A comparison of risk factors for interval and screen-detected breast cancers: a case-case analysis of the Breast Cancer Now Generations Cohort, UK

A comparison of risk factors for interval and screen-detected breast cancers: a case-case analysis of the Breast Cancer Now Generations Cohort, UK | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A comparison of risk factors for interval and screen-detected breast cancers: a case-case analysis of the Breast Cancer Now Generations Cohort, UK Louise Johns, Martina Brayley, Reuben Frost, Penny Coulson, Micheal Jones, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7940334/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Interval breast cancers (IBC), diagnosed between routine screening rounds, tend to have a worse prognosis than screen-detected breast cancers (SDBC). Identifying risk factors for IBCs is critical for improving early detection and developing risk-stratified screening strategies to reduce their incidence. We evaluated associations of breast density, reproductive, hormonal, lifestyle and medical factors with IBC compared to SDBC in a large UK cohort. Methods Analyses included 1,940 women diagnosed with breast cancer after enrolment in the Breast Cancer Now Generations Study, a prospective UK cohort linked to the National Health Service Breast Screening Programme. Pre-diagnostic risk factors were collected at enrolment, and breast density was estimated from pre-diagnostic mammograms in a subset of 1,191 cases. Screening histories were used to identify 1,185 SDBCs and 755 IBCs. Logistic regression models estimated odds ratios (OR) and 95% confidence intervals (CI) for associations between risk factors and IBC, adjusting for breast density and tumour characteristics. Results Higher breast density was associated with later age at menarche, nulliparity, breastfeeding, history of benign breast disease (BBD), alcohol consumption, lower body mass index (BMI) at recruitment and at age 20, and current use of menopausal hormone therapy (MHT). In a mutually adjusted model, IBC risk was lower in overweight women (OR (95% CI ) = 0.74 (0.58–0.93) vs normal weight), and higher with high breast density (2.13 (1.44–3.17) for Q4 vs Q1), later age at menopause (1.60 (1.02–2.50) 55 + vs < 50), current MHT use (1.41 (1.04–1.91) vs never users), history of BBD (1.36 (1.11–1.68)), being underweight at age 20 (1.65 (1.14–2.38) vs normal weight), family history of breast cancer (1.26 (1.00–1.58)) and ever using oral contraceptives (1.25 (0.93–1.69) vs never). These risk factor associations were independent of tumour characteristics. Conclusions Breast density and several risk factors independently increase the likelihood of IBC, highlighting opportunities for tailored screening strategies to enhance early detection and reduce IBCs incidence. breast cancer interval cancers risk factors mammographic density Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The ability to detect breast cancers during routine mammographic screening (screen-detected breast cancers, SDBC), rather than in the interval between scheduled screening episodes following a negative mammogram (interval breast cancers, IBC), is a key measure for monitoring the impact of population mammographic screening programs like the UK National Health Service Breast Screening Programme (NHSBSP) [ 1 ]. This distinction is important because SDBCs are typically diagnosed at an earlier, more treatable stage, while IBCs are often diagnosed symptomatically, presenting with more aggressive features such as higher grade, larger size, and lymph nodal involvement [ 2 ]. Improving detection methods to reduce IBC incidence and identifying associated risk factors could inform risk-stratified screening strategies and provide important etiological insights into whether certain risk factors are likely to affect tumour development or detection. These insights can help refine our understanding of breast cancer aetiology and may support the development of more tailored screening approaches, such as adjusted screening intervals or supplemental imaging ( e.g . ultrasound, MRI) for women at higher risk of IBCs. Lower body mass index (BMI) and menopausal hormone therapy (MHT) use have been consistently found to be associated with increased chances of being diagnosed with IBC compared to SDBC [ 2 – 7 ]. A recent population-based cohort study in Sweden also showed strong evidence for family history of breast cancer being associated with increased risk of IBC, particularly if the affected relative also had IBC [ 5 ]. Weaker or less consistent evidence exists for other risk factors, such as reproductive factors, smoking, and benign breast disease. Many of these factors also influence breast density [ 8 ], a well-established independent risk factor for breast cancer and a major determinant of mammographic sensitivity [ 9 ]. For example, MHT increases breast density, while higher BMI reduces it, thereby affecting the sensitivity of mammographic detection [ 3 , 5 , 10 ]. Breast density may therefore confound associations between certain risk factors and mode of detection, potentially introducing detection bias if not properly accounted for. Adjusting for breast density is therefore essential to better distinguish associations that may reflect underlying etiological pathways from those more likely explained by differential detectability at screening. Moreover, several risk factors predispose to tumours with distinct characteristics, such as oestrogen receptor (ER) status, histological subtype and grade of differentiation [ 11 – 13 ] which may themselves affect the likelihood of detection at screening [ 2 , 6 ]. Therefore, tumour characteristics could mediate or modify the relationship between risk factors and mode of detection. For example, some risk factors may predispose to faster-growing or less radiologically visible tumour types, increasing the chances of interval diagnosis. Accounting for tumour characteristics can therefore help clarify potential mechanisms underlying risk factor associations with mode of detection. Most previous studies examining IBC risk factors have not adequately adjusted for breast density or tumour characteristics in their analyses. many also relied on self-reported detection status without access to information on screening histories, increasing the risk of misclassification of IBCs and SDBCs [ 3 , 4 ]. The Breast Cancer Now Study (BGS; formerly known as the Breakthrough Generations Study), a large UK-based prospective cohort, offers detailed risk factor data, linkage to NHSBSP screening histories and breast tumour pathology information. This enabled us to examine associations between breast cancer risk factors and the likelihood of IBC versus SDBC as determined from screening histories, accounting for breast density and tumour characteristics. Our analyses therefore aim to clarify the complex interplay between risk factors, detection processes, and tumour presentation. METHODS Study population The BGS enrolled 113,757 women between 2003 and 2021, collecting baseline data on breast cancer risk factors via questionnaires, as previously described [ 14 ]. Linkage to NHSBSP screening histories was available for 71,482 participants resident in England, of whom 65,121 (91%) attended at least one routine screen between 1988 and 2016. Among 3,039 women diagnosed with breast cancer (invasive or ductal carcinoma in situ (DCIS)) post-recruitment, 2,073 had screening records suitable for determining detection mode, resulting in 1,185 SDBCs, 755 IBCs, and 173 unclassified cases. Unclassified cases were typically diagnosed outside the screening data period. Figure 1 shows the selection of BGS participants eligible for analyses. Information on derivation of mode of detection based on screening histories is detailed in Supplementary Methods) . Breast cancer tumour characteristics and screening data Breast cancers were confirmed via self-reports, relative notifications, NHS data linkages, or medical records, classified as invasive (ICD C50) or DCIS (ICD D051, D059, D05Z). Tumour characteristics, including morphology, grade, size, lymph node status, and ER/PR/HER2 status, were obtained from cancer registries and pathology reports. NHSBSP linkage provided screening histories for approximately 350,000 screening episodes between 1988–2016. For each episode, the date of invitation, attendance and assessment and the outcome of each stage were returned. Summarised biopsy results, final action, status at episode closure and screening centre involved were supplied. For cases diagnosed after the latest NHSBSP data, a screen-detected flag from cancer registries was used to classify detection mode. ( Supplementary Information; Supplementary Table 1 ). Questionnaire risk factors and mammographic density Baseline recruitment questionnaires captured risk factor data including age at menarche, oral contraceptive (OC) use, parity, age at first birth, number of children, duration of breastfeeding, menopausal status, age at menopause, menopausal hormone therapy (MHT) use, alcohol units per week, body mass index (BMI) calculated from self-reported weight and heigh at age 20 and at recruitment, leisure time physical activity (in metabolic equivalents, METS, hours per week), personal history of benign breast disease, and family history of breast cancer in first degree relatives. BMI was categorized according to the WHO/UK NICE guidelines as underweight (< 18.5), normal weight (18.5–24.9), overweight (25–29.9), obese (≥ 30), not known or pregnant. For BMI at age 20, overweight and obese categories were combined due to a small number of participants. Mammographic density was measured using Cumulus software on pre-diagnostic mammograms (preferably medio-lateral oblique views) for 1,191 cases (401 IBCs, 790 SDBCs), typically from the screening round before diagnosis. Density was averaged across both breasts (details in Supplementary Information ). Statistical analysis Descriptive statistics were used to summarise questionnaire-based risk factors, breast density and tumour characteristics by mode of detection (IBC or SDBC). The distribution of percent mammographic density across mode of detection and risk factor categories is shown graphically using violin plots. Differences in mean percent density were tested using linear regression models with density as the outcome variable, both unadjusted and adjusted by age at mammogram. Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between risk factors, breast density and tumour characteristics for IBC vs SDBC. Covariates for inclusion in the multivariate models were selecte d a priori to include established risk factors for breast cancer with data available. Indicator variables for parity and menopausal status and interaction terms were included in the models to estimate ORs for risk factors defined only among parous (age at first birth, breastfeeding duration) or post-menopausal women (age at menopause, MHT status). Missing data on explanatory variables in the models was accounted by including indicator terms, allowing all participants to contribute to the analysis. We also ran sensitivity analyses restricted to women with observed density. To account for differential times between recruitment and breast cancer diagnosis, risk factor analyses were adjusted by age at diagnosis, time from recruitment to breast cancer diagnosis, and year of diagnosis. We fitted four main logistic models comparing IBC with SDBC: (1) risk factors only (except breast density); (2) risk factors plus breast density, (3) risk factors plus tumour characteristics, (4) all variables. Models including breast density were further adjusted by time from mammographic density to breast cancer diagnosis. All statistical tests were two-sided, with an alpha-level of 0.05. Statistical analyses were performed in R version 4.4.1 (2024-06-14 ucrt) ( www.rproject.org . 18). Code for statistical analyses is available at GitHub. RESULTS Women with IBC and SDBC had similar mean ages at recruitment (~ 55 years), age at screening mammogram (~ 58 years) and age at breast cancer diagnosis (~ 62 years) (Table 1 ). The mean (SD) time between recruitment to breast cancer diagnosis was longer for IBC (7.19 (3.69 years) than SDBC (6.42 (3.58) years) since, by definition, IBCs were diagnosed after a screening mammogram. The mean (SD) time between recruitment and density measure was much shorter since we selected pre-diagnostic mammograms to measure density, and it was also longer for IBC (1.67 (2.98) years) than SDBC (1.16 (2.95) years). IBCs exhibited more aggressive characteristics than SDBCs, with higher grade, node positivity, larger size, and ER negativity being statistically significant after adjusting for all characteristics (Table 1 ). The mean (SD) percent density was 29% (19%) for women diagnosed with IBC and 24% (17%) for women with SDBC (Fig. 2 ; Supplementary Table 2 ). After adjusting for age at mammogram, women with IBC had, on average, 5.56% higher percent density than women with SDBC (Fig. 2 ; Supplementary Table 2 ). Respectively, women with lobular and mixed histologic types had 4.64% and 6.01% higher breast density than women with ductal tumours (Fig. 2 ; Supplementary Table 2 ). None of the other tumour characteristics were significantly associated with breast density after adjusting for age at mammogram. In contrast, percent mammographic breast density was significantly associated with several risk factors evaluated at recruitment after adjusting by age at mammogram (Fig. 3 ; Supplementary Table 3 ). Specifically, breast density was significantly higher in participants with older age at menarche, being nulliparous, having breastfed for longer periods, currently taking MHT, having a personal history of benign breast disease (BBD), having a lower BMI at age 20 or at recruitment, physical activity, and drinking more alcohol (Fig. 3 ; Supplementary Table 3 ). The largest differences in density, after adjustment for age at mammogram, were observed for current use of MHT at recruitment (9.65% higher density than never users); obesity at recruitment (17.10% lower breast density than normal weight women); overweight/obesity at age 20 (11.07% lower density than normal weight); and BBD (7.36% higher density for presence vs absence). Ever OC users and post-menopausal women had higher density and post-menopausal women lower density, but these associations were substantially attenuated and not significant after adjusting for age at mammogram. Age at first birth, age at menopause, family history of breast cancer and smoking status were not significantly associated with breast density in unadjusted or age-adjusted analyses. Figure 4 and Supplementary Table 4 show estimates for the association between breast density and risk factors with IBC vs SDBC in mutually adjusted analyses. After adjusting for other risk factors, risk of IBC increased with increasing breast density (OR (95%CI) = 1.39 (0.95–2.03) for Q2 vs Q1, 1.66 (1.14–2.44) for Q3 vs Q1 and 2.13 (1.44–3.17) for Q4 vs Q1). After adjusting for breast density, we identified significant associations between IBC and late age at menopause (1.60 (1.02–2.50) for 55 + vs < 50); current use of MHT (1.41 (1.04–1.91) for current use vs never use), and personal history of BBD (1.36 (1.11–1.68)). Although not nominally significant, women with family history of breast cancer (1.26 (1.00-1.58)) and a history of OC use (1.25 (0.93–1.69) current vs never) were also at increased odds of IBC. Both overweight and obese women were less likely to have a diagnosis of IBC than women with a normal weight. However, the association was stronger for overweight than obese women, and it was attenuated after adjustment for breast density. After density adjustment, ORs (95% CIs) changed from 0.69 (0.55–0.86) to 0.74 (0.58–0.93) for overweight women, and from 0.78 (0.59–1.04) to 0.88 (0.65–1.19) for obese women (Fig. 4 ; Supplementary Table 4) . In contrast, neither overweight nor obesity at age 20 were significantly associated with IBC, but women who were underweight at that age were more likely to be diagnosed with IBC compared to normal weight women (1.65 (1.14–2.38)). This association was independent of other risk factors, including their BMI at recruitment. Sensitivity analyses restricted to women with available mammographic density data yield similar results than the main analyses including an indicator term for missingness. The risk factor associations with IBC remained after accounting for tumour characteristics ( Supplementary Table 4) . DISCUSSION In this study of women undergoing routine mammography screening in England, we found that several established risk factors for breast cancer were associated with breast density, and that both density and risk factors independently influenced the likelihood of being diagnosed with an IBC. Specifically, late age at menopause, current use of MHT, and a history of BBD were each associated with higher likelihood of IBC, while being overweight at recruitment was associated with reduced risk. These findings build on previous research by simultaneously accounting for breast density and tumour characteristics- two important determinants of detectability- and by using linked screening histories rather than self-reports, thus minimizing detection and misclassification biases. Clinically, these results may help identify women at higher risk of IBCs who could benefit from more tailored screening approaches to improve outcomes. Our findings confirmed the established association between the increased risk of being diagnosed with IBC for women with higher breast density [ 2 , 5 ], as well as the tendency for IBCs to present with more aggressive features, including higher grade, nodal involvement, larger size, and ER negativity [ 2 ]. Consistent with previous reports [ 14 , 15 ], we also found that several established breast cancer risk factors measured at recruitment were significantly associated with percent breast density, independently of the age at mammogram. For most women in our study, density was measured using the most recent pre-diagnostic mammogram, taken within a few years of the recruitment questionnaire (mean interval: 1.7 years for IBCs ,1.2 years for SDBCs). While this time gap may have attenuated associations due to temporal changes in density or risk factors, the intervals were relatively short, and results remained robust. Women who experience menopause at age 55 or older were 1.6 times more likely to be diagnosed with an IBC than those with menopause before age 50. Although later age at menopause was also associated with higher breast density, the association remained