In the light of the breast density legislation in the USA, it is important to know a woman’s breast cancer risk, but particularly her risk of a tumor that is not detected through mammographic screening . Therefore, we examined the associations of automatically measured volumetric breast density with screen-detected and interval cancer risk, separately.Volumetric breast measures were assessed automatically using Volpara version 1.5.0  for the first available digital mammography (DM) examination of 52,814 women (age 50 − 75 years) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. We excluded all screen-detected breast cancers diagnosed as a result of the first digital screening examination. During a median follow-up period of 4.2 (IQR 2.0–6.2) years, 523 women were diagnosed with breast cancer of which 299 were screen-detected and 224 were interval breast cancers. The associations between volumetric breast measures and breast cancer risk were determined using Cox proportional hazards analyses.p for heterogeneity < 0.001)). Dense volume (DV) was also found to be positively associated with both interval and screen-detected breast cancers (HR 4.92 (95% CI 2.98–8.12) and HR 2.30 (95% CI 1.39–3.80), respectively, for VDG-like category (C)4 compared to C1 (p for heterogeneity = 0.041)). The association between percentage dense volume categories and interval breast cancer risk (HR 8.37) was not significantly stronger than the association between absolute dense volume categories and interval breast cancer risk (HR 4.92).Percentage dense volume was found to be positively associated with both interval and screen-detected breast cancers (hazard ratio (HR) 8.37 (95% CI 4.34–16.17) and HR 1.39 (95% CI 0.82–2.36), respectively, for Volpara density grade category (VDG) 4 compared to VDG1  contains supplementary material, which is available to authorized users. The female breast consists of fibroglandular (dense) and fat (nondense) tissue. It is known that breast density, measured as either the percentage or absolute amount of fibroglandular tissue in the breast, is a strong breast cancer risk factor –3. BesidWhen breast density measures are used as a tool to stratify women for different screening strategies, it is important that these measures are highly reproducible as it is undesirable that the likelihood of a woman being classified as having dense breasts is dependent on which radiologist she goes to. Another prerequisite is that these measures can be obtained in the daily practice of high-throughput screening.Breast density can be measured using several methods. The most widely used methods are visual estimation using Breast Imaging - Reporting and Data System (BI-RADS) density categories and the semi-automatic area-based Cumulus method . The forNowadays, digital mammography (DM) is widely used and several software packages are available to measure breast density fully automatically on digital mammograms. Most packages report volumetric breast density measures, which are user-independent and developed with the intention to give an accurate and reproducible estimation of the amount of fibroglandular tissue in the breast using physics-based modeling based on information in the header of the raw digital images , 14. ThiIn light of discussions on the need for supplemental screening after a negative mammogram, measures that indicate the risk of a tumor that is not detected at mammography screening  may be even more relevant. BI-RADS density and other area-based density measures have been shown to be strongly related to the risk of interval cancer , 21–26. The study population consists of 52,814 women aged between 50 and 75 years who had one or more digital mammographic examinations at the Preventicon screening unit in Utrecht, the Netherlands, between 2003 and 2011. The Preventicon screening unit is one screening unit of the Foundation of Population Screening Mid-West screening region, which is one of the five screening regions of the Dutch biennial breast cancer screening program. All regions follow the same screening protocol. The Dutch program involves mammography only. By participating in the Dutch screening program, women consent to their data being used for evaluation and improvement of the screening, unless they have indicated otherwise.In this breast cancer screening cohort, for each participant we collected the first unprocessed DM examination that was taken at the Preventicon screening unit between 2003 and 2011. All mammograms were acquired using Lorad Selenia DM systems . If this was the participant’s first screening examination within the program, this examination included the two standard views, craniocaudal (CC) and mediolateral oblique (MLO). If this was not her first screening examination within the program, i.e. when she had undergone film screen mammography in earlier rounds, MLO was the routinely acquired view and the CC view was taken only when indicated . A CC view was present in 57% of the subsequent screening examinations. Information about follow up was obtained from the screening registration system. Women were