Breast density is a strong and potentially modifiable breast cancer risk factor. Almost everything we know about breast density has been derived from mammography, and therefore, very little is known about breast density in younger women aged <40. This study examines the acceptability and performance of two alternative breast density measures, Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA), in women aged 18–40.Breast tissue composition  was measured in 539 women aged 18–40 using OBS. For a subset of 169 women, breast density was also measured via DXA (percent fibroglandular dense volume (%FGV), absolute dense volume (FGV), and non-dense volume (NFGV)). Acceptability of the measurement procedures was assessed using an adapted validated questionnaire. Performance was assessed by examining the correlation and agreement between the measures and their associations with known determinants of mammographic breast density.Over 93% of participants deemed OBS and DXA to be acceptable. The correlation between OBS-%water + collagen and %FGV was 0.48. Age and BMI were inversely associated with OBS-%water + collagen and %FGV and positively associated with OBS-%lipid and NFGV.OBS and DXA provide acceptable and viable alternative methods to measure breast density in younger women aged 18–40 years. Mammographic breast density, represented by the white radiographic appearance of epithelial and connective breast tissue on a mammogram, is one of the strongest predictors of breast cancer risk, with high breast density associated with increased risk , 2. BreaBreast density appears to be modifiable, and reducing breast density through medical intervention  reduces breast cancer risk , 8. Recer = 0.88) [Bridging the gaps in knowledge regarding the distribution and determinants of breast density in younger women requires a safe, acceptable, and viable measurement method. Alternative methods include optical techniques –13 and D = 0.88) . Newly d = 0.88) . For the = 0.88) .r = 0.76) [r = 0.72), with similar associations with age and BMI [Breast density can also be measured in younger women using DXA, which measures breast density using minimal X-ray radiation safely in non-pregnant individuals . DXA mea = 0.76)  and in p and BMI . %FGV ha and BMI .This study examines the acceptability and performance of these two alternative procedures, OBS and DXA, to measure breast density in younger women aged 18–40 years. We compare measures of OBS and DXA and investigate their associations with known determinants of mammographic breast density that also predict breast cancer risk.Five hundred and thirty-nine women aged between 18 and 40 were recruited via the University of Western Australia Crowd Sourcing website , RegisteParticipation included a height measurement using a wall-mounted stadiometer and a weight measurement using digital floor weight scales, completion of an epidemiological questionnaire, an OBS breast scan, and a post-scan acceptability questionnaire. Areola size, skin colour and information regarding nipple piercings and scars/tattoos on the breasts were also recorded. Women recruited after October 2017 were also asked to complete a breast DXA scan.2). The epidemiological questionnaire included questions relating to hormonal contraceptive use , age of menarche, alcohol and tobacco use , pregnancy, age of first and last births, breastfeeding , and history of breast disease and family history of breast cancer . The acceptability questionnaire assessed the overall acceptability of the OBS and DXA scans regarding scan comfort, duration, and positioning, using a 5-point Likert scale.BMI was calculated from the measured height and weight data  benign breast disease. Intraclass correlation coefficients (ICC) were used to assess intra- and inter-reader reliability for the DXA breast density measures.Approval to conduct this research was provided by the University of Western Australia Human Research Ethics Committee in accordance with its ethics review and approval procedures (2020/ET000013). Informed written consent was obtained from all participants.Participants were asked to undress from the waist up and change into an open-fronted hospital gown for the examination. Participants chose an appropriate breast size from four cups representing approximate bra cup sizes A-D. If a woman’s breast exceeded the largest cup size, only a fraction of the volume would be optically interrogated. A trained research assistant performed a reference measure on a static silicone phantom mould using the chosen cup. Participants were asked to place the cup over their left breast and hold it in place during the scan. Figure Breast tissue composition measures using chromophore concentrations were calculated, providing measures of OBS-%water, OBS-%collagen and OBS-%lipid. A combined measure of %water plus %collagen  was also calculated, as was the principal component with the strongest water-associated absorption, used previously to represent the fibroglandular tissue in the breast .DXA scans of both breasts were carried out using a clinical GE DXA machine according to the breast density measurement and calibration protocol previously outlined . Briefly3 were re-checked for image quality. An average of the left and right breast was used for each participant.The total projected breast area was manually delineated on each image, and the %FGV, absolute dense volume (FGV), and total breast volume were computed . Non-denDescriptive statistics  were used to summarise participant characteristics and acceptability of OBS and DXA. Age at first and last birth were centred on their mean, and all women having never given birth were assigned 0.Scatterplots were used to visualise the correlation between OBS measures  and DXA measures . Corresponding Pearson correlation coefficients were estimated and stratified by OBS breast cup size. Linear mixed-effects models were used to investigate the interaction between cup size and a DXA measure as a predictor of an OBS measure. Likelihood ratio tests assessed evidence of an interaction. Agreement between quartiles and dichotomised measures of OBS and DXA were assessed using weighted Cohen’s Kappa statistics.P value of <0.05. Model fit was compared using likelihood ratio tests and the Akaike information criterion. Sensitivity analysis was performed on women with both OBS and DXA measurements .Univariable and multivariable linear regression were used to investigate the associations of OBS measures with age, BMI, oral conceptive use, reproductive history, family history of breast cancer and smoking and alcohol use. Similarly, univariable and multivariable mixed-effect models were used to investigate the same associations with the DXA measures. The reader was treated as a random effect to account for repeated DXA measures (from two readers). Age and BMI were included in all multivariable models as these are known predictors for breast density, as was the number of live births when adjusting for breastfeeding. Diagnostic plots of age-adjusted residuals were checked for the model assumption of normality. This required FGV and NFGV to be square root transformed. Backward stepwise regression was performed for the multivariable models using a cut-off Of the 539 women who had an OBS scan, 8 were excluded post-examination due to ineligibility and 20 due to insufficient OBS data. Of the remaining 511 women, chromophore concentrations were estimated for 501 women, but the principal component analysis was only possible for 397 women.Of the 169 women with DXA scans, 6 were excluded due to image artefacts, and 7 were excluded post-examination due to ineligibility. When DXA measures were the outcome, only women who had eligible OBS and DXA measures were included, leaving 132 women in the subset of women with DXA data. A flowchart showing recruitment and measurement numbers is presented in Supplementary Fig. 2, s.d. = 5.4). The majority of women with OBS measures had no live births (56.3%), had never breastfed (57.9%) and the mean age of menarche was 12.8 years (s.d. = 1.4). Most women had no family history of breast cancer (56.1%). The mean of OBS-%water+collagen was 38.2% (s.d. = 9.6) and the mean of %FGV was 45.2% (s.d. = 17.0).Table The ICCs for %FGV, FGV and NFGV demonstrated high intra- and inter-reader reliability, with the ICCs > 0.90 for all three measures. For the repeated DXA scans on the left breast, the ICCs were likewise high, >0.85, for both %FGV and FGV.Over 93% of participants deemed the examination process for OBS to be an acceptable method of measuring breast density, compared with over 98% finding DXA to be acceptable. The exam comfort for both OBS and DXA was considered acceptable by more than 97% of participants. The duration of scans was also acceptable for both procedures . The weighted kappa score for %FGV and FGV was −0.05 (P = 0.36), indicating poor agreement.Table Tables Age was inversely associated with OBS-%water+collagen, FGV and %FGV and positively associated with OBS-%lipid and NFGV.BMI was inversely associated with OBS-%water+collagen and %FGV and positively associated with OBS-%lipid and NFGV.Univariably, pregnancy variables  were inversely associated with OBS-%water+collagen and %FGV. However, these associations were attenuated with the addition of age in the multivariable model.No association was found between contraceptive use and the other OBS or DXA measures.Ptrend = 0.01) and positively associated with NFGV (Ptrend = <0.001). There was also evidence of the association between alcohol use and FGV (Ptrend = <0.001).Smoking was inversely associated with %FGV (Ptrend<0.01) however, this association was only observed in the multivariable model.Incidence of 1st-degree family history of breast cancer was inversely associated with %FGV  and viable alternative breast density measures. We found that OBS and DXA measures are significantly correlated but less so in larger-breasted women. The determinants of OBS and DXA measures in younger women were found to be consistent with known determinants of mammographic breast density in screen-aged women, suggesting that much of what we know about the factors that influence breast density in older women can be extrapolated to younger women. Further investigation of OBS and DXA measures as predictors of breast cancer risk and their effectiveness in monitoring change in breast density over time is needed.This study is the first to demonstrate participant acceptability of the OBS and DXA measurements, with over 93% of women reporting that both OBS and DXA procedures were acceptable. Finding a method of measuring breast density that is acceptable for women, particularly younger women, is vital to ensure high levels of participation for repeated measures and follow-up. OBS is a custom-designed modality specifically designed to quantify a breast density surrogate and is potentially a preferred procedure for repeated measures as it is much more cost-effective, portable, easy to use, and emits no ionising radiation, compared to DXA.r = 0.48), consistent with recently reported correlations in adolescent girls [Overall, OBS%-water+collagen was correlated with %FGV  are known to strongly predict breast cancer risk ; that isIn the absence of a gold standard breast density measurement that is safe for younger women, reporting associations with other measures associated with mammographic breast density and/or breast cancer risk is arguably a better approach to infer performance as pseudo-breast density measures. We found the determinants of OBS breast density and DXA breast density in younger women to be consistent with known determinants of mammographic breast density in screen-aged women. Age is strongly negatively associated with both the OBS and DXA breast density measures and positively associated with the non-dense or “fatty” measures, which is consistent with evidence that mammographic breast density significantly decreases with age . BMI wasOur study is the first to examine the relationship between an optical measure of the percentage of breast collagen and image-estimated fibroglanduar volume. We found no correlation between OBS-%collagen and overall %FGV, which