after adjustment. Similarly, we showed that current MHT users are 1.4 times more likely to be diagnosed with an IBC than never users, independently of density. Our findings are relevant because although the association between these menopausal factors and IBC are well established [ 2 , 10 ], most reports speculated that they might be explained by their associations with a higher mammographic breast density [ 15 ]. Our results suggest that other factors not fully captured by density, such as mammographic textural features, could influence cancer detection during mammography screening. Measurement error in the breast density estimates could also result in residual confounding. Furthermore, MHT use has also been linked to lobular histology and lower-grade tumours [ 11 , 13 ], both of which may affect detectability on mammography. In our data, lobular cancers were more frequent in women with dense breasts, but they occurred at similar frequencies in IBC or SDBC cases. Tumour grade was not associated with density, but IBCs were more likely to be of higher grade, consistent with faster growing being missed at screening. Thus, while grade could partially mediate the MHT association with IBC, this association remained after adjusting for tumour grade and other tumour characteristics. Further studies are needed to clarify the mechanisms underlying the associations of late age at menopause and MHT use with IBC. The association of IBC with a personal history of BBD is also consistent with previous reports [ 3 , 16 ]. We showed that although women with BBD have higher density, it does not fully explain the association with IBC. The association remained independent of tumour characteristics and was stronger after adjustment for these variables. As in other studies, BBD was self-reported and included a wide range of conditions such as hyperplasia, cysts, mastitis and others, which may have district mammographic features and associations with risk of IBC. For instance, calcifications have been found to be more frequent in proliferative disease with atypia, while masses are more common in lower-risk, non-proliferative BBDs [ 17 ]. These findings underscore the need for future studies to evaluate specific types of BBD and their relationship with IBC risk, as well as imaging or pathological features that may mediate this association. Consistent with previous studies, higher BMI was associated with lower breast density and a reduced risk of IBCs [ 2 , 4 , 7 , 14 – 16 ]. In our analyses, the inverse association between overweight and IBC was substantially attenuated – although still statistically significant- after adjusting for breast density, while the association with obesity was weaker and not statistically significant after adjustment. These findings suggest that lower density among women with higher BMI may partly mediate the observed lower risk of IBC, likely through improved tumour visibility on mammograms. These findings underscore the potential for detection bias in studies of BMI and breast cancer risk conducted in screened, predominantly post-menopausal populations, and could help explain discrepancies between observational studies—which typically show a direct association between BMI and postmenopausal breast cancer[ 18 ] —and Mendelian randomization studies, which have reported inverse associations [ 19 ]. Interestingly, BMI at age 20 was not associated with density in our study, but underweight women at that age appeared more likely to be diagnosed with IBC later in life- a novel finding not previously reported[ 3 ] that needs confirmation. Together, these findings highlight the importance of accounting for breast density and screening history when assessing associations between BMI and breast cancer risk in populations undergoing mammographic screening [ 20 ]. Family history and OC use showed weaker, statistically non-significant, associations, consistent with mixed finding in the literature. However, a recent large population-based cohort in Sweden showed that the association with IBC is stronger for those women whose relatives also had IBCs (1.18-fold increase for any breast cancer history compared to 1.70-fold increase in risk for a history of IBC), particularly when relatives were diagnosed at a younger age [ 5 ]. This suggests that more detailed family history could better identify women at higher risk of IBC. Our findings of ever OC users being more likely to be diagnosed with IBC suggest that OC use might have longer-lasting effects that are specifically relevant to IBC, which may be missed in studies examining overall breast cancer risk. The association with IBC was independent of tumour characteristics, indicating that IBC risk is not fully explained by OC use predisposing to tumour subtypes that are harder to detect on screening mammography. Previous studies on the potential associations between IBC and age at menarche, parity, age at first birth, smoking status, and alcohol consumption have been inconsistent [ 3 – 5 , 7 , 16 ] and were not confirmed in our study. The strengths of our study include access to detailed screening histories, comprehensive risk factor data, breast density measurements, and detailed tumour characteristics within a prospective cohort study. These features allowed us to evaluate the impact of breast density as a confounder factor in studies of risk factors for IBC in screened populations. In addition, our analyses were restricted to women who attended routine screening, ruling out attendance bias as a confounder. This restriction ensures that our findings are directly relevant to populations undergoing mammographic screening and more accurately reflect relationships between risk factors, density, and IBC. Furthermore, we evaluated the role of tumour characteristics in detection and whether they could be potential explanations of why some risk factors predispose to tumours more likely to be diagnosed between screening rounds. This study has several limitations. Risk factors were measured at recruitment rather than at the specific time of the relevant screening, and breast density measurements were primarily taken from the screening mammogram during the cycle preceding cancer detection. This could introduce differences by mode of detection, as the time interval between the density measurement and diagnosis was, by definition, shorter for IBCs compared to SDBCs ( Supplementary Fig. 1 ). To address these potential biases, we adjusted analyses for the time between density measurement and diagnosis, as well as the time between risk factor assessment and diagnosis. Although breast density was more often missing among women with IBC (47%) than SDBCs (33%), missingness was unrelated to the risk factors examined. We included a separate category for missing density in multivariable models and confirmed our results with sensitivity analyses restricted to women with density measurements, suggesting that missing data did not affect our conclusions drawn from the results. We conducted case-case analyses rather than prospective cohort analyses to estimate hazard ratios for risk factors separately for IBC and SDBC due to the lack of density data at the cohort level. While this approach allows for more detailed examination of risk factors for IBC, any differences in HR would be driven by the examined case-case associations. Additionally, the study lacked access to radiological reviews, which would have enabled differentiation between false-negative, occult or minimal-sign IBCs (missed cancers) and true interval cancers. Occult IBCs are strongly associated with high breast density, where tumours are more challenging to detect radiologically, whereas the density association is weaker for true interval cancers or false-negatives [ 6 ]. Using proxy measures for true IBC (e.g. those in women with low breast density) or incorporating radiological reviews in future studies could provide further insights into the interplay between density, detection biases, and tumour biology. Conclusions Our findings, together with previous literature, provide strong evidence that breast density and several risk factors independently predict IBC risk. Understanding how these factors influence tumour detectability could support development of tailored breast screening strategies, such as adjusted intervals or supplementary imaging, to improve early detection. Further research should investigate whether these associations reflect detection issues/challenges or biological mechanisms/aetiology and explore specific BBD subtypes and detailed family history to enhance risk stratification and screening efficiency. Abbreviations BBD Benign Breast Disease BMI Body Mass Index DCIS Ductal Carcinoma In–Situ ER Oestrogen Receptor BGS Breast Cancer Now Generations Study HER2 Human Epidermal Growth Factor Receptor 2 IBC Interval Breast Cancer ICD International Classification of Diseases MET Metabolic Equivalent of Task MHT Menopausal Hormone Therapy NHS National Health Service NHSBSP National Health Service Breast Screening Programme OC Oral Contraceptives OR Odds Ratio PR Progesterone Receptor SDBC Screen–Detected Breast Cancer SHIM Screening Histories Information Manager TC Tumour Characteristics WHO World Health Organisation Declarations Ethics approval and consent to participate The study was undertaken with informed consent and ethics approval from the Southeast Multi-Centre Research Ethics Committee [MREC 03/01/014)] Consent for publication: Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to the sensitive nature of the data. Information on data access can be found in the Generations Study website https://thegenerationsstudy.co.uk/. Competing interests: The authors declare that they have no competing interests Funding: This work was funded by Breast Cancer Now and The Institute of Cancer Research. Authors' contributions LJ and MB analysed and interpreted the data with equal contributions. RF and MJ contributed to the interpretation of data and writing of the manuscript. LJ and MGC wrote the first draft of the manuscript. ABG and MGC obtained funding and supervised the work. All authors read and approved the final manuscript. Acknowledgements The authors acknowledge the contributions of Anthony Swerdlow and Minouk Shoemaker for the generation of data included in these analyses. 