLG, and those for OS of MTV from whole-body tumor were NS. The pooled HR for OS of TLG from whole-body tumor was 2.95 (P = 0.18; I = 71%)., Conclusion: Volumetric parameters from F-FDG PET are significant prognostic factors for outcome in patients with breast cancer. Patients with a high MTV or TLG from primary tumor have a higher risk of adverse events. Patients with a high TLG from whole-body tumor have a higher risk of deaths.

Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer., (© 2019 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Introduction: For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk., Methods and Analysis: The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk., Ethics and Dissemination: Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3)., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

DNA methylation-based biological age (DNAm age), as well as genome-wide average DNA methylation, have been reported to predict breast cancer risk. We aimed to investigate the associations between these DNA methylation-based risk factors and 18 conventional breast cancer risk factors for disease-free women. A sample of 479 individuals from the Australian Mammographic Density Twins and Sisters was used for discovery, a sample of 3354 individuals from the Melbourne Collaborative Cohort Study was used for replication, and meta-analyses pooling results from the two studies were conducted. DNAm age based on three epigenetic clocks (Hannum, Horvath and Levine) and genome-wide average DNA methylation were calculated using the HumanMethylation 450 K BeadChip assay data. The DNAm age measures were positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche (all nominal P < 0.05). Genome-wide average DNA methylation was negatively associated with smoking and number of live births, and positively associated with age at first live birth (all nominal P < 0.05). The association of DNAm age with BMI was also evident in within-twin-pair analyses that control for familial factors. This study suggests that some lifestyle and hormonal risk factors are associated with these DNA methylation-based breast cancer risk factors, and the observed associations are unlikely to be due to familial confounding but are likely causal. DNA methylation-based risk factors could interplay with conventional risk factors in modifying breast cancer risk.

Background: Obtaining tumor specimens and re-evaluating targeted markers is recommended, if possible, in breast cancer patients who relapsed after curative treatment. The biomarker status changes in rebiopsied tumors have been demonstrated to have considerable clinical implications., Objectives: To identify the changes of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status between the primary and recurrent lesions., Materials and Methods: We conducted a study among 67 patients with recurrent breast cancer, recruited from January 2014 to September 2018 in the Vietnam National Cancer Hospital to compare ER, PR, and HER2 status between the primary and recurrent lesions. For each patient, a specimen of their primary tumor and another specimen of recurrent lesions underwent pathological assessment. Immunohistochemistry (IHC) was performed to determine ER, PR, and HER2 status in both specimens., Results: Biomarker status conversion rates (in both directions) between primary and recurrent tumors were 26.9% for ER, 38.8% for PR, and 22.4% for HER2. Overall, IHC subtypes (hormone receptor positive, HER2 amplified, and triple-negative) changed in 25 out of 67 (37.3%) cases. Conversion rates were not statistically significantly different between patients with different recurrent sites and times of recurrence. Eight out of 13 initially triple-negative patients (61.5%) had a change to positive status of either ER, PR, or HER2., Conclusion: A substantial discordance in ER, PR, and HER2 status were observed between primary breast cancer tissues and recurrent lesions. Rebiopsy could bring new therapeutic opportunities in the management of patients with recurrent breast cancer., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2019 Thi Hoa Nguyen et al.)

The aim of the study was to investigate health-related quality of life among Vietnamese breast cancer women who were treated at National Cancer Hospital, Hanoi, Vietnam, in 2018. Information about physical functioning, Role Physical, Bodily Pain, General Health, vitality, Social Functioning, Role Emotional, and Mental Health of 200 patients with breast cancer was collected through face-to-face interview, using short form-36 questionnaire. We found that the older patients (older than 50 years) had higher score of Mental Health than patients at age 50 and lower ( P < .05). The patients who had better economic status had significantly higher score of Vitality ( P < .05). Patients who were married and living with their partners/husband had better quality of life in General Health ( P <0.05). The patients who had less than 6 months of treatment had better physical functioning score ( P < .05) than the patients who had treatment longer than 6 months. Patients with caring supports from family members had higher scores of Bodily Pain, Social Functioning, Role Emotional, and Mental Health. Patients who have stressed feelings had significantly lower scores of all domains, except for Physical Functioning. The participants who usually stay up late reported lower scores of all components except for Physical Functioning and Role Physical. In conclusion, it is needed to develop psychosocial services, enhance early screening, and diagnose for the women in Vietnam.

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.