is consistent with literature however, a positive association between collagen fibre density and local percent density measures has been reported among women referred for biopsy . CollageThis study also demonstrated that measuring breast density using chromophore concentrations is more effective/efficient compared to principal component measures. The number of usable observations was reduced for OBS-PC3 due to data processing. Less than 2% of available data was lost using chromophore concentration processing, instead of almost 25% lost using principal component analysis due to the stricter data requirements.Study limitations include restrictions to participation due to breast size, effectively eliminating women with breasts larger than the biggest cup. Study strengths include its size, the largest investigation of breast density in younger women aged 18–40 to date, with over 500 participants. The inclusion of both optical and image-based measures enabled comparisons not previously reported within this age range.This study showed that OBS and DXA are acceptable and viable alternative methods of measuring breast density in younger women (18–40 years). OBS and DXA measures correlate but do not necessarily measure the same breast tissue composition. We presented new evidence regarding the determinants of breast density in younger women, suggesting that much of what we know about the factors that influence breast density in older women can be extrapolated to younger women. This has important implications for future research investigating the utility of measuring breast density in younger women to identify and target those at increased risk of breast cancer later in life. Our research focus will now shift to investigating the effectiveness of OBS and DXA to measure changes in breast density over time, informing the utility of breast density to monitor the effectiveness of breast cancer prevention strategies that target breast density reduction.Supplementary Information


Long-term survivors of cutaneous malignant melanoma (CMM) risk subsequent malignancies due to both host-related and environmental risk factors. This retrospective population-based study differentially assesses the risk of synchronous and metachronous cancers in a cohort of CMM survivors stratified by sex.The cohort study (1999–2018) included 9,726 CMM survivors  recorded by the cancer registry of all 5,000,000 residents in the Italian Veneto Region. By excluding subsequent CMM and non-CMM skin cancers, the incidence of synchronous and metachronous malignancies was calculated according to sex and tumor site, standardizing for age and calendar year. The Standardized Incidence Ratio (SIR) was calculated as the ratio between the number of subsequent cancers among CMM survivors and the expected number of malignancies among the regional population.Irrespective of the site, the SIR for synchronous cancers increased in both sexes . Both sexes also demonstrated an excess risk for synchronous kidney/urinary tract malignancies , and women had an increased risk of synchronous breast cancer (SIR = 1.69). CMM male survivors featured a higher risk of metachronous thyroid , and prostate  malignancies. Among females, metachronous cancers featured higher SIR values than expected: kidney/urinary tract , non-Hodgkin’s lymphoma , and breast . Females had an overall increased risk of metachronous cancers in the first 5 years after CMM diagnosis (SIR = 1.54 at 6–11 months and 1.37 at 1–5 years).Among CMM survivors, the risk of metachronous non-skin cancers is higher than in the general population and differs significantly by sex. These results encourage sex-tailored interventions for metachronous secondary cancer prevention. Over the last few decades, we have seen a constant increase in cutaneous malignant melanoma (CMM), particularly among fair-skinned populations . AccountIn the United States alone, the incidence of CMM went from 7.9/100,000 in 1975 to 25.3/100,000 in 2018, more than a 320% increase. In Italy in 2020, CMM was the third most common malignancy, with 14,900 new cases and a 5 year survival rate of 88% for men and 91% for women , as well as decision-supporting tools. The current retrospective population-based study differentially assesses the risk of synchronous and metachronous cancers in a cohort of CMM survivors stratified by sex.In Northeast Italy, the Veneto region covers approximately 18,345 square kilometers, with a resident population of over 4.8 million . MortaliRegistro Tumori Veneto” [RTV]), which was first established in 1987. The population coverage increased from 1,154,000 inhabitants in 1987 to nearly five million  in 2014  primary malignancies except for skin cancers (melanomatous and non-melanomatous) were considered. An interval time of 6 months from the index-CMM was used to distinguish synchronous (time elapsed ≤6 months) versus metachronous (time elapsed >6 months) multiple malignancies , 11.Cancer incidence rates were calculated from the RTV database, stratifying patients by sex, malignancy site, age bracket  and calendar year group . The Standardized Incidence Ratio (SIR) was calculated as the ratio between the observed cancers and the number that would be expected based on the incidence rates for the general population . The obsTo calculate the expected number of malignancies, the accumulated person-years at risk (PY) was multiplied by the rates to be expected if CMM survivors experienced the same cancer rates as the general reference population. The PY were defined as the period between a patient’s melanoma diagnosis and one of these following events: a second cancer diagnosis, death, or the end of the period considered . Byar’s accurate approximation to the exact Poisson distribution was used to calculate 95% confidence intervals (95% CI) . The Ben[(observed number – expected number) ÷ PY] × 1,000.p < 0.05 level. The Multiple Primary – Standardized Incidence Ratios session of SEER*Stat 8.4.0  was used for the analysis  were retrospectively considered. In 65,046 PY, 833 s malignancies (92 synchronous and 741 metachronous) were recorded.Between January 1st, 1999 and December 31st, 2018, 9,726 CMM patients  and metachronous  malignancies were more common among males.Both synchronous  was 1.84  for synchronous and 1.12  for metachronous malignancies. The Absolute Excess Risk (AER) per 1,000 PY was 8.93  for synchronous and 1.33  for metachronous cancers.Irrespective of the cancer site, the SIR of synchronous cancers was higher than expected in males  and females  .Both sexes featured an increased risk, compared to the general reference population, of synchronous kidney/urinary tract cancer . Females also had an increased risk of synchronous breast cancer.Between the 7th month and the end of the 12th month, the SIR of metachronous cancers was 1.29 , while from the 13th to the 60th month from the CMM index, and the SIR was 1.01 .For metachronous cancer sites, the SIRs for prostate  and thyroid  cancers were significantly higher than expected . ConsistThe SIR of metachronous prostate cancer was higher in the 13–60 month interval . Similar findings were also obtained for thyroid malignancies .Regardless of the cancer site, both SIR  and AER per 1,000 PY  showed an increased risk of metachronous malignancies. Between the 7th and the end of the 12th month and from the 13th to the 60th month, the SIR for metachronous cancers were 1.54  and 1.37 , respectively .When considering individual cancer sites, breast  kidney/urinary tract , and hematological  featured significantly higher SIRs. The AER of metachronous breast cancers was 1.25 , while the AER per 1,000 PY for kidney/urinary tract  and for NHL  were not significant.The SIRs of metachronous breast cancer were found to be significantly higher in the 13–60 month interval  as well as more than 10 years after diagnosis . Similarly, the SIRs of metachronous kidney/urinary malignancies were also significant at 7–12 months from diagnosis .This population-based cohort study analyzed data for 9,726 CMM survivors and found that the risk for synchronous cancers increased in both sexes, irrespective of cancer site. An excess risk for synchronous kidney/urinary tract malignancies was detected in both sexes, while women also had an increased risk of synchronous breast cancer. Concerning metachronous cancers, male survivors had a higher risk of thyroid and prostate malignancies, while females had an increased risk of kidney/urinary tract cancer, non-Hodgkin’s lymphoma, and breast cancer. Females had also an overall increased risk of metachronous cancers in the first 5 years after CMM diagnosis.In western populations, the rising incidence of cancer and the increasing number of cancer survivors (which are frequently exposed to adjuvant carcinogenic therapies) has in turn increased the number of multiple primary malignancies , 16, 17.By applying SEER’s criteria in a cohort of 10,857 CMM survivors, Bradford et al. reported a significantly increased risk for non-melanomatous malignancies, particularly for breast, prostate, and non-Hodgkin’s lymphoma  .In this context, the sex-related incidence of multiple primaries is a critical issue that needs to be investigated further. A US study  showed a sex-independent increase of subsequent thyroid cancer, NHL, and chronic lymphocytic leukemia, with a higher risk of second kidney and prostate cancers in males, and a higher risk of breast cancer in females .To delve deeper into the effect of sex on cancer incidence, the current retrospective population-based study assessed data on multiple (synchronous and metachronous) primary malignancies in 9,726 CMM patients stratified by sex and followed up for 65,046 person-years.Our study shows that CMM patients carry a greater risk of multiple malignancies within 6 months of the index case. When patients develop a secondary primary cancer, these are categorized as either synchronous or metachronous primaries, depending on the length of time between the two cancers. However, the timeframe considered differs significantly according to SEER and IARC definitions. The SEER database considers any malignancy diagnosed within 2 months from the index cancer as synchronous, while the IARC considers any malignancy diagnosed within 6 months of the index cancer. This enormous nosology inconsistency results in significant differences in the epidemiological profile and the clinical interpretation of multiple primaries.From an epidemiological viewpoint, inconsistent definitions result in unreliable data interpretation and comparisons. From a clinical viewpoint, real-world experience shows that the two-month interval is too narrow to accomplish all diagnostic/staging cancer procedures, thus increasing the relative number of metachronous malignancies recorded. By expanding the timeframe of synchronous malignancies to 6 months, the IARC definition is more consistent with the natural cancers’ history, and also supports the clinical-biological rationale of secondary prevention strategies. Thus, the present study considered all multiple malignancies in different sites diagnosed within 6 months from the index CMM as synchronous.Consistent with the IARC conventional definition, synchronous cancers may plausibly be considered as side-effects of the diagnostic procedures triggered by the index cancer.This study documented that the overall risk of synchronous cancers was higher in both sexes, irrespective of the site. Both sexes featured an excess risk for synchronous kidney/urinary malignancies, while females showed a short-term increased “detection” of breast cancer.Although shared pathogenic risk factors cannot be excluded, the higher incidence of synchronous cancers could also be interpreted as an effect of increased access to the diagnostic imaging procedures that CMM patients undergo during the staging process of the index melanoma. These procedures may lead to the incidental discovery of asymptomatic/undiagnosed tumors in the first month after the index CMM , 19–22.Among CMM male survivors, the present findings did not feature any overall excess of metachronous cancer risk. Consistent with previous studies however, male patients did