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Oxford University Press; 2015. pp. 390–3. https://doi.org/10.1093/aje/kwv086 Tables Table 1 Demographic and tumour characteristics of the study population Characteristic Interval N = 755 Screen-Detected N = 1,185 p-value 1 Age at recruitment, mean (SD) 55.13 (7.11) 55.19 (6.93) Age at mammogram, mean (SD) 58.56 (5.39) 58.49 (5.37) Unknown, N 354 395 Age at diagnosis, mean (SD) 62.32 (6.80) 61.61 (6.38) 0.306 Time from recruitment to mammogram (years), mean (SD) 2.54 (2.28) 2.32 (2.16) Unknown, N 354 395 Time from recruitment to diagnosis (years), mean (SD) 7.19 (3.69) 6.42 (3.58) 0.021 Year of diagnosis, N (%) 2004–2008 96 (13) 182 (15) 2009–2013 255 (34) 496 (42) 0.256 2014–2018 404 (54) 507 (43) 0.972 Invasive status, N (%) DCIS 47 (6.2) 261 (22) Invasive 708 (94) 924 (78) 0.080 Grade, N (%) 1 88 (12) 250 (21) 2 335 (44) 477 (40) 0.006 3 264 (35) 187 (16) < 0.001 Unknown, N 68 271 Morphology type, N (%) Ductal 549 (73) 698 (59) Lobular 95 (13) 119 (10) 0.833 Mixed 25 (3.3) 43 (3.6) 0.777 Other 39 (5.2) 64 (5.4) 0.724 Unknown, N 47 261 Node status, N (%) Negative 304 (40) 601 (51) Positive 234 (31) 194 (16) < 0.001 Unknown, N 217 390 Tumour size (mm), N (%) < 21 336 (45) 656 (55) 21–50 225 (30) 162 (14) < 0.001 50+ 31 (4.1) 15 (1.3) 0.003 Unknown, N 163 352 ER status, N (%) Positive 525 (70) 791 (67) Negative 137 (18) 87 (7.3) 0.007 Unknown, N 93 307 PR status, N (%) Positive 283 (37) 375 (32) Negative 175 (23) 154 (13) 0.313 Unknown, N 297 656 HER2 status, N (%) Positive 102 (14) 92 (7.8) Negative 524 (69) 725 (61) 0.943 Unknown, N 129 368 1 p-value from logistic regression multivariate model of all listed variables Additional Declarations No competing interests reported. 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10:06:33","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92596,"visible":true,"origin":"","legend":"","description":"","filename":"5c377a2e5e2d441eaaf7b4d93308c1951structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/66639aa6b2491edb99f501a9.xml"},{"id":97125901,"identity":"28229ee3-9891-49f4-bf93-387905aa2261","added_by":"auto","created_at":"2025-12-01 08:22:10","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100880,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/7d00c3a8de8d9d27ffb282f2.html"},{"id":97125881,"identity":"e13b60c8-45a4-4953-9167-f13a2dada6d2","added_by":"auto","created_at":"2025-12-01 08:22:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":217425,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart showing the selection of Generations Study breast cancer cases eligible for analyses\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/53b15010233911d2d90569b2.png"},{"id":97125883,"identity":"8ce09150-42e3-4f8f-8a09-978b4cb37fd2","added_by":"auto","created_at":"2025-12-01 08:22:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":202310,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of mammographic percent density by mode of detection and tumour characteristics. Details in\u003cstrong\u003eSupplementary Table 2, \u003c/strong\u003eincluding test for differences adjusted and unadjusted by age at mammogram.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/f4dbb91903a81a94b277a671.png"},{"id":97125885,"identity":"45c3fd5f-1db0-49ac-99ec-ec70eae67c05","added_by":"auto","created_at":"2025-12-01 08:22:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212890,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of mammographic percent density by risk factors for breast cancer. Details and age adjusted analyses are shown in\u003cstrong\u003e Supplementary Table 3, \u003c/strong\u003eincluding test for differences adjusted and unadjusted by age at mammogram.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/cd56d83d9d961aa81939dae5.png"},{"id":97125882,"identity":"1d120bc0-f977-45c7-b53e-85d3eb23a2a3","added_by":"auto","created_at":"2025-12-01 08:22:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80010,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing the association between risk factors with IBC compared to SDBC, measured by the odds ratio (OR) and 95% confidence intervals (CI) from models unadjusted (model 1) and adjusted (model 2) by percent mammographic density. Panel A shows reproductive and hormonal factors and Panel B lifestyle and medical history factors. Parameter estimates are shown in \u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/fda41d66d774189ba915aa84.png"},{"id":103050529,"identity":"34b5db66-86a8-41f1-932b-48d1aef85958","added_by":"auto","created_at":"2026-02-20 07:50:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1438169,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/ea862fbe-b60d-46b7-81b1-5e65ea1f55d3.pdf"},{"id":97125891,"identity":"e20d09d9-1771-4617-919f-9de9b4637b98","added_by":"auto","created_at":"2025-12-01 08:22:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":270348,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinfov8.docx","url":"https://assets-eu.researchsquare.com/files/rs-7940334/v1/2d2aa3be385a52f2308072e4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A comparison of risk factors for interval and screen-detected breast cancers: a case-case analysis of the Breast Cancer Now Generations Cohort, UK","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe ability to detect breast cancers during routine mammographic screening (screen-detected breast cancers, SDBC), rather than in the interval between scheduled screening episodes following a negative mammogram (interval breast cancers, IBC), is a key measure for monitoring the impact of population mammographic screening programs like the UK National Health Service Breast Screening Programme (NHSBSP) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This distinction is important because SDBCs are typically diagnosed at an earlier, more treatable stage, while IBCs are often diagnosed symptomatically, presenting with more aggressive features such as higher grade, larger size, and lymph nodal involvement [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Improving detection methods to reduce IBC incidence and identifying associated risk factors could inform risk-stratified screening strategies and provide important etiological insights into whether certain risk factors are likely to affect tumour development or detection. These insights can help refine our understanding of breast cancer aetiology and may support the development of more tailored screening approaches, such as adjusted screening intervals or supplemental imaging (\u003cem\u003ee.g\u003c/em\u003e. ultrasound, MRI) for women at higher risk of IBCs.\u003c/p\u003e\u003cp\u003eLower body mass index (BMI) and menopausal hormone therapy (MHT) use have been consistently found to be associated with increased chances of being diagnosed with IBC compared to SDBC [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A recent population-based cohort study in Sweden also showed strong evidence for family history of breast cancer being associated with increased risk of IBC, particularly if the affected relative also had IBC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Weaker or less consistent evidence exists for other risk factors, such as reproductive factors, smoking, and benign breast disease.\u003c/p\u003e\u003cp\u003eMany of these factors also influence breast density [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], a well-established independent risk factor for breast cancer and a major determinant of mammographic sensitivity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, MHT increases breast density, while higher BMI reduces it, thereby affecting the sensitivity of mammographic detection [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Breast density may therefore confound associations between certain risk factors and mode of detection, potentially introducing detection bias if not properly accounted for. Adjusting for breast density is therefore essential to better distinguish associations that may reflect underlying etiological pathways from those more likely explained by differential detectability at screening.