followed until breast cancer diagnosis (event), death or until 2 years after the last available mammogram, whichever came first. Information on breast cancer development was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. This also included information on the mode of detection: through screening mammography (screen-detected cancer) or through diagnosis in the 24 months screening interval after a negative screening mammogram . Interval cancers diagnosed more than 24 months after a negative screening mammogram were not included for analysis. Within the screening program, information on risk factors other than age is not collected.Absolute dense volume (DV), percentage dense volume (PDV) and absolute nondense volume (NDV) were automatically assessed from unprocessed mammograms of the left and right breasts, using Volpara density . Although normally both MLO and CC views would be used for calculating volumetric density measures with Volpara, here we used the mean of only the left and right MLO views for the risk analyses, since this is the routinely acquired view and CC views were not available for all women. In this way we ensured that breast density was assessed in the exact same way in all participants. Volpara density grades (VDGs) were constructed based on this average PDV using the standard Volpara cutoff points from version 1.5.0 . The VDGs are designed to mimic the fourth edition of the BI-RADS breast density categories. We chose to use this edition, because it is most comparable to what has been used in previous papers.Descriptive statistics of age and breast measures  were determined. Continuous breast measures were transformed using the natural logarithm (ln) to obtain normal distributions. Pearson correlation coefficients for correlation between different breast measures and between breast measures and age were determined. We also assessed the correlation between breast measures on the MLO and CC views among women who had both views available, and on the MLO views of the left and right breasts.We examined the associations between the different breast density measures and breast cancer risk by calculating hazard ratios (HR) and their 95% confidence intervals (95% CI) using Cox proportional hazards analyses for quartiles (quartile cutoffs were determined based on the distribution of the different density measures in the whole cohort at baseline) and continuous measures (per standard deviation (SD) increase of the ln-transformed measure) of DV and PDV. We also determined associations between breast cancer risk and two additional breast density measures: (1) VDG categories (Volpara BI-RADS) and (2) a categorized DV variable in which the cutoff points were created as to obtain the same group sizes as with the VDG categories (VDG-like categories).To minimize the number of breast cancer cases in the model that were diagnosed based on the same mammogram as was used for breast density assessment, we excluded all screen-detected breast cancer cases of women diagnosed on the basis of their first digital screening examination.Cox proportional hazards analyses were used to examine the association between breast density and breast cancer risk. Age was used as the underlying time scale with entry and exit time defined as age at time of the first available DM and age at breast cancer diagnosis, death or censoring 2 years after the last DM before 1 January 2012. In this way, the confounding effect of age was taken into account. Two models were constructed. The first model only contained one of the density measures. The second model was adjusted for NDV in case the model included quartiles, categories or continuous measures of DV. We found that DV and NDV volume were positively correlated (Pearson correlation = 0.50). By adjusting for NDV we attempted to determine the independent effect of DV on breast cancer risk, without the interference of NDV. Models containing a PDV measure (including VDG) were not adjusted for NDV, since NDV is already part of PDV (PDV = DV/(DV + NDV)). The proportional hazards assumption was evaluated by Schoenfeld residual plots and log-minus-log plots, and the assumption was not violated. To examine the presence of a linear trend in HRs over the quartiles and categories of breast measures, quartile and category variables were added to the models as continuous variables.To examine the association between the different density measures and screen-detected or interval cancers and to test whether the association was statistically significantly different between density measures and screen-detected versus interval cancers, the method of Lunn and McNeil was used for competing risk analysis , 28. CoxA bootstrap method was used to determine whether measures based on PDV or DV were stronger breast cancer risk factors. This was done for VDG versus VDG-like categories, quartiles of PDV versus quartiles of DV and continuous PDV versus continuous DV. The differences between coefficients for models containing a PDV or DV measure were determined within 2000 bootstrap samples. A difference of 0 indicates that there is no