feature an excess risk of metachronous prostate and thyroid malignancies . This inIf we exclude skin melanomatous and non-melanomatous malignancies, females consistently featured an overall excess of metachronous cancer risk. When looking at single cancer sites, breast, kidney/urinary tract, and hematological (NHL) malignancies also showed a significant excess of risk. This is in line with a previously-reported two-way association between invasive CMM and NHL –29. The While the increased risk of synchronous breast cancer likely results from the increased medical surveillance triggered by the CMM staging protocols, the excess of breast cancer risk (which can be confirmed up until 10 years after the index-CMM) also suggests a potential pathogenetic involvement of BRCA2 and CDKN2A mutations –40. ThusAn important limitation of the present study is the lack of detailed information on environmental cancer risk factors, including socioeconomic status, genetic variants, and behavioral characteristics: collecting this level of patient-profiling data will be the next frontier of high-resolution cancer registration. Furthermore, only a fraction of the regional population was covered by the cancer registry in the first few years assessed in this study, which could affect generalizability. Nonetheless, the historical registration area yielded incidence estimates for various cancer sites that were proven to be comparable with those available for the whole population area.The present study found a significantly increased risk of synchronous and metachronous cancers in survivors of CMM, especially in terms of prostate, thyroid, or kidney and urinary tract cancers in men, and NHL, breast, or kidney and urinary tract cancers in women. Studies on multiple primary cancers may provide powerful insights into cancer etiology, including the cancer-promoting role of environmental and genetic risk factors.In CMM cancer patients, the results of this study provide the clinical rationale for developing secondary prevention strategies (including counseling), as well as decision-supporting tools. Moreover, in CMM survivors, the present results add clinically helpful information for sex-tailored surveillance protocols. Patients diagnosed with melanoma should therefore remain under surveillance, not only for recurrences but also for new primary melanomas and other cancers.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Veneto Oncological Institute’s Ethics Committee (No. 52/2016). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.AB, SG, MR, and SM: conceptualization. PF and MZ: methodology, software, and formal analysis. AB, EB, ST, LD, and CR: investigation. MZ: data curation. PF, ST, LD, CR, and SM: visualization. GG and MM: writing – original draft preparation. AB, SM, and MR: writing – editing supervision. All authors contributed to the article and approved the submitted version.This research has received “Current Research” funds from the Italian Ministry of Health to cover publication costs.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Despite similar incidence rates among Black and White women, breast cancer mortality rates are 40% higher among Black women. More than half of the racial difference in breast cancer mortality can be attributed to triple negative breast cancer (TNBC), an aggressive subtype of invasive breast cancer that disproportionately affects Black women. Recent research has implicated neighborhood conditions in the etiology of TNBC. This study investigated the relationship between cumulative neighborhood‐level exposures and TNBC risk.This single‐institution retrospective study was conducted on a cohort of 3316 breast cancer cases from New Castle County, Delaware (from 2012 to 2020), an area of the country with elevated TNBC rates. Cases were stratified into TNBC and “Non‐TNBC” diagnosis and geocoded by residential address. Neighborhood exposures included census tract‐level measures of unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental hazards. An overall cumulative risk score was calculated based on tract‐level exposures.Univariate analyses showed each tract‐level exposure was associated with greater TNBC odds. In multivariate analyses that controlled for patient‐level race and age, tract‐level exposures were not associated with TNBC odds. However, in a second multivariate model that included patient‐level variables and considered tract‐level risk factors as a cumulative exposure risk score, each one unit increase in cumulative exposure was significantly associated with a 10% increase in TNBC odds. Higher cumulative exposure risk scores were found in census tracts with relatively high proportions of Black residents.Cumulative exposure to neighborhood‐level risk factors that disproportionately affect Black communities was associated with greater TNBC risk. In a multilevel model that adjusted for patient age at diagnosis and race, a one‐unit increase in the cumulative exposure to census tract‐level measures of unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental hazards was associated with a 10% increase in the odds of triple negative breast cancer (TNBC) relative to other subtypes of invasive breast cancer. Higher cumulative exposure risk scores were found in census tracts with relatively high proportions of Black residents, which may help to explain racial disparities in breast cancer mortality. Breast cancer is the leading cause of both cancer incidence and mortality among Black women,Tumor biology may underpin Black‐White differences in breast cancer mortality not solely attributable to healthcare access. In particular, triple negative breast cancer (TNBC) is an aggressive subtype of invasive breast cancer that is twice as prevalent among Black women.Efforts to improve the prevention of TNBC have been stymied by a lack of progress in identifying risk factors that have been validated among Black women.Building on these efforts, we previously considered the role of residential segregation for TNBC incidence. Given the well‐documented and pervasive pattern of residential segregation in the USThe objective of this study was to extend this research and conduct a comprehensive assessment of contextual exposures that may help to explain the relationship between neighborhood factors and TNBC risk. We considered all known domains of contextual exposures that have been shown to impact patient‐level exposure to TNBC risk factors, including unhealthy alcohol use, metabolic dysfunction , and reproductive factors .22.1This single‐institution retrospective study was conducted with data from two patient cohorts from New Castle County, Delaware. New Castle County is the catchment area for the Helen F. Graham Cancer Center & Research Institute (HFGCCRI), which is a part of the Christiana Care Health System.The first patient cohort (“Breast Cancer Cohort”) was composed of breast cancer patients treated at the HFGCCRI. The HFGCCRI provides cancer care to more than 600 analytic breast cancer cases annually, including 85% of all breast cancer cases in New Castle County.The second patient cohort  was used to generate census tract‐level prevalence estimates of alcohol use disorder and diabetes in New Castle County. Patient records came from electronic health record (EHR) data from the Christiana Care Health System, which provides 88% of all non‐veteran adult acute care in New Castle County.This study was reviewed and approved by the Christiana Care Health System Institutional Review Board and was conducted in accordance with the US Common Rule. The need for consent was waived for both patient cohorts.2.2Patient measures for the Breast Cancer Cohort included age at diagnosis, race, residential address, and breast cancer subtype markers, which were abstracted from the HFGCCRI cancer registry. Breast cancer cases were classified as “TNBC” when ER, PR, and HER2 were all known negative; all other cases were classified as “Non‐TNBC.”A total of 19 census tract‐level measures were initially considered as proxies for each of the four domains of contextual exposures under consideration: unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental factors. These were reduced to a final measure for each domain based on both statistical and substantive considerations  prevalence and diabetes prevalence estimates generated from the General Hospital Cohort were selected as the proxy measures for contextual unhealthy alcohol use and metabolic dysfunction, respectively. Tract‐level “percentage of single female‐headed households with children” was selected as the proxy measure for breastfeeding initiation. This measure was obtained from the US Census Bureau's American Community Survey.Tract‐level Risk Screening Environmental Indicators (RSEI) cancer risk scoreFinally, an overall cumulative risk score was calculated based on methods used in other studies of cumulative exposures and health.2.3Spatial data management and statistical analyses were performed in the R Statistical Computing Environment.Two multilevel logistic regression models were used to examine the odds of TNBC (vs. Non‐TNBC). Both models adjusted for individual (level 1) variables and either included census tract (level 2) variables individually or summarized in the cumulative exposure risk score. Individual‐level variables included age at diagnosis and race. Tract‐level variables included AUD prevalence, diabetes prevalence, percentage single female‐headed households with children, and the RSEI cancer score. A census tract‐level random effect was tested for inclusion in both models to account for the clustering of patients within tracts.The spatial covariation of the cumulative exposure risk score and TNBC risk was visualized with a boxplot and a choropleth map. Superimposed on the choropleth map are New Castle County TNBC hot and cold spots that were previously detected and described elsewhere.The spatial covariation between the cumulative exposure risk score and census tract racial composition was visualized with a bivariate choropleth map,3Of the 3316 cases in the Breast Cancer Cohort, 453 (13.6%) were classified as TNBC and 2863 (86.3%) as Non‐TNBC .Estimating cumulative exposure to contextual risk factors is consistent with a This study has several important limitations. First, the single‐site, cross‐sectional design precludes drawing causal and generalizable inferences. Second, we did not have access to residential histories or patient‐level measures of relevant exposures , which may introduce potential exposure misclassification. Third, while our prior research suggests that the area‐level exposure estimates generated from EHR data are strongly correlated with small‐area estimates provided by the CDC,Beyond the replication and validation of our findings with similar methods, we wish to highlight the example and methodological recommendations offered by a few key researchers to further advance research that considers structural racism, residential segregation, and health disparities.In conclusion, the results from this study extend prior research that has documented a relationship between general neighborhood characteristics  and TNBC riskScott Siegel: Conceptualization (lead); project administration (lead); resources (lead); writing – original draft (lead). Madeline Brooks: Data curation (lead); formal analysis (lead); visualization (lead); writing – original draft ; writing – review and editing . Jesse Berman: Conceptualization ; methodology ; writing – review and editing (supporting). Shannon Lynch: Conceptualization ; writing – review and editing . Jennifer Sims‐Mourtada: Conceptualization ; data curation ; writing – review and editing (supporting). Zachary Schug: Conceptualization (supporting); writing – review and editing (supporting). Frank Curriero: Conceptualization ; methodology (lead); supervision ; writing – original draft (supporting); writing – review and editing .This project was supported by NIGMS (P20 GM103446) from the NIH and the State of Delaware (to S.D. Siegel).The authors declare that they have no competing interests.Figure S1–S2:Click here for additional data file.Table S1–S4:Click here for additional data file.