\u003c/p\u003e\u003cp\u003eMoreover, several risk factors predispose to tumours with distinct characteristics, such as oestrogen receptor (ER) status, histological subtype and grade of differentiation [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] which may themselves affect the likelihood of detection at screening [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, tumour characteristics could mediate or modify the relationship between risk factors and mode of detection. For example, some risk factors may predispose to faster-growing or less radiologically visible tumour types, increasing the chances of interval diagnosis. Accounting for tumour characteristics can therefore help clarify potential mechanisms underlying risk factor associations with mode of detection.\u003c/p\u003e\u003cp\u003eMost previous studies examining IBC risk factors have not adequately adjusted for breast density or tumour characteristics in their analyses. many also relied on self-reported detection status without access to information on screening histories, increasing the risk of misclassification of IBCs and SDBCs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Breast Cancer Now Study (BGS; formerly known as the Breakthrough Generations Study), a large UK-based prospective cohort, offers detailed risk factor data, linkage to NHSBSP screening histories and breast tumour pathology information. This enabled us to examine associations between breast cancer risk factors and the likelihood of IBC versus SDBC as determined from screening histories, accounting for breast density and tumour characteristics. Our analyses therefore aim to clarify the complex interplay between risk factors, detection processes, and tumour presentation.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThe BGS enrolled 113,757 women between 2003 and 2021, collecting baseline data on breast cancer risk factors via questionnaires, as previously described [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Linkage to NHSBSP screening histories was available for 71,482 participants resident in England, of whom 65,121 (91%) attended at least one routine screen between 1988 and 2016. Among 3,039 women diagnosed with breast cancer (invasive or ductal carcinoma in situ (DCIS)) post-recruitment, 2,073 had screening records suitable for determining detection mode, resulting in 1,185 SDBCs, 755 IBCs, and 173 unclassified cases. Unclassified cases were typically diagnosed outside the screening data period. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the selection of BGS participants eligible for analyses. Information on derivation of mode of detection based on screening histories is detailed in \u003cb\u003eSupplementary Methods)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBreast cancer tumour characteristics and screening data\u003c/h3\u003e\n\u003cp\u003eBreast cancers were confirmed via self-reports, relative notifications, NHS data linkages, or medical records, classified as invasive (ICD C50) or DCIS (ICD D051, D059, D05Z). Tumour characteristics, including morphology, grade, size, lymph node status, and ER/PR/HER2 status, were obtained from cancer registries and pathology reports. NHSBSP linkage provided screening histories for approximately 350,000 screening episodes between 1988\u0026ndash;2016. For each episode, the date of invitation, attendance and assessment and the outcome of each stage were returned. Summarised biopsy results, final action, status at episode closure and screening centre involved were supplied. For cases diagnosed after the latest NHSBSP data, a screen-detected flag from cancer registries was used to classify detection mode. (\u003cb\u003eSupplementary Information; Supplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eQuestionnaire risk factors and mammographic density\u003c/h3\u003e\n\u003cp\u003eBaseline recruitment questionnaires captured risk factor data including age at menarche, oral contraceptive (OC) use, parity, age at first birth, number of children, duration of breastfeeding, menopausal status, age at menopause, menopausal hormone therapy (MHT) use, alcohol units per week, body mass index (BMI) calculated from self-reported weight and heigh at age 20 and at recruitment, leisure time physical activity (in metabolic equivalents, METS, hours per week), personal history of benign breast disease, and family history of breast cancer in first degree relatives. BMI was categorized according to the WHO/UK NICE guidelines as underweight (\u0026lt;\u0026thinsp;18.5), normal weight (18.5\u0026ndash;24.9), overweight (25\u0026ndash;29.9), obese (\u0026ge;\u0026thinsp;30), not known or pregnant. For BMI at age 20, overweight and obese categories were combined due to a small number of participants.\u003c/p\u003e\u003cp\u003eMammographic density was measured using Cumulus software on pre-diagnostic mammograms (preferably medio-lateral oblique views) for 1,191 cases (401 IBCs, 790 SDBCs), typically from the screening round before diagnosis. Density was averaged across both breasts (details in \u003cb\u003eSupplementary Information\u003c/b\u003e).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to summarise questionnaire-based risk factors, breast density and tumour characteristics by mode of detection (IBC or SDBC). The distribution of percent mammographic density across mode of detection and risk factor categories is shown graphically using violin plots. Differences in mean percent density were tested using linear regression models with density as the outcome variable, both unadjusted and adjusted by age at mammogram.\u003c/p\u003e\u003cp\u003eLogistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between risk factors, breast density and tumour characteristics for IBC \u003cem\u003evs\u003c/em\u003e SDBC. Covariates for inclusion in the multivariate models were selecte\u003cem\u003ed a priori\u003c/em\u003e to include established risk factors for breast cancer with data available. Indicator variables for parity and menopausal status and interaction terms were included in the models to estimate ORs for risk factors defined only among parous (age at first birth, breastfeeding duration) or post-menopausal women (age at menopause, MHT status). Missing data on explanatory variables in the models was accounted by including indicator terms, allowing all participants to contribute to the analysis. We also ran sensitivity analyses restricted to women with observed density. To account for differential times between recruitment and breast cancer diagnosis, risk factor analyses were adjusted by age at diagnosis, time from recruitment to breast cancer diagnosis, and year of diagnosis. We fitted four main logistic models comparing IBC with SDBC: (1) risk factors only (except breast density); (2) risk factors plus breast density, (3) risk factors plus tumour characteristics, (4) all variables. Models including breast density were further adjusted by time from mammographic density to breast cancer diagnosis. All statistical tests were two-sided, with an alpha-level of 0.05.\u003c/p\u003e\u003cp\u003eStatistical analyses were performed in R version 4.4.1 (2024-06-14 ucrt) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.rproject.org\u003c/span\u003e\u003cspan address=\"http://www.rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 18). Code for statistical analyses is available at GitHub.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWomen with IBC and SDBC had similar mean ages at recruitment (~\u0026thinsp;55 years), age at screening mammogram (~\u0026thinsp;58 years) and age at breast cancer diagnosis (~\u0026thinsp;62 years) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean (SD) time between recruitment to breast cancer diagnosis was longer for IBC (7.19 (3.69 years) than SDBC (6.42 (3.58) years) since, by definition, IBCs were diagnosed after a screening mammogram. The mean (SD) time between recruitment and density measure was much shorter since we selected pre-diagnostic mammograms to measure density, and it was also longer for IBC (1.67 (2.98) years) than SDBC (1.16 (2.95) years). IBCs exhibited more aggressive characteristics than SDBCs, with higher grade, node positivity, larger size, and ER negativity being statistically significant after adjusting for all characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe mean (SD) percent density was 29% (19%) for women diagnosed with IBC and 24% (17%) for women with SDBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). After adjusting for age at mammogram, women with IBC had, on average, 5.56% higher percent density than women with SDBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Respectively, women with lobular and mixed histologic types had 4.64% and 6.01% higher breast density than women with ductal tumours (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). None of the other tumour characteristics were significantly associated with breast density after adjusting for age at mammogram.