High mammographic density (MD) is the most important risk factor for breast cancer. This study aimed to clarify the relationship between MD and breast cancer subtypes defined by tumor markers. We enrolled 642 women with breast cancer (69% premenopausal) and 1241 controls matched for age and menopausal status. Absolute mammographic dense area (ADA), percent mammographic dense area (PDA), and nondense area were assessed using a computer-assisted thresholding technique. We classified breast cancer cases into four subtypes using information on tumor marker expression such as estrogen receptor (ER), progesterone receptor (PR), and Cerb2 receptor (HER2); luminal A (ER+ and/or PR+, HER2-), luminal B (ER+ and/or PR+, HER2+), HER2-overexpressing (ER-, PR-, and HER2+), and triple-negative (ER-, PR-, and HER2-). Analysis was carried out using a conditional logistic regression model with adjustment for covariates. ADA and PDA were associated positively with the risk of breast cancer overall. Both ADA and PDA tended to have a positive association with breast cancer with any ER, any PR, or HER2-, but not for HER2+. The risk of luminal A breast cancer increased significantly 1.11 times (95% confidence interval: 1.01-1.23) for ADA and 1.12 times (95% confidence interval: 1.01-1.24) for PDA, estimated per 1 SD of the age and BMI-adjusted MD. However, the risk of breast cancer with luminal B, HER2-overexpressing, and triple-negative subtypes did not differ (P>0.10). Differential associations between MD measures and breast cancer by tumor marker status or tumor marker-defined subtypes were not detected. These findings suggested that the association between MD and breast cancer subtype may be because of other causal pathways.

Purpose To compare three mammographic density measures defined by different pixel intensity thresholds as predictors of breast cancer risk for two different digital mammographic systems. Materials and Methods The Korean Breast Cancer Study included 398 women with invasive breast cancer and 737 control participants matched for age at mammography (±1 year), examination date, mammographic system, and menopausal status. Mammographic density was measured by using the automated Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software and the semiautomated Cumulus software at the conventional threshold (Cumulus) and at increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were Box-Cox-transformed and adjusted for age, body mass index, and menopausal status. Conditional logistic regression was used to estimate risk associations. For calculation of measures of predictive value, the change in odds per standard deviation (OPERA) and the area under the receiver operating characteristic curve (AUC) were used. Results For dense area, with use of the direct conversion system the OPERAs were 1.72 (95% confidence interval [CI]: 1.38, 2.15) for LIBRA, 1.58 (95% CI: 1.27, 1.97) for Cumulus, 2.04 (95% CI: 1.60, 2.59) for Altocumulus, and 3.48 (95% CI: 2.45, 4.47) for Cirrocumulus (P < .001). The corresponding AUCs were 0.70, 0.69, 0.76, and 0.89, respectively. With use of the indirect conversion system, the corresponding OPERAs were 1.50 (95% CI: 1.28, 1.76), 1.36 (95% CI: 1.16, 1.59), 1.40 (95% CI: 1.19, 1.64), and 1.47 (95% CI: 1.25, 1.73) (P < .001) and the AUCs were 0.64, 0.60, 0.61, and 0.63, respectively. Conclusion It is possible that mammographic density defined by higher pixel thresholds could capture more risk-predicting information with use of a direct conversion mammographic system; the mammographically bright, rather than white, regions are etiologically important. © RSNA, 2017.

Background: In spite of the increasing incidence of in situ breast cancer, the information about the risk factors of in situ breast cancer (DCIS) is scarce as compared to the information available for invasive ductal breast cancer (IDC), with inconsistent findings regarding the difference in risk factors between DCIS and IDC., Methods: We enrolled 472 women with IDC and 90 women with DCIS and 1088 controls matching for age and menopausal status. Information on risk factors was collected through self-administered questionnaire. Percent mammographic dense area (PDA), absolute mammographic dense area (ADA), and nondense area were assessed using a computer-assisted thresholding technique. Odds ratio (OR) and 95% confidence intervals (CI) were estimated by conditional logistic regression model with adjustment for covariates., Results: Later age at menarche and regular physical exercise were associated with decreased risk of IDC, whereas alcohol consumption, previous benign breast disease, and family history of breast cancer were associated with increased risk of IDC. For DCIS, previous benign breast disease and alcohol consumption were associated with the increased risk, and regular physical exercise was associated with decreased risk. Increase of ADA by 1-quartile level and PDA increase by 10% were associated with 1.10 (95% CI: 1.01, 1.21) and 1.10 (95% CI: 1.01, 1.19) times greater risk of IDC, respectively. The increase of ADA by 1-quartile level and PDA increase by 10% were associated with 1.17 (95% CI: 0.91, 1.50) times and 1.11 (95% CI:0.90,1.37) times greater risk of DCIS, respectively, but the associations were not statistically significant. There was no significant difference in the association with risk factors and mammographic density measures between IDC and DCIS (P > 0.1)., Conclusions: Differential associations of DCIS with mammographic density and risk factors as compared with the associations of IDC were not evident. This finding suggests that IDC and DCIS develop through the shared causal pathways.

Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer., Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus , and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus , respectively. All measures were Box-Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC)., Results: Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6 , respectively) . For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus , respectively (all P  < 0.001). After adjusting for Altocumulus and Cirrocumulus , Cumulus was not significant ( P  > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64-2.14] and AUC = 0.68 (0.65-0.71)., Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research., (© The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association)

The aim of the present study is to determine if body mass index (BMI) during childhood is associated with the breast cancer risk factor 'adult mammographic density adjusted for age and BMI'. In 1968, the Tasmanian Longitudinal Health Study studied every Tasmanian school child born in 1961. We obtained measured heights and weights from annual school medical records across ages 7-15 years and imputed missing values. Between 2009 and 2012, we administered to 490 women a questionnaire that asked current height and weight and digitised at least one mammogram per woman. Absolute and percent mammographic densities were measured using the computer-assisted method CUMULUS. We used linear regression and adjusted for age at interview and log current BMI. The mammographic density measures were negatively associated: with log BMI at each age from 7 to 15 years (all p < 0.05); with the average of standardised log BMIs across ages 7-15 years (p < 0.0005); and more strongly with standardised log BMI measures closer to age 15 years (p < 0.03). Childhood BMI measures explained 7 and 10 % of the variance in absolute and percent mammographic densities, respectively, and 25 and 20 % of the association between current BMI and absolute and percent mammographic densities, respectively. Associations were not altered by adjustment for age at menarche. There is a negative association between BMI in late childhood and the adult mammographic density measures that predict breast cancer risk. This could explain, at least in part, why BMI in adolescence is negatively associated with breast cancer risk.

Introduction: When measured using the computer-assisted method CUMULUS, mammographic density adjusted for age and body mass index predicts breast cancer risk. We asked if new mammographic density measures defined by higher brightness thresholds gave better risk predictions., Methods: The Korean Breast Cancer Study included 213 women diagnosed with invasive breast cancer and 630 controls matched for age at full-field digital mammogram and menopausal status. Mammographic density was measured using CUMULUS at the conventional threshold (Cumulus), and in effect at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus, respectively. All measures were Box-Cox transformed and adjusted for age, body mass index, menopausal status and machine. We used conditional logistic regression to estimate the change in Odds PER standard deviation of transformed and Adjusted density measures (OPERA). The area under the receiver operating characteristic curve (AUC) was estimated., Results: Corresponding Altocumulus and Cirrocumulus density measures were correlated with Cumulus measures (r approximately 0.8 and 0.6, respectively). Altocumulus and Cirrocumulus measures were on average 25% and 80% less, respectively, than the Cumulus measure. For dense area, the OPERA was 1.18 (95% confidence interval: 1.01-1.39, P = 0.03) for Cumulus; 1.36 (1.15-1.62, P < 0.001) for Altocumulus; and 1.23 (1.04-1.45, P = 0.01) for Cirrocumulus. After fitting the Altocumulus measure, the Cumulus measure was no longer associated with risk. After fitting the Cumulus measure, the Altocumulus measure was still associated with risk (P = 0.001). The AUCs for dense area was 0.59 for the Altocumulus measure, greater than 0.55 and 0.57 for the Cumulus and Cirrocumulus measures, respectively (P = 0.001). Similar results were found for percentage dense area measures., Conclusions: Altocumulus measures perform better than Cumulus measures in predicting breast cancer risk, and Cumulus measures are confounded by Altocumulus measures. The mammographically bright regions might be more aetiologically important for breast cancer, with implications for biological, molecular, genetic and epidemiological research and clinical translation.

We carried out this study to evaluate the association between mammographic density adjusted for age and BMI and early-onset breast cancer in Asian women. We recruited 213 Korean patients with breast cancer (45% diagnosed before the age of 50 years) and 630 controls matched for age, menopausal status, and examination date. The percentage and absolute size of dense areas on digital mammograms were measured using a computer-assisted thresholding technique (Cumulus). We carried out an analysis using the conditional logistic regression model with adjustment for covariates. An increase by 1 SD in age and BMI-adjusted absolute dense area and percentage dense area was associated with a 1.15-fold (95% confidence interval: 1.03, 1.29) and 1.20-fold (95% confidence interval: 1.06, 1.37) increased risk of breast cancer, respectively. These associations were stronger for premenopausal disease (P=0.07 and 0.01, respectively) and for disease diagnosed before age 50 (P=0.07 and 0.02, respectively) than for postmenopausal disease (P=0.16 and 0.23, respectively) or later onset disease (P=0.10 and 0.10, respectively). There was no difference in the associations with premenopausal versus postmenopausal and early-onset versus late-onset disease. After adjusting for age and BMI, both a greater absolute dense area and a greater percentage dense area were associated with an increased risk of breast cancer, particularly at a young age.