Awareness of screening procedures and illness warning signals is critical for expanding and implementing screening programs in society, which would improve the odds of early identification of breast cancer.This study aimed to evaluate the knowledge, awareness, attitudes, and practices related to breast cancer risk factors, signs, symptoms and methods of screening among female faculty and students at Hail University in the Kingdom of Saudi Arabia.A cross-sectional study was conducted from January 2021 through February 2021 in the Hail region of Saudi Arabia. A closed-ended questionnaire, which consisted of 37 questions, was distributed online (using a Google Forms link) in both English and Arabic languages. Data was collected from 425 female subjects who participated in the study.The study showed an overall knowledge level of 46.36% regarding breast cancer. Participants had average knowledge about risk factors, signs, and symptoms, whereas their awareness and practice of breast self-examination and screening methods were weak.The current study concluded that public awareness of breast cancer remains relatively low, and Saudi Arabia still needs several public awareness initiatives using mass media, such as television, the Internet, and radio, as well as social media. Special awareness programs should also be held in places where a large number of women can easily be reached, such as colleges, universities, and hospitals. Breast cancer is the most common type of cancer and the second highest cause of mortality in women . The forHistorically, the risk of breast cancer among the female population in Saudi Arabia was presumed to be insignificant; nonetheless, updated statistics contradicted that presumption indicating that breast cancer incidences among the Saudi female population is as significantly high as global statistics . MoreoveGender, age, family history, racial factors, radiation exposure, breast changes, early menarche, late menopause, prolonged null parity, overweight, diet, alcohol consumption, smoking, excessive estrogenic exposure, oral contraceptive use, stress, and anxiety account for only 25 to 40 percent of breast cancer cases .Awareness of screening procedures and illness warning signals is critical to advancing and implementing screening programs in society, which will improve the odds of early detection of breast cancer and thus improve quality of life . It is bThe early detection of breast cancer can be accomplished by establishing proactive screenings for women at risk, encouraging yearly mammograms and raising awareness . Women iWhen dealing with such a serious disease, it is essential to make sure the public is aware of its aggressiveness and the importance of early detection through known signs, as being proactive leads to increased chances of remission. They should also be aware of the various treatment options to combat breast cancer. Even though there is a guideline from the Ministry of Health, there is a dearth of literature on attitudes, knowledge levels and awareness regarding breast cancer in Saudi Arabia. Thus, the purpose of the present study was to evaluate the knowledge, awareness, attitudes, and practices related to breast cancer risk factors, signs and symptoms and methods of screening among female faculty and students at Hail University in the Kingdom of Saudi Arabia.A total of 425 female students and faculty of 18–50 years of age were recruited from the Hail region for this cross-sectional study that was conducted from January 2021 to February 2021. Furthermore, an informed consent was obtained from each participant before being interviewed through the use of an anonymous questionnaire to maintain the privacy of participants. The questionnaire was distributed among participants using a Google forms link in both Arabic and English format after obtaining the approval of the ethical committee of scientific research at Hail University. Also, all participants in this study were informed of their right to refuse to be part of the study at any time.The survey had 37 questions divided into seven sections: socio-demographic, sources of breast cancer information, knowledge of breast cancer risk factors, awareness and practice of breast self-examination (BSE), knowledge of signs and symptoms, knowledge of breast cancer screening methods, and perceptions about breast cancer treatment. To validate the questionnaire, a pilot study involving randomly selected 15 participants from the Hail region was carried out. As a result, the questionnaire did not need to be modified. Moreover, all pilot study participants were excluded from the study subjects.In the questionnaire, each correct response was awarded a score of one, whereas an incorrect answer or ‘don’t know’ response was given a zero. A total score for each participant was calculated by summing the number of correct answers. In addition, the level of knowledge was calculated by summing the scores of all knowledge questions. Out of a maximum score of 100, the results were divided into three main categories, Weak (25–49%), Average (50–74%) and Good (75% and above).Signs and symptoms were estimated as percentages using SPSS version 28 in addition to socio-demographic data, sources of information, awareness and practices, and understanding of risk factors. The association between these characteristics and total knowledge regarding breast cancer was determined using Chi-square analysis. Ethical approval (No. of research H-2021-223 dated 6/12/2021) was granted by the Research Ethics Committee (REC) at the University of Hail, Hail, Kingdom of Saudi Arabia.The study involved a total of 425 individuals. The most common source of information reported by all students was the awareness campaigns (54.1%), followed by media such as television and the Internet (38.6%). Most of the participants were students who had learned about breast cancer through the Ministry of Health’s university-based breast cancer education programme .In the current study, we observed poor overall knowledge about breast cancer in 46.22% of the participants. Participants had average knowledge about risk factors as well as signs and symptoms , whereas awareness and practice of BSE and screening methods were weak  .Most of the study participants (92.2%) did not know how to perform a BSE, and 56.7% were aware of the importance of BSE for the early detection of breast cancer. Many of the participants understood that obesity, lack of exercise, smoking and X-rays increase the risk of breast cancer. They also had average knowledge of the signs and symptoms of breast cancer; 79.8%, 63.5% and 53.4% of the participants, respectively, knew that changes in breast and nipple shape, size and colour, lumps in the breast and discharge from the nipple are among the signs and symptoms of breast cancer. Also, 96.9% of the participants knew that early detection of breast cancer leads to improve treatment outcomes .All participants responded to the perception of breast cancer treatment neutrally; 43.3% of the participants agreed with the statement that women could enjoy a good quality of life after treatment of breast cancer. There were neutral responses to the questions regarding the treatment process, the effectiveness of treatment for the young population, social stigma associated with treatment and loss of self-confidence post treatment  .This study was conducted to assess the awareness, knowledge, attitudes and perceptions of female students and staff at a university in the Hail region in Saudi Arabia. The results of this study indicate that educational campaigns were the main source of information about breast cancer (54.1%) compared to 43% from a study conducted at Najran University . This waWhen the level of knowledge was tested, the results showed a lack of information about the basic nature of the disease. Most of the participants did not know that breast cancer is essentially an abnormal growth; the majority did not know that breast cancer is a multifactorial disease and that early menarche is the least recognized risk factor, whereas smoking, obesity and physical inactivity are the most recognized risk factors, which is consistent with the studies conducted in Jeddah and Riyadh, respectively , 13. MorIn a study conducted in Riyadh , women hThe overall awareness and practice of BSE in this study was 36.37%. However, the decision to practice BSE is complex and influenced by several factors, such as age, occupation, knowledge about breast cancer and awareness of BSE. In the present study, 92.2% of the participants did not know how to perform a BSE despite knowing that it helps in the early detection of breast cancer. These results are consistent with previous studies that investigated breast cancer awareness, knowledge and BSE practices among women and college students in Saudi Arabia , 16, 17.Participants’ overall perception regarding breast cancer treatment was 46.36%. Approximately, 58.6% of women had little knowledge of breast cancer treatment and felt that it is a lengthy and painful procedure; however, 43.3% of participants understood that after treatment, women could enjoy a good quality of life. The BSE proves to be a simple and inexpensive method that plays an important role in the early detection of breast cancer. Failure to perform BSE is associated with delayed diagnosis and subsequently poor long-term survival rates.Finally, the survey was done through an online Questionnaire in lieu of personal interviews, which was the safest method to protect participants and researchers from spreading coronavirus. However, the online questionnaire had a few disadvantages that could not be avoided including the biased responses. In addition, the calculation of an accurate response rate was found to be difficult because it was not possible to track the number of subjects who received the questionnaire link and who responded accordingly.The present study concluded that public awareness of breast cancer remains relatively low, and Saudi Arabia still needs several public awareness initiatives using mass media, such as television, the Internet, and radio, as well as social media. Special awareness programs should also be held in places where a large number of women can easily be reached, such as colleges, universities, and hospitals. Women should be encouraged to talk about self-examination techniques with their friends at home in order to create awareness about BSE and encourage them to get mammograms at regular intervals. It is expected that this study may help in measuring the awareness and therefore planning an effective solution to enhance the awareness, which may lead to a reduction in mortality rate, improvement in quality of life and survival rate due to early detection of breast cancer.S1 File(PDF)Click here for additional data file.