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn contrast, percent mammographic breast density was significantly associated with several risk factors evaluated at recruitment after adjusting by age at mammogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Specifically, breast density was significantly higher in participants with older age at menarche, being nulliparous, having breastfed for longer periods, currently taking MHT, having a personal history of benign breast disease (BBD), having a lower BMI at age 20 or at recruitment, physical activity, and drinking more alcohol (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). The largest differences in density, after adjustment for age at mammogram, were observed for current use of MHT at recruitment (9.65% higher density than never users); obesity at recruitment (17.10% lower breast density than normal weight women); overweight/obesity at age 20 (11.07% lower density than normal weight); and BBD (7.36% higher density for presence vs absence). Ever OC users and post-menopausal women had higher density and post-menopausal women lower density, but these associations were substantially attenuated and not significant after adjusting for age at mammogram. Age at first birth, age at menopause, family history of breast cancer and smoking status were not significantly associated with breast density in unadjusted or age-adjusted analyses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e show estimates for the association between breast density and risk factors with IBC vs SDBC in mutually adjusted analyses. After adjusting for other risk factors, risk of IBC increased with increasing breast density (OR (95%CI)\u0026thinsp;=\u0026thinsp;1.39 (0.95\u0026ndash;2.03) for Q2 vs Q1, 1.66 (1.14\u0026ndash;2.44) for Q3 vs Q1 and 2.13 (1.44\u0026ndash;3.17) for Q4 vs Q1). After adjusting for breast density, we identified significant associations between IBC and late age at menopause (1.60 (1.02\u0026ndash;2.50) for 55\u0026thinsp;+\u0026thinsp;vs\u0026thinsp;\u0026lt;\u0026thinsp;50); current use of MHT (1.41 (1.04\u0026ndash;1.91) for current use vs never use), and personal history of BBD (1.36 (1.11\u0026ndash;1.68)). Although not nominally significant, women with family history of breast cancer (1.26 (1.00-1.58)) and a history of OC use (1.25 (0.93\u0026ndash;1.69) current vs never) were also at increased odds of IBC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBoth overweight and obese women were less likely to have a diagnosis of IBC than women with a normal weight. However, the association was stronger for overweight than obese women, and it was attenuated after adjustment for breast density. After density adjustment, ORs (95% CIs) changed from 0.69 (0.55\u0026ndash;0.86) to 0.74 (0.58\u0026ndash;0.93) for overweight women, and from 0.78 (0.59\u0026ndash;1.04) to 0.88 (0.65\u0026ndash;1.19) for obese women (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;4)\u003c/b\u003e. In contrast, neither overweight nor obesity at age 20 were significantly associated with IBC, but women who were underweight at that age were more likely to be diagnosed with IBC compared to normal weight women (1.65 (1.14\u0026ndash;2.38)). This association was independent of other risk factors, including their BMI at recruitment.\u003c/p\u003e\u003cp\u003eSensitivity analyses restricted to women with available mammographic density data yield similar results than the main analyses including an indicator term for missingness. The risk factor associations with IBC remained after accounting for tumour characteristics (\u003cb\u003eSupplementary Table\u0026nbsp;4)\u003c/b\u003e.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study of women undergoing routine mammography screening in England, we found that several established risk factors for breast cancer were associated with breast density, and that both density and risk factors independently influenced the likelihood of being diagnosed with an IBC. Specifically, late age at menopause, current use of MHT, and a history of BBD were each associated with higher likelihood of IBC, while being overweight at recruitment was associated with reduced risk. These findings build on previous research by simultaneously accounting for breast density and tumour characteristics- two important determinants of detectability- and by using linked screening histories rather than self-reports, thus minimizing detection and misclassification biases. Clinically, these results may help identify women at higher risk of IBCs who could benefit from more tailored screening approaches to improve outcomes.\u003c/p\u003e\u003cp\u003eOur findings confirmed the established association between the increased risk of being diagnosed with IBC for women with higher breast density [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], as well as the tendency for IBCs to present with more aggressive features, including higher grade, nodal involvement, larger size, and ER negativity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consistent with previous reports [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], we also found that several established breast cancer risk factors measured at recruitment were significantly associated with percent breast density, independently of the age at mammogram. For most women in our study, density was measured using the most recent pre-diagnostic mammogram, taken within a few years of the recruitment questionnaire (mean interval: 1.7 years for IBCs ,1.2 years for SDBCs). While this time gap may have attenuated associations due to temporal changes in density or risk factors, the intervals were relatively short, and results remained robust.\u003c/p\u003e\u003cp\u003eWomen who experience menopause at age 55 or older were 1.6 times more likely to be diagnosed with an IBC than those with menopause before age 50. Although later age at menopause was also associated with higher breast density, the association remained after adjustment. Similarly, we showed that current MHT users are 1.4 times more likely to be diagnosed with an IBC than never users, independently of density. Our findings are relevant because although the association between these menopausal factors and IBC are well established [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], most reports speculated that they might be explained by their associations with a higher mammographic breast density [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our results suggest that other factors not fully captured by density, such as mammographic textural features, could influence cancer detection during mammography screening. Measurement error in the breast density estimates could also result in residual confounding.\u003c/p\u003e\u003cp\u003eFurthermore, MHT use has also been linked to lobular histology and lower-grade tumours [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], both of which may affect detectability on mammography. In our data, lobular cancers were more frequent in women with dense breasts, but they occurred at similar frequencies in IBC or SDBC cases. Tumour grade was not associated with density, but IBCs were more likely to be of higher grade, consistent with faster growing being missed at screening. Thus, while grade could partially mediate the MHT association with IBC, this association remained after adjusting for tumour grade and other tumour characteristics. Further studies are needed to clarify the mechanisms underlying the associations of late age at menopause and MHT use with IBC.\u003c/p\u003e\u003cp\u003eThe association of IBC with a personal history of BBD is also consistent with previous reports [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. We showed that although women with BBD have higher density, it does not fully explain the association with IBC. The association remained independent of tumour characteristics and was stronger after adjustment for these variables. As in other studies, BBD was self-reported and included a wide range of conditions such as hyperplasia, cysts, mastitis and others, which may have district mammographic features and associations with risk of IBC. For instance, calcifications have been found to be more frequent in proliferative disease with atypia, while masses are more common in lower-risk, non-proliferative BBDs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These findings underscore the need for future studies to evaluate specific types of BBD and their relationship with IBC risk, as well as imaging or pathological features that may mediate this association.\u003c/p\u003e\u003cp\u003eConsistent with previous studies, higher BMI was associated with lower breast density and a reduced risk of IBCs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our analyses, the inverse association between overweight and IBC was substantially attenuated \u0026ndash; although still statistically significant- after adjusting for breast density, while the association with obesity was weaker and not statistically significant after adjustment. These findings suggest that lower density among women with higher BMI may partly mediate the observed lower risk of IBC, likely through improved tumour visibility on mammograms.