Purpose: Bone mineral density (BMD) may be useful as a surrogate marker reflecting lifetime exposure to estrogen in a woman. Our study aimed to investigate an association between BMD and breast cancer risk., Methods: A case-control study was conducted using 253 breast cancer cases and 506 age and menopausal status-matched controls from the same institution. Cases were ascertained through medical record review of the women with abnormal mammographic findings. BMD was measured at the lumbar spine and femoral neck using a dual-energy X-ray absorptiometry. The association was estimated by conditional logistic regression analysis with an adjustment for covariates., Results: Although there was no difference in the association between pre- and postmenopausal disease, the association between BMD and breast cancer was evident for postmenopausal breast cancer. One standard deviation in age and menopausal status adjusted BMD at lumbar spine and femur neck was associated with 1.35-fold (standard error = 0.19, P = .04) and 1.34-fold (standard error = 0.20, P = .05) increased likelihood of breast cancer risk, respectively, for postmenopausal women., Conclusion: After adjusting for covariates, higher BMD at lumbar spine and femur neck are associated with increased likelihood of breast cancer risk for postmenopausal women. These findings suggest that BMD could be included in breast cancer risk prediction models for postmenopausal Korean women., (Copyright © 2014 Elsevier Inc. All rights reserved.)

HER2 overexpression is associated with aggressive breast cancer with high recurrence rate and poor patient prognosis. Treatment of HER2 overexpressing patients with the HER2 targeting therapy trastuzumab results in acquired resistance within a year. The HER2/EGFR dual kinase inhibitor lapatinib was shown to inhibit some trastuzumab resistant breast cancer cell lines and is currently in clinical trials. Our group has found two new quinone compounds that show excellent inhibition of breast tumor cells expressing HER2 or the trastuzumab resistant HER2 oncogenic isoform, HER2Δ16. Compound 4 ((1R,2S,3S)-1,2,3,5,8-pentahydroxy-1,2,3,4-tetrahydroanthracene-9,10-dione) and compound 5 (5,8-dihydroxy-2,3-bis(hydroxymethyl)naphthalene-1,4-dione) showed sub-micromolar inhibition potency against these cell lines. These compounds also inhibit auto-phosphorylation of the Y1248 and Y1068 residues of HER2 and EGFR, respectively., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Background: Mammographic density, the area of the mammographic image that appears white or bright, predicts breast cancer risk. We estimated the proportions of variance explained by questionnaire-measured breast cancer risk factors and by unmeasured residual familial factors., Methods: For 544 MZ and 339 DZ twin pairs and 1,558 non-twin sisters from 1,564 families, mammographic density was measured using the computer-assisted method Cumulus. We estimated associations using multilevel mixed-effects linear regression and studied familial aspects using a multivariate normal model., Results: The proportions of variance explained by age, body mass index (BMI), and other risk factors, respectively, were 4%, 1%, and 4% for dense area; 7%, 14%, and 4% for percent dense area; and 7%, 40%, and 1% for nondense area. Associations with dense area and percent dense area were in opposite directions than for nondense area. After adjusting for measured factors, the correlations of dense area with percent dense area and nondense area were 0.84 and -0.46, respectively. The MZ, DZ, and sister pair correlations were 0.59, 0.28, and 0.29 for dense area; 0.57, 0.30, and 0.28 for percent dense area; and 0.56, 0.27, and 0.28 for nondense area (SE = 0.02, 0.04, and 0.03, respectively)., Conclusions: Under the classic twin model, 50% to 60% (SE = 5%) of the variance of mammographic density measures that predict breast cancer risk are due to undiscovered genetic factors, and the remainder to as yet unknown individual-specific, nongenetic factors., Impact: Much remains to be learnt about the genetic and environmental determinants of mammographic density., (©2013 AACR.)

Objectives: Syndecan-1 expression is decreased in diverse tumor types but remains controversial in breast carcinomas. The goal of the study was to examine syndecan-1 expression in breast carcinoma and its prognostic significance., Methods: The epithelial expression of syndecan-1 was examined in tissue microarrays constructed from 62 consecutive breast carcinoma cases diagnosed between 1997 and 2004 with distant organ metastasis and 10 consecutive control cases (breast carcinoma with no distant metastasis after at least 8 years of follow-up). The prognostic significance of syndecan-1 was estimated by utilizing a Cox proportional hazards regression model., Results: Among tumors with distant metastasis, syndecan-1 expression was significantly associated with a higher histologic grade and inversely related to hormonal receptor status. The HER2 subtype and triple-negative carcinomas exhibited markedly higher syndecan-1 levels than those of luminal subtypes, while the latter remained significantly higher than nonmetastatic control cases. Furthermore, high syndecan-1 expression had a negative impact on both overall and disease-free survival rates., Conclusions: These findings suggest that syndecan-1 may regulate breast cancer cell behavior and thus deserves further investigation to ascertain its potential as a therapeutic target, especially in metastatic, triple-negative carcinomas.