This cross-sectional study was conducted on the associations between body composition, energy expenditure and caloric intake among 45 Nigerian breast cancer patients.t-test, paired t-test, Spearman correlation and linear regression using Statistical Package for the Social Sciences 23.0.Forty-five Nigerian breast cancer patients were measured and analysed for their body composition, energy expenditure and caloric intake. Statistical analyses included a chi-square test, Student’s r = 0.84). BMI and (FMI – fat mass in relation to height) were significantly correlated with the Harris–Benedict equation for energy expenditure , but they were not correlated significantly with the Karnofsky performance status. A paired t-test showed that caloric intake was significantly higher than total energy expenditure (p < 0.001). FFM was the best predictor of resting energy expenditure (REE).The body fat indices (body mass index (BMI), fat mass index (FMI), and body fats percentage) show that more than 50% of breast cancer patients were either overweight or obese. The Spearman correlation showed that fat-free mass (FFM) was the most strongly correlated with energy expenditure (In conclusion, FFM remains the best predictor of REE. High body mass and high caloric intake indicate the need for support from nutritional programmes. Breast cancer is the most common female malignancy, and it accounted for about 30% of all cancer diagnoses in women in 2019 ; and witIt is postulated that such environmental factors as diet and dietary intake, body composition e.g., body mass index (BMI), reduced metabolic rate and reduced physical activity are associated with breast cancer risk –9. Womenet al [VanItallie et al  suggesteOn the other hand, REE ) is known to be variable in cancer, and it is influenced by several factors including body composition . FurtherThis cross-sectional study was conducted at the University of Ilorin Teaching Hospital, Ilorin, from January 2018 to June 2018. Ethical approval was obtained from the Hospital’s Ethical Research Committee (ERC PAN/2015/12/1483). The study group comprised 45 histologically diagnosed female breast cancer patients who are >18 years old. Informed consent was obtained from each participant. The exclusion criteria included patients who were pregnant/breastfeeding, diagnosed with any other cancer, uncontrolled chronic comorbidities such as hypo/hyperthyroidism, on anti-hypertensive, on lipid-lowering drugs (HMG CoA reductase inhibitors (statins), bile acid sequestrants, nicotinic acid, fibric acids), on hormonal medications, with a Karnofsky performance status less than 50%, hospitalised or bedridden and patients with apparent nutritional impairment were excluded from this study. The patients were staged clinically according to the tumour, node and metastasis classification.The participants’ weight was measured in kilograms (kg) using a bathroom scale with participants wearing minimal clothing and no shoes. Height was measured in meters using a stadiometer with the participants standing erect, bare-footed and looking straight ahead.2. A proprietary algorithm for calculating body fat percentage (BF%) was used. The equation of Deurenberg et al [et al [2 as normal, 25–29.9 kg/m2 as overweight and >30 kg/m2 as obese, while the BF% was classified as <23% underfat, 23%–33% healthy, 33%–39% overweight, 40% and above as obese. The categorisation of the FMI was <5 as underweight, 5–9 as normal, 9–13 as overweight and >13 as obese, while FFMI was <18 skinny, 18–20 average, 20–22 good and >22 excellent.The BMI was derived as weight divided by height squared expressed as kg/mrg et al  was usedl [et al . The BMIThe REE predictive equation used in this study was the Harris–Benedict equation (HBE) . The TEETEE (from HBE) = REE (HBE) × AF × IF where AF = 1.3 and IF = 1.1The energy requirement was further estimated using the 25–30 kcal/kg/day formula.Because dietary intake is a key component of energy balance, dietary intake was assessed using the 48 hours of dietary recall. The type and amount of food that the patient consumed in the last 48 hours were asked by a trained nutritionist. All recalls were analysed with the Nigerian Food Composition table version 1.0 .The physical and functional ability were determined using the Karnofsky performance status scale.R-values were obtained to observe the strength and direction of the correlation. A paired T-test was performed to compare the mean difference between the 48 hours of dietary recall and TEE. Predictors of REE were evaluated using multiple linear regression analysis with REE as the dependent variable and body composition as the independent variables.Statistical analysis was performed using Statistical Package for the Social Sciences version 23.0. The descriptive data were given as mean ± SD. A correlation analysis was conducted to see the correlation between the parameters using the Spearman correlation analysis. The A total of 45 female breast cancer patients were included in this study. The mean patient age was 49.3 ± 14.8 years. The anthropometric characteristics and predicted energy requirement of the participants are presented in The multiple linear regression analysis of REE was used to relate REE to the body composition variables. FFM was the single best predictor of REE among female Nigerian breast cancer patients in this study as shown in This study was embarked on to observe the body composition as well as the energy expenditure and its predictors among Nigerian breast cancer patients which have not been reported before now. The mean age (49.3 ± 14.8 years) of the study sample indicates breast cancer continues to be a disease of the middle-aged and younger age group (<40 years) in this part of the world .et al [The association between body fat and cancer risk has been noted in different studies , 23. Traet al , though et al . Like inet al , 28, our2 increase in BMI for postmenopausal women [In lieu of the aforementioned risks, our breast cancer patients in this study may need a weight management plan to prevent some morbidities.Body fat distribution is a well-known predictor of cardiovascular disease , some caal women . In lieuThe REE of women with breast cancer was determined using the HBE, which has been shown to give a higher value of REE compared to indirect calorimetry . Howeveret al [Individuals with cancer experience signiﬁcant changes in body composition and REE with varying factors such as oral intake, physical activity, inflammatory processes, etc. REE is noted to be a result of metabolic activities within body tissues and organs especially organs like the liver and lungs. Elevated REE can potentially promote weight loss which can produce suboptimal clinical outcomes and increase morbidity and mortality risk. A relationship between REE, cachexia and survival were observed by Vazeille et al . They alThe role of energy intake on breast cancer risk has been studied in both experimental and observational studies. According to the International Agency for Research on Cancer, there is sufficient evidence from experimental animal studies indicating that limiting body-weight gain by caloric restriction has a preventive effect on cancer of the mammary gland . Energy et al [This present study also analysed the association between relative energy intake , and breast cancer with TEE and expected caloric estimate . Our result shows that there is a positive energy balance with energy expenditure, and a caloric estimate of 29 kcal/kg BW is sufficient to meet daily requirements. This positive energy balance is also statistically significant thus the excess body fats in our subjects may be a result of nutritional intake. Lope et al  in the Er = 0.84; p < 0.01), it has the weakest negative correlation with HBE/kg of BW, while FM has the strongest negative correlation with HBE/kg of BW . In healthy adults, a decrease in FFM explains approximately 60% of the decrease in REE observed with age [et al [et al [While excess fats are a breast cancer risk, especially in postmenopausal women, it should be noted that FM contributes to REE. FFM is the single best predictor of REE  as also with age . Howevere [et al  in colorl [et al  also repet al [In summary, our study shows that excess body fats among other variables in our breast cancer patients may explain their breast cancer status. The excess body fats may also be a result of increased caloric intake as derived from their 48 hours of dietary recall. This is the first study in Nigerian breast cancer patients that examine their body composition in association with energy expenditure as well as caloric intake. It thus brings to the fore the need for public health awareness of overweight and obesity and its association with the increasing numbers of breast cancer cases in Nigerian society. The increased consumption of fast foods in the country  also conet al  very recet al . It is pet al . This weet al .Based on the results of this study, we propose that a nutrition planning/weight control programme and physical workout plan should be part of the supportive care of Nigerian breast cancer patients.Several limitations should be considered when interpreting our findings. First, it is a single-centre, cross-sectional study. A multi-centre prospective study would need to be carried out to further confirm our findings. Second, the small number of our study group may increase bias. A larger multi-centre study group may be needed to validate our results. Third, due to cost and limited expertise, most patients did not carry out immunohistochemistry tests such as oestrogen, progesterone receptors, HER-2 and Ki-67, so they could not be reported. While indirect calorimetry is the gold standard for measuring REE, due to non-availability at our centre, logistics and its impractical nature for clinical use could not be assessed for this study. Indirect calorimetry is expensive, time-consuming and requires trained technicians, and to the best of our knowledge is not available in our country so the need to use a predictive equation. Body composition especially body fats could not be assessed by a bioelectrical impedance analyser or dual-energy X-ray absorptiometry due to non-availability at our centre, we, therefore, used anthropometric equations to derive our body composition. The self-reported dietary recall is subject to bias based on the extent of recall of diets taken during the period examined.This study equally has its strength. First, this is the first study to examine the body composition among breast cancer patients in Nigeria, most importantly looking at other indices other than BMI e.g., FMI and FFMI. Second, to our knowledge, this is the first study on energy expenditure in Nigerian breast cancer patients and its association with daily energy requirements  and excessive caloric intake. Third, we believe our results from this study may spur other research in the study of body composition, energy expenditure and well-being of the Nigerian breast cancer patient.In conclusion, the breast cancer patients in this study have excess fat. The caloric intake of breast cancer patients was significantly higher than their TEE. The importance of this is the need to set up a nutritional programme for breast cancer patients and monitor their calorie intake. This can help in the reduction of excess fats and help promote healthy lifestyles.No potential conflict of interest was reported by the authors.The researchers did not receive any external funding for this work.The data that support the findings of this study are available from the corresponding author, (OI), upon reasonable request.OI was involved in the design of the study, data analysis and writing of the first draft manuscript. TS was involved in data analysis, study coordination and correction of the draft. SO, HO and OA were involved in the design of the study, coordination and corrections of the draft manuscript. All authors read and approved the final manuscript.