\u003c/p\u003e\u003cp\u003eThese findings underscore the potential for detection bias in studies of BMI and breast cancer risk conducted in screened, predominantly post-menopausal populations, and could help explain discrepancies between observational studies\u0026mdash;which typically show a direct association between BMI and postmenopausal breast cancer[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] \u0026mdash;and Mendelian randomization studies, which have reported inverse associations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Interestingly, BMI at age 20 was not associated with density in our study, but underweight women at that age appeared more likely to be diagnosed with IBC later in life- a novel finding not previously reported[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] that needs confirmation. Together, these findings highlight the importance of accounting for breast density and screening history when assessing associations between BMI and breast cancer risk in populations undergoing mammographic screening [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFamily history and OC use showed weaker, statistically non-significant, associations, consistent with mixed finding in the literature. However, a recent large population-based cohort in Sweden showed that the association with IBC is stronger for those women whose relatives also had IBCs (1.18-fold increase for any breast cancer history compared to 1.70-fold increase in risk for a history of IBC), particularly when relatives were diagnosed at a younger age [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This suggests that more detailed family history could better identify women at higher risk of IBC. Our findings of ever OC users being more likely to be diagnosed with IBC suggest that OC use might have longer-lasting effects that are specifically relevant to IBC, which may be missed in studies examining overall breast cancer risk. The association with IBC was independent of tumour characteristics, indicating that IBC risk is not fully explained by OC use predisposing to tumour subtypes that are harder to detect on screening mammography. Previous studies on the potential associations between IBC and age at menarche, parity, age at first birth, smoking status, and alcohol consumption have been inconsistent [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and were not confirmed in our study.\u003c/p\u003e\u003cp\u003eThe strengths of our study include access to detailed screening histories, comprehensive risk factor data, breast density measurements, and detailed tumour characteristics within a prospective cohort study. These features allowed us to evaluate the impact of breast density as a confounder factor in studies of risk factors for IBC in screened populations. In addition, our analyses were restricted to women who attended routine screening, ruling out attendance bias as a confounder. This restriction ensures that our findings are directly relevant to populations undergoing mammographic screening and more accurately reflect relationships between risk factors, density, and IBC. Furthermore, we evaluated the role of tumour characteristics in detection and whether they could be potential explanations of why some risk factors predispose to tumours more likely to be diagnosed between screening rounds.\u003c/p\u003e\u003cp\u003eThis study has several limitations. Risk factors were measured at recruitment rather than at the specific time of the relevant screening, and breast density measurements were primarily taken from the screening mammogram during the cycle preceding cancer detection. This could introduce differences by mode of detection, as the time interval between the density measurement and diagnosis was, by definition, shorter for IBCs compared to SDBCs (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). To address these potential biases, we adjusted analyses for the time between density measurement and diagnosis, as well as the time between risk factor assessment and diagnosis. Although breast density was more often missing among women with IBC (47%) than SDBCs (33%), missingness was unrelated to the risk factors examined. We included a separate category for missing density in multivariable models and confirmed our results with sensitivity analyses restricted to women with density measurements, suggesting that missing data did not affect our conclusions drawn from the results.\u003c/p\u003e\u003cp\u003eWe conducted case-case analyses rather than prospective cohort analyses to estimate hazard ratios for risk factors separately for IBC and SDBC due to the lack of density data at the cohort level. While this approach allows for more detailed examination of risk factors for IBC, any differences in HR would be driven by the examined case-case associations. Additionally, the study lacked access to radiological reviews, which would have enabled differentiation between false-negative, occult or minimal-sign IBCs (missed cancers) and true interval cancers. Occult IBCs are strongly associated with high breast density, where tumours are more challenging to detect radiologically, whereas the density association is weaker for true interval cancers or false-negatives [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Using proxy measures for true IBC (e.g. those in women with low breast density) or incorporating radiological reviews in future studies could provide further insights into the interplay between density, detection biases, and tumour biology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings, together with previous literature, provide strong evidence that breast density and several risk factors independently predict IBC risk. Understanding how these factors influence tumour detectability could support development of tailored breast screening strategies, such as adjusted intervals or supplementary imaging, to improve early detection. Further research should investigate whether these associations reflect detection issues/challenges or biological mechanisms/aetiology and explore specific BBD subtypes and detailed family history to enhance risk stratification and screening efficiency.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBenign Breast Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDuctal Carcinoma In\u0026ndash;Situ\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOestrogen Receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBreast Cancer Now Generations Study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Epidermal Growth Factor Receptor 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterval Breast Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Classification of Diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMetabolic Equivalent of Task\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMHT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMenopausal Hormone Therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Health Service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHSBSP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Health Service Breast Screening Programme\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOral Contraceptives\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgesterone Receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eScreen\u0026ndash;Detected Breast Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSHIM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eScreening Histories Information Manager\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumour Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organisation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was undertaken with informed consent and ethics approval from the Southeast Multi-Centre Research Ethics Committee [MREC 03/01/014)]\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication:\u0026nbsp;\u003c/em\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to the sensitive nature of the data. Information on data access can be found in the Generations Study website https://thegenerationsstudy.co.uk/.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u0026nbsp;\u003c/em\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u0026nbsp;\u003c/em\u003eThis work was funded by Breast Cancer Now and The Institute of Cancer Research.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLJ and MB analysed and interpreted the data with equal contributions. RF and MJ contributed to the interpretation of data and writing of the manuscript. LJ and MGC wrote the first draft of the manuscript. ABG and MGC obtained funding and supervised the work. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the contributions of Anthony Swerdlow and Minouk Shoemaker for the generation of data included in these analyses. The authors also thank Breast Cancer Now (BCN) and The Institute of Cancer Research for support and funding of the BCN Generations Study, and the study participants, study staff, and the doctors, nurses, and other health-care providers and health information sources who have contributed to the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNHS Breast Cancer Screening Programme. Last accessed: 16 May 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolm J, Humphreys K, Li J, Ploner A, Cheddad A, Eriksson M, et al. Risk factors and tumor characteristics of interval cancers by mammographic density. 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Am J Epidemiol Oxf Univ Press. 2015;181:956\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwu474\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwu474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Rodriguez J, Mao X, Grassmann F, Tapia J, Eriksson M, et al. Incidence and Risk Factors of Interval and Screen-Detected Breast Cancer. JAMA Oncol [Internet]. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamaoncol.2025.0167\u003c/span\u003e\u003cspan address=\"10.1001/jamaoncol.2025.0167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlanch J, Sala M, Ib\u0026aacute;\u0026ntilde;ez J, Domingo L, Fernandez B, Otegi A, et al. 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Determinants of mammographic density change. JNCI Cancer Spectr. Oxford University Press; 2019. p. 3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jncics/pkz004\u003c/span\u003e\u003cspan address=\"10.1093/jncics/pkz004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarnes I, Garcia-Closas M, Gathani T, Sweetland S, Floud S, Reeves GK, et al. A comparative analysis of risk factor associations with interval and screen-detected breast cancers: A large UK prospective study. 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Mammographic features of benign breast lesions and risk of subsequent breast cancer in women attending breast cancer screening. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-021-08118-y/Published\u003c/span\u003e\u003cspan address=\"10.1007/s00330-021-08118-y/Published\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlintman M, Rosendahl AH, Randeris B, Eriksson M, Czene K, Hall P, et al. Postmenopausal overweight and breast cancer risk; results from the KARMA cohort. Breast Cancer Res Treat Springer. 2022;196:185\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10549-022-06664-7\u003c/span\u003e\u003cspan address=\"10.1007/s10549-022-06664-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShu X, Wu L, Khankari NK, Shu XO, Wang TJ, Michailidou K, et al. Associations of obesity and circulating insulin and glucose with breast cancer risk: A Mendelian randomization analysis. Int J Epidemiol Oxf Univ Press. 2019;48:795\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyy201\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyy201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia-Closas M, De Gonzalez AB. Invited commentary: Screening and the elusive etiology of prostate cancer. Am J Epidemiol. Oxford University Press; 2015. pp. 390\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aje/kwv086\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwv086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and tumour characteristics of the study population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterval \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;755\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScreen-Detected \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,185\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at recruitment, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.13 (7.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.19 (6.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at mammogram, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.56 (5.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.49 (5.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at diagnosis, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.32 (6.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.61 (6.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime from recruitment to mammogram (years), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.54 (2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.32 (2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime from recruitment to diagnosis (years), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.19 (3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.42 (3.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of diagnosis, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2004\u0026ndash;2008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2009\u0026ndash;2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e496 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u0026ndash;2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e404 (54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e507 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive status, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261 (22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e708 (94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e924 (78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e250 (21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e335 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e477 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e264 (35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorphology type, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuctal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e549 (73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e698 (59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.724\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNode status, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e601 (51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e234 (31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumour size (mm), N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e336 (45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e656 (55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225 (30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER status, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e525 (70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e791 (67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR status, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e283 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e375 (32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e175 (23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2 status, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e524 (69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e725 (61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown, N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ep-value from logistic regression multivariate model of all listed variables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, interval cancers, risk factors, mammographic density","lastPublishedDoi":"10.21203/rs.3.rs-7940334/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7940334/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eInterval breast cancers (IBC), diagnosed between routine screening rounds, tend to have a worse prognosis than screen-detected breast cancers (SDBC). Identifying risk factors for IBCs is critical for improving early detection and developing risk-stratified screening strategies to reduce their incidence. We evaluated associations of breast density, reproductive, hormonal, lifestyle and medical factors with IBC compared to SDBC in a large UK cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAnalyses included 1,940 women diagnosed with breast cancer after enrolment in the Breast Cancer Now Generations Study, a prospective UK cohort linked to the National Health Service Breast Screening Programme. Pre-diagnostic risk factors were collected at enrolment, and breast density was estimated from pre-diagnostic mammograms in a subset of 1,191 cases. Screening histories were used to identify 1,185 SDBCs and 755 IBCs. Logistic regression models estimated odds ratios (OR) and 95% confidence intervals (CI) for associations between risk factors and IBC, adjusting for breast density and tumour characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigher breast density was associated with later age at menarche, nulliparity, breastfeeding, history of benign breast disease (BBD), alcohol consumption, lower body mass index (BMI) at recruitment and at age 20, and current use of menopausal hormone therapy (MHT). In a mutually adjusted model, IBC risk was lower in overweight women (OR (95% CI )\u0026thinsp;=\u0026thinsp;0.74 (0.58\u0026ndash;0.93) vs normal weight), and higher with high breast density (2.13 (1.44\u0026ndash;3.17) for Q4 vs Q1), later age at menopause (1.60 (1.02\u0026ndash;2.50) 55\u0026thinsp;+\u0026thinsp;vs\u0026thinsp;\u0026lt;\u0026thinsp;50), current MHT use (1.41 (1.04\u0026ndash;1.91) vs never users), history of BBD (1.36 (1.11\u0026ndash;1.68)), being underweight at age 20 (1.65 (1.14\u0026ndash;2.38) vs normal weight), family history of breast cancer (1.26 (1.00\u0026ndash;1.58)) and ever using oral contraceptives (1.25 (0.93\u0026ndash;1.69) vs never). These risk factor associations were independent of tumour characteristics.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eBreast density and several risk factors independently increase the likelihood of IBC, highlighting opportunities for tailored screening strategies to enhance early detection and reduce IBCs incidence.\u003c/p\u003e","manuscriptTitle":"A comparison of risk factors for interval and screen-detected breast cancers: a case-case analysis of the Breast Cancer Now Generations Cohort, UK","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:22:04","doi":"10.21203/rs.3.rs-7940334/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb74a986-afb8-425c-a03c-14f5ace3b038","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T23:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:22:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7940334","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7940334","identity":"rs-7940334","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}