The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women. Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer deaths in women worldwide . MammogrHowever, a “one-size-fits-all” approach for screening may have the risk of underdiagnosis of breast cancer, especially in women with dense breasts. The sensitivity of mammography depends on breast density, which refers to the amount of fibroglandular tissue compared with that of fatty tissue in the breast ,5,6,7. AIn recent decades, several risk prediction models for breast cancer have been developed ,16,17,18With the development of artificial intelligence (AI), studies have applied a deep learning algorithm to breast cancer risk assessment using information on mammograms ,23,24,25Given the differences in mammographic and clinical characteristics between Asian and Western women, developing a risk prediction model for Asian women is needed. The purpose of this study was to develop a deep learning model based on mammograms alone for predicting breast cancer risk in Asian women.This retrospective study was approved by the Institutional Review Board of our institution (IRB No. 2020-02-033-003) and the requirement for written informed consent was waived. We collected consecutive digital mammograms between November 2012 and March 2022 at two tertiary hospitals. We also obtained data on patients with breast cancer from electronic medical records and pathology reports. The cancer group included mammograms before cancer diagnosis in patients with breast cancer diagnosed at each hospital. We excluded mammograms within 1 year before breast cancer diagnosis and women with a prior history of breast cancer in the same breast. However, women with a history of contralateral breast cancer were included. The negative group included all screening mammograms in women who had at least 5-year follow-up. Only mammograms with negative or benign results (Breast Imaging and Reporting Data System [BI-RADS] category 1 or 2) were included based on the mammography report in both groups. If each woman had multiple mammographic examinations during the study period, each examination was independently included as the index mammography. In addition, 17 examinations were excluded due to technical issues in image preprocessing for deep learning.Finally, our study included 287 examinations in 153 women  in the cancer group and 736 examinations in 447 women  in the negative group.Full-field digital mammography was acquired using two different machines  at two hospitals. Standard mammography included craniocaudal and mediolateral oblique views of each breast. The final assessment category and breast density based on the BI-RADS system were determined based on the mammography report . We clasAll mammograms were saved as digital imaging and communications in medicine (DICOM) images and uploaded to the cloud system for data processing. In the cancer group, only images of the breast where cancer occurred were uploaded for each examination. In the negative group, all four full-field digital mammograms were uploaded. All images were labeled as “cancer” or “negative” and “dense” or “nondense.” After image preprocessing, all mammograms were randomly assigned to either training, validation, or test sets. We split the dataset by women, so each woman only contributed mammograms to one set. A flowchart illustrating the construction and distribution of whole datasets is shown in In this study, we prepared the two separate datasets to investigate whether prior mammograms could predict a risk of breast cancer: the whole dataset, which included all mammograms in women with dense and nondense breasts; and the dense-only dataset, which included mammograms only in women with dense breasts. The deep convolutional neural networks were, respectively, trained using the whole and dense-only datasets. In addition, deep learning models were trained based on both the examinations (comprising two images per breast) and the images.We used commercially available software  to train the two deep learning models—that is, image-level and examination-level models. Data augmentation options, which included the image rotation at 90° and the image flipping process for horizontal and vertical directions, were randomly applied as follows: hue at –0.1 to 0.1, brightness at –0.12 to 0.12, contrast at 0.6 to 1.4, and flipping process for horizontal and vertical directions.All training processes were performed on a single workstation computer with the Windows operating system  and using the NVIDIA Quadro RTX 8000 with 48 GB of memory .The deep learning models separately evaluated each image. However, individual image results were combined as the outcome of a mammographic examination. The trained model was analyzed by mammographic images and examinations to evaluate the performance of the deep learning model. For analysis based on mammographic images, the result was considered as cancer if an image predicted a developing breast cancer. For analysis based on mammographic examinations, the result was considered as cancer if at least one image predicted a developing breast cancer. Otherwise, the result was considered as negative. Consequently, four deep learning models were obtained: an image-level model with the whole dataset, an image-level model with the dense-only dataset, an examination-level model with the whole dataset, and an examination-level model with the dense-only dataset. For each scenario, the model performance was evaluated with the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC). A two-sided 95% confidence interval (CI) was obtained for the sensitivity, specificity, and AUC.A total of 1023 mammographic examinations in 600 women  were included, and we randomly assigned to training (701 examinations in 416 women), validation (160 examinations in 87 women), and test (162 examinations in 97 women) sets. The dense-only dataset consisted of 737 examinations in 437 women with dense breasts. This dataset was divided into training (507 examinations in 301 women), validation (113 examinations in 66 women), and test (117 examinations in 70 women) sets .We evaluated the performance of mammography-based deep learning models for predicting breast cancer risk. The performance measures of the risk prediction models on test sets are summarized in p = 0.17; dense-only, p = 0.16). The ROC curves of the four models are shown in The image-level model showed AUCs of 0.71 (95% CI: 0.67–0.75) and 0.75 (95% CI: 0.70–0.79) in the whole and dense-only datasets, respectively. The examination-level model showed AUCs of 0.66 (95% CI: 0.58–0.73) and 0.67 (95% CI: 0.58–0.75) in the whole and dense-only datasets, respectively. No statistically significant difference was observed between the image-level and examination-level models  and the dense-only dataset . In terms of the precision and F1 score, the examination-level model was superior to the image-level model in the whole dataset  and the dense-only dataset .In this study, we developed a deep learning model using full-field digital mammograms to predict breast cancer risk in Asian women with a high proportion of dense breasts. The image-level and examination-level deep learning models were created and trained with whole and dense-only datasets, respectively. On a test set, the mammography-based risk models showed a reasonable performance in predicting the risk of breast cancer .The AUC values of our model were comparable with those of existing image-based risk models ,23,24. YThe majority of image-based risk models were developed on predominantly white populations, and thus have limitations in predicting risk for Asian women ,24,25. ISimulation modeling studies have shown that screening strategies should be personalized based on a woman’s age, breast density, and other risk factors ,31. IndiIn this study, both image-level and examination-level deep learning models performed better on the dense-only dataset compared with the whole dataset in all performance measures except sensitivity. Our study populations had a high percentage of dense breast tissue. In the whole dataset, the proportions of dense breasts were 72.3% (507 of 701) and 70.6% (113 of 160) in the training and validation sets, respectively.Our study had several limitations. First, this was a retrospective study with a relatively small sample size. Because we used mammography data obtained from only two institutions, the generalizability of our results may be limited. If a deep learning model is trained with a large dataset collected from multiple institutions, the model performance could be further improved. Second, negative or benign results from the mammography report might include missed or subtle cancers. However, we did not include mammograms within 1 year before breast cancer diagnosis in the cancer group. Lastly, our deep learning models were developed based on the patient’s mammographic images alone. The predictive accuracy of deep learning risk models can be further improved by incorporating the genetic test result and clinical risk factors such as age, menopausal status, family history of breast cancer, prior benign biopsy, or use of hormone therapy. However, the mammography-based deep learning model has advantages because it does not require knowledge of those risk factors required by traditional risk models.In conclusion, we developed a deep learning risk model based on the mammography data from Asian women. Our deep learning model demonstrated comparable predictive accuracy in breast cancer risk assessment compared with existing risk models. Further studies are required to validate our model across institutions in larger datasets. The mammography-based risk model has the potential to support effective risk-based screening in clinical practice.


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.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.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 ; and of light areas and bright areas on Cirrus .The mammogram risk scores were correlated within twin pairs and with each other  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.The online version contains supplementary material available at 10.1186/s13058-023-01733-1. Mammographic density refers to the areas of a two-dimensional mammogram appears to be white. This is conventionally defined based on pixel threshold that can differentiate light or bright regions from dark regions, and can be measured using the semi-automated CUMULUS software, developed by Yaffe et al. in the 1990s .We found that two additional mammographic density measures defined by successively higher pixel brightness thresholds and called Altocumulus and Cirrocumulus, respectively Fig. , when siAs well as mammographic density, the breast parenchyma could possess other mammographic patterns, such as textures, that predict breast cancer risk . For exaThe different mammogram risk scores above are correlated with one another to varying degrees , 10, 11.We have consistently found that when the different mammogram risk scores are fitted together, their breast cancer risk gradients do not necessarily attenuate towards the null to the same extent as would be expected if their associations with one another were due solely to confounding , 10. In There are also potentially causal relationships between Cumulus and the other risk scores. Whether the associations between risk scores are causal, and in which direction is unknown; they could also be due to non-causal effects such as confounding and conditioning on colliders . To addrTwin and family studies have shown that the density-based risk scores are correlated between relatives; i.e. they are familial . This meIn this study, we applied ICE FALCON to try to understand if the causal relationships between the texture-based mammogram risk score (Cirrus) and three non-overlapping density-related mammogram risk scores , in order to provide evidence for which risk scores are more relevant to breast cancer aetiology.We used data from the Australian Mammographic Density Twins and Sisters Study , which iDemographic information, anthropometric measurements, menstrual and reproductive history, lifestyle factors, and personal and family history of breast cancer were collected via telephone-administered questionnaires between 2004 and 2008. Zygosity was determined from genome-wide association data . As therMammograms were retrieved from BreastScreen Australia services (80%), clinics (5%), and from participants themselves (15%) and digitised using the Lumysis 85 scanner at the Australian Mammographic Density Research Facility. For each woman, only the craniocaudal-view mammogram from the right breast taken closest to the survey was used in this study.The dense areas were measured using a computer-assisted semi-automated thresholding technique and the CUMULUS software based on a sliding scale ranging from 0 to 4095 pixels. Four observers were trained to measure mammographic density independently, as previously described [A conventional pixel threshold was first used to identify dense areas with grey levels appearing at least mammographically light . Similarly, the pixel brightness threshold was then increased to identify the denser areas . The pixel threshold was then further increased to identify the densest areas . The reproductivity was assessed by conducting the measurements in sets of 100 mammograms, and 10% of samples in each set were repeated. The intraclass correlation coefficients were 0.98, 0.99, and 0.93 for Cumulus, Altocumulus, and Cirrocumulus, respectively. A total of 200 images were measured for Cumulus, Altocumulus, and Cirrocumulus with the correlations between readers being 0.95, 0.89, and 0.85, respectively. Details of these three density measurements can be found elsewhere , 17.We created two new non-overlapping measures: light areas, which subtracted Altocumulus from Cumulus, and bright areas, which subtracted Cirrocumulus from Altocumulus. Along with a measure of the brightest areas (Cirrocumulus), their relationships, in terms of relative brightness, are shown in Fig. Cirrus is an agnostic algorithm developed using deep learning techniques applied to 20 textural features extracted from 46,158 analogue, craniocaudal-view, mammograms [In this study, we conducted analyses of Cirrus and the three spatially independent density measures including light, bright, and brightest areas (Cirrocumulus). Table 2, All mammographic measures were first transformed using a Box–Cox power transformation  to have Given that age at mammography is negatively associated with the mammographic density measures being studied as putative risk factors for breast cancer, and that breast cancer risk increases with age, all the measures were adjusted for age at mammography. This adjustment explained 8–11% of the variances in the studied measures, except for the light areas, for which the proportion of the variance explained was 2%. The variance explained by other breast cancer risk factors combined, including age at menarche, menopausal status, BMI, ever being pregnant, number of live births, benign breast disease history, and breast cancer family history, was between 4 and 7%  = 1. These standardised residuals are the mammogram risk scores used in the subsequent analyses. Correlations between these risk scores, within twin pair and within a person, respectively, were estimated using Pearson’s correlation coefficient.The correlations between the risk scores were decomposed into different sources, including confounding and causal effects originated from various pathways using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method . ICE FALY variable and another as the predictor variable X, and the Y value of a twin was regressed against the X variable of herself and/or of her co-twin . Second, the twin’s outcome variable was regressed on her co-twin’s predictor variable to estimate the regression coefficient βco-twin (Model 2). Third, the twin’s outcome variable was regressed on both her own and her co-twin’s predictor variables to estimate the conditional regression coefficients β′self and β′co-twin, respectively (Model 3). The use of the prime on the conditional regression coefficient estimates indicates that the Model 3 regression coefficients can be interpreted as the change in outcome for change in a given predictor while keeping the other predictor constant, which is not the same interpretation for the corresponding unconditional regression coefficients of Models 1 and 2.Three models were fitted to the twin pair data. First, a twin’s outcome variable was regressed on her own predictor variable to estimate the regression coefficient βco-twin would be different from zero, β′co-twin would be closer to zero than βco-twin, and β′self would not be different from βself. If there is familial confounding between the predictor and the outcome, β′self and β′co-twin would both be away from their corresponding coefficients βself and βco-twin to a similar extent. If there is a combination of familial confounding and causal effects, the results would be the combinations of the two scenarios. According to Wright’s path tracing rules [If the predictor has a causal effect on the outcome, ng rules , the protor; see . The cauP < 0.05 was considered to be nominally statistically significant.To investigate causal pathways between two risk scores that are not through other risk scores, we used the standardised residuals of the predictor and the outcome after adjusting for the third risk score in addition to age at mammography. The results from the analyses were used to produce a summary causal diagram. Causal relationship analyses were also conducted by the level of breast density to check whether the causal relationships differ by density levels. The sample was divided into two subgroups according to the median of 30.5% for Cumulus per cent mammographic density, with each group including 140 complete twin pairs. ICE FALCON analyses were conducted within each subgroup; see Supplemental material for more details. All the analyses were conducted using the R package [P < 0.05).Table Table P = 0.02) from βself = 0.802 (P = 10–161) in Model 1 to β′self = 0.770 (P = 10–117) in Model 3 and a decrease of 86% (P = 10–34) from βco-twin = 0.513 (P = 10–34) in Model 2 to β′co-twin = 0.070 (P = 0.01) in Model 3. These results were consistent with the light areas having a causal effect on the bright areas that accounted for 89% of their association, with marginal evidence for familial confounding.With the light areas as the predictor and the bright areas as the outcome, there was a decrease of 4% (P = 10–5) from βself = 0.770 (P = 10–210) in Model 1 to β′self = 0.727 (P = 10–174) in Model 3 and a decrease of 64% (P = 10–33) from βco-twin = 0.404 (P = 10–19) in Model 2 to β′co-twin = 0.144 (P = 10–9) in Model 3. These results were consistent with the bright areas having a causal effect on the light areas that accounted for 58% of their association and familial confounding that accounted for 42%.With the bright areas as the predictor and the light areas as the outcome, there was a decrease of 6%  from βself = 0.352 (P = 10–17) in Model 1 to β′self = 0.328 (P = 10–16) after adjusting for co-twin’s light areas in Model 3. There was a decrease of 51% (P = 10–7) from βco-twin = 0.176 (P = 10–5) in Model 2 to β′co-twin = 0.087 (P = 0.02) after adjusting for the twin’s light areas in Model 3. These results are consistent with there being a combination of familial confounding that accounted for 37% of the association between the two risk scores as well as the light areas having a causal effect on Cirrus that accounted for 63% of their association.With Cirrus as the outcome and the light areas as the predictor, there was a decrease of 7% (βself = 0.285 (P = 10–15) in Model 1 to β′self = 0.303 (P = 10–20) in Model 3 (P = 0.02). There was also an increase in the co-twin’s coefficient from βco-twin = 0.091 (P = 0.006) in Model 2 to β′co-twin = 0.135 (P = 10–5) in Model 3 (P = 0.003). These results were consistent with the findings above that the association was due to a combination of familial confounding and the light areas having a causal effect on Cirrus.We then reversed the predictor and outcome roles by assigning the light areas to be the outcome and Cirrus as the predictor. There was an increase from P = 0.002) from βself = 0.398 (P = 10–27) in Model 1 to β′self = 0.367 (P = 10–21) in Model 3. There was a decrease of 35% (P = 10–4) from βco-twin = 0.215 (P = 10–8) in Model 2 to β′co-twin = 0.139 (P = 10–4) in Model 3. These results were consistent with there being a combination of familial confounding that accounted for 72% of the association between the two risk scores, and the bright areas having a causal effect on Cirrus that accounted for 28% of their association.With Cirrus as the outcome and the bright areas as the predictor, there was a decrease of 8% (βself was not significantly different from β′self after adjusting for co-twin’s Cirrus (P = 0.5), and a similar statement applies to the lack of a difference between βco-twin and β′co-twin (P = 0.9). These results were not consistent with Cirrus having a causal effect on the bright areas.When we reversed the predictor and outcome roles by assigning the bright areas to be the outcome and Cirrus as the predictor, P = 10–3) from βself = 0.389 (P = 10–23) in Model 1 to β′self = 0.360 (P = 10–19) in Model 3 (P = 0.004), while there was a decrease of 40% (P = 10–5) from βco-twin = 0.191 (P = 10–6) in Model 2 to β′co-twin = 0.114 (P = 0.002) in Model 3 (P = 10–5). These results were consistent with Cirrus having a causal effect on the brightest areas (Cirrocumulus), which accounted for 34% of their association, as well as there being familial confounding which accounted for 64%.With brightest areas (Cirrocumulus) as the outcome and Cirrus as the predictor, there was a decrease of 7% (βself and β′self (P = 0.5), while there was an increase from βco-twin = 0.138 (P = 10–5) in Model 2 to β′co-twin = 0.166 (P = 10–7) in Model 3 that was not nominally significant (P = 0.08). These results were not consistent with the brightest areas (Cirrocumulus) having a causal effect on Cirrus.When we reversed the predictor and outcome roles by assigning brightest areas (Cirrocumulus) to be the predictor and Cirrus as the outcome, there was no difference between P = 0.008) from βself = 0.470 (P = 10–38) in Model 1 to β′self = 0.424 (P = 10–25) in Model 3 and a decrease of 64% (P = 10–13) from βco-twin = 0.284 (P = 10–12) in Model 2 to β′co-twin = 0.103 (P = 0.005) in Model 3. These results were consistent with the light areas having a causal effect on the brightest areas (Cirrocumulus) that accounted for 55% of their association and familial confounding which accounted for 45%.With the light areas as the predictor and the brightest areas (Cirrocumulus) as the outcome, there was a decrease of 10% (βself = 0.347 (P = 10–23) in Model 1 to 0.395 (P = 10–33) in Model 3 (P = 10–5) and an increase from βco-twin = 0.113 (P = 10–4) in Model 2 to β′co-twin = 0.221 (P = 10–13) in Model 3 (P = 10–9). These results were not consistent with the brightest areas (Cirrocumulus) having a causal effect on the light areas.With the brightest areas (Cirrocumulus) as the predictor and the light areas as the outcome, there was an increase from P = 0.06) from βself = 0.680 (P = 10–159) in Model 1 to β′self = 0.653 (P = 10–109) in Model 3 (P = 0.06), while there was a decrease of 85% (P = 10–25) from βco-twin = 0.378 (P = 10–22) in Model 2 to β′co-twin = 0.058 (P = 0.07) in Model 3 of 85% (P = 10–25). These results were consistent with the bright areas having a causal effect on the brightest areas (Cirrocumulus) that accounted for 85% of their association and familial confounding which accounted for 15%.With the bright areas as the predictor and the brightest areas (Cirrocumulus) as the outcome, there was a marginally significant decrease of 4% (βself and β′self (P = 0.21), while there was an increase in βco-twin = 0.136 (P = 10–4) in Model 2 to β′co-twin = 0.202 (P = 10–14) in Model 3 (P = 0.02). These results were not consistent with the brightest areas (Cirrocumulus) having a causal effect on the bright areas.With the brightest areas (Cirrocumulus) as the predictor and the bright areas as the outcome, there was no difference between The above results were consistent with the existence of causal pathways from both the light areas and the bright areas to the brightest areas (Cirrocumulus), and to Cirrus, and from Cirrus to the brightest areas (Cirrocumulus).βself in Model 1 and β′self in Model 3 (P = 0.2), while there was a decrease from βco-twin = 0.198 (P = 10–6) in Model 2 to β′co-twin = 0.051 (P = 0.2) in Model 3 by 74% (P = 10–10). These results were consistent with a causal effect from the light areas to the brightest areas (Cirrocumulus) that not through Cirrus, which accounted for 64% of their association and with familial confounding that accounted for 36%.With the light areas adjusted for Cirrus as the predictor and the brightest areas (Cirrocumulus) adjusted for Cirrus as the outcome, there was no difference between βself increased from 0.287 (P = 10–17) in Model 1 to 0.339 (P = 10–23) after adjusting for co-twin’s brightest areas (Cirrocumulus) in Model 3 (P = 10–5), βco-twin increased from 0.046 (P = 0.2) in Model 2 to β′co-twin = 0.165 (P = 10–7) after adjusting for the twin’s brightest areas (Cirrocumulus) in Model 3 (P = 10–11). These results were consistent with a combination of a causal effect from the light areas to the brightest areas (Cirrocumulus) not through Cirrus and familial confounding.With brightest areas (Cirrocumulus) adjusted for Cirrus as the predictor and light areas adjusted for Cirrus as the outcome, βself decreased, but not significantly (P = 0.3), from 0.608 (P = 10–90) in Model 1 to β′self = 0.597 (P = 10–75) after adjusting for co-twin’s bright areas in Model 3, βco-twin decreased from 0.268 (P = 10–9) in Model 2 to the null (P = 0.3) after adjusting for co-twin’s bright areas in Model 3 by 87% (P = 10–13). These results were consistent with the bright areas having a causal effect on the brightest areas (Cirrocumulus) not through Cirrus, which accounted for 92% of their association and the familial confounding accounting for 8% of the association.With the bright areas adjusted for Cirrus as the predictor and the brightest areas (Cirrocumulus) adjusted for Cirrus as the outcome, βself did not change significantly (P = 0.3) after adjusting for co-twin’s brightest areas (Cirrocumulus), βco-twin increased from 0.08 (P = 0.186) in Model 2 to β′co-twin 0.153 (P = 10–6) after adjusting for the twin’s brightest areas (Cirrocumulus) in Model 3 (P = 0.001). These results are consistent with a combination of a causal effect, from the bright areas to the brightest areas (Cirrocumulus) not through Cirrus, and familial confounding.With the brightest areas (Cirrocumulus) adjusted for Cirrus as the predictor and the bright areas adjusted for Cirrus as the outcome, From the subgroup analyses according to the level of per cent mammographic density based on Cumulus measure, similar causal evidence was found between subgroups for the causal relationships between most pairs of mammographic risk scores, which supported that the causal relationships were unlikely to depend on breast density level  areas a woman has on her mammogram might cause her to also have more of the brightest (highly dense) areas, currently the strongest density measure associated with breast cancer risk –4, 7–9. Our findings as encapsulated by Fig. Note that when the light areas risk score was replaced by Cumulus ), and the bright areas risk score was replaced by Altocumulus (i.e. the areas including bright and brightest areas (Cirrocumulus)) in the above models, the results were similar . This isSubtracting the causal pathways involving Cirrus, the causal effect of bright areas on brightest areas (Cirrocumulus) remained strong, while the causal effect from light areas on brightest areas (Cirrocumulus) was much weaker (i.e. effect size of 0.3 versus 0.6). This is consistent with the observations that the greater the brightness of the dense region the stronger its association with breast cancer risk .For Cirrus, the effect size of its causal associations with density measures is around 0.1, regardless of the direction and the threshold for defining dense areas. Dense area-based measures all have positive associations with Cirrus. Of them, bright areas and light areas are both the causes; while brightest areas (Cirrocumulus) are causally affected by Cirrus, which is less aetiologically important than light areas and bright areas, with both of their total effect sizes larger than 0.2. The weak causal relationships between Cirrus and other mammogram risk scores are reasonable. Apart from Cirrus capturing textural information , which is different from what can be captured using the threshold-based measures (i.e. pixel counts above or below an intensity level), Cirrus also uses the information from the whole breast, rather than the local information measured by threshold-based measures. These results add grounded evidence to previous association-based speculations that texture-based mammographic measures, at least Cirrus, could be an independent and intrinsic risk factor for breast cancer , 12.The findings of this study could inform biological research in trying to understand why conventional density is associated with breast cancer risk. Mammographic density is defined by different levels of pixel brightness thought to represent different types of breast tissue based on their differential X-ray attenuation. Fat tissue appears to be radiologically dark, while fibroglandular tissue appears to be light or bright. As mammograms are two-dimensional representations of tissue composition, each pixel in the image reflects a combination of fibroglandular and fat tissue. Malignant breast tissue, however, is developed from fibroglandular tissue and appears radiologically brighter than normal fibroglandular tissue. Therefore, the brightest areas (Cirrocumulus) contain more pixels and are closer to the radiological appearance of malignant breast tissue than the light areas and bright areas. This might explain in part its typically stronger association with screen-detected, young-age-at-diagnosis, and contralateral breast cancer risk, than other mammographic density measures, as observed in the previous studies , 10. It Additionally, light areas contain a greater amount of less dense tissue (dispersed fibroglandular tissue and possibly fat tissue), compared with bright areas. The difference in tissue composition might explain the observed bidirectional causal relationships between light areas and bright areas, with the causal effect size of 0.7 on bright areas caused by light areas being stronger than the reverse causal effect size of 0.4. Therefore, less dense tissue, including dispersed fibroglandular tissue or fat tissue, could have causal effects on dense fibroglandular tissue.The study has also demonstrated the value of ICE FALCON in decomposing associations between intercorrelated disease biomarkers into pathways. These causal associations could be identified and quantified even though there was also familial confounding, something that by definition cannot be considered if we used Mendelian randomisation based on assuming genetic variants for these mammogram risk scores were true instrumental variable. The same approach could be applied to other biomarkers and diseases, such as lipids and cardiovascular diseases.One strength of our study is that when conducting the ICE FALCON analyses, which can infer causation and its direction even if there is a familial confounding between mammogram risk scores, we used monozygotic twin pairs. This maximised the within-pair correlations in the risk scores, and they were close to 0.5. This also maximised the potential existence of non-genetic factors shared by twins and hence the amount of familial confounding.Considering that there could be bidirectional causal relationships between two traits and the need to make robust inference, we conducted ICE FALCON analyses by switching the roles of predictors and outcome for each pair. Causal inference was only made when the evidence from the two rounds of analyses was consistent or does not contradict each other. We also extensively checked the influence of other common covariates, on the mammographic measures included in this study, that have established or possible associations with breast cancer risk or mammographic density measures according to the previous publications.One limitation of the study is that the epidemiological data were not collected strictly contemporaneous with the participants’ mammography episodes. But we have previously shown that mammographic density adjusted for age and/or BMI tracks strongly with time, at least over 8 years , 32. AlsTo the best of our knowledge, we are the first to investigate causal relationships between mammographic measures that predict breast cancer risk. The previous studies on mammographic density have primarily focused on its risk prediction capabilities for breast cancer , 35 withIn a mammograme, the less dense (light and bright) areas could have a positive causal effect on the densest (brightest) areas which eventually become cancers themselves. There could also be another pathway to cancer evident through textural features not related to density per se, i.e. Cirrus, which also has a causal effect on the brightest areas (Cirrocumulus). Given our previous findings, the brightest areas (Cirrocumulus) are more aetiologically important than lighter areas for screen-detected, young-age-at-diagnosis, and contralateral breast cancer, while light areas have an independent causal effect from the brightest areas for interval breast cancer. In addition to the causal effects from density measures, mammographic measures based on specific textural features, like Cirrus, contain aetiologically independent information for breast cancer risk , 10.Additional file 1: Figure S1. The diagram of ICE FALCON methodology. Figure S2. The diagram of the relationships between risk scores analysed using ICE FALCON. Table S1. Linear mixed-effects models of mammographic measures and covariates. Table S2. The relationships between Cirrus and mammographic density measures analysed by using the ICE FALCON method. Table S3. The relationships between Cirrus and mammographic density measures for percent mammographic density ≤ 30.5% analysed by using the ICE FALCON method. Table S4. The relationships between Cirrus and mammographic density measures for percent mammographic density > 30.5% analysed by using the ICE FALCON method.