difference between the effect of DV and PDV on breast cancer risk. A 95% CI that does not include 0 means that one measure is statistically significantly more strongly related to breast cancer risk than the other measure.In addition, a sensitivity analysis was performed where we only used the contralateral MLO view for breast cancer cases and for non-breast cancer cases the MLO view of a random chosen side, with the same left and right distribution as that of the breast cancer cases.For the main analysis both invasive and in situ breast cancers were used. In a sensitivity analysis, we only took invasive breast cancers into account for data analysis.For all breast density measures, we compared the fourth category or quartile with the first. Nowadays, often the second category is used as the reference category, as BI-RADS breast density category 2 is the most prevalent category among women of breast cancer screening age. To be able to compare with other studies, we also present results using the second category or quartile of each breast density measure as reference category. Statistical analyses were performed using SPSS version 22 and R version 3.2.0.After excluding cases of screen-detected breast cancer, diagnosed on the basis of the first available digital screening examination, 523 breast cancers, including 299 screen-detected and 224 interval breast cancers were used for analyses. At the first digital screening mammogram the median age was 56 years IQR 51–63) in women who did not develop breast cancer , 59 years (IQR 54–64) in women who were diagnosed with screen-detected breast cancer and 55 years (IQR 50–62) in women diagnosed with interval breast cancer during follow up. The median total number of screening rounds in which women participated  and digital together) was 5 (IQR 2–8), 7 (IQR 4–9) and 4 (IQR 2–7) in women who were not diagnosed with breast cancer, were diagnosed with a screen-detected breast cancer and were diagnosed with interval cancer, respectively. The median follow-up time after the first digital mammogram until breast cancer diagnosis (event), death or end of follow up, was 4.2 years (IQR 2.0–6.2), 3.8 years (IQR 2.1–4.3) and 2.3 (IQR 1.3–4.0), in women with no diagnosis of breast cancer, screen-detected breast cancer and interval breast cancer, respectively (Table –63 in wop < 0.01)) and positively correlated with DV . DV was positively correlated with NDV . DV and PDV were both negatively correlated with age , whereas NDV was positively correlated with age . There was strong correlation between breast measures of the left and right breast and between CC and MLO views. Correlations between the MLO views of the left and right breast for DV, PDV and NDV was 0.86, 0.91 and 0.98, respectively . Correlation between MLO and CC views of the right breast was 0.86, 0.90 and 0.97 DV, PDV and NDV, respectively .PDV was strongly negatively correlated with NDV  and 1.45 (95% CI 1.32–1.60), respectively, for one standard deviation increase in the ln-transformed continuous measure  and 1.39 (95% CI 0.82–2.36) for VDG-like category and VDG category 4 compared to category 1, respectively and were 2.24 (95% CI 1.51–3.31) and 1.36 (95% CI 0.97–1.94) for the highest compared to the lowest quartiles of DV and PDV, respectively. The HRs were 1.27 (95% CI 1.14–1.43) and 1.10 (0.97–1.25) for continuous measures of DV and PDV, respectively. DV, measured continuously, had a significantly stronger association with screen-detected breast cancer risk than continuously measured PDV ) the risk in relation to interval breast cancer was statistically significantly stronger than that in relation to screen-detected breast cancer  were in situ, and 466 (89%) were invasive breast cancers. For one case of breast cancer, information on invasiveness was missing. Of the interval cancers, 218 (97%) were invasive breast cancers compared to 83% of screen-detected cancers. Results restricted to invasive breast cancers are presented in Tables Our results did not change when we only used the contralateral MLO view for breast cancer cases and the MLO view of a random chosen side for non-breast cancer cases (instead of the mean of the left and right view) for breast density measurements in relation to breast cancer risk (data not shown).For reference and for comparison with other papers, Additional file In this study we found that automatically assessed volumetric breast density measures are positively associated with breast cancer risk and in particular with the risk of interval cancer. The difference in effect on interval and screen-detected cancers appeared slightly more pronounced for the percentage dense volume measures than for absolute dense volume measures. The VDG categories that are comparable to the BI-RADS categories had the highest risk estimates for association with interval breast cancers.To our knowledge the relationship between volumetric breast density and risk of screen-detected and interval cancers, respectively, has not been studied separately before, but there are several studies on the association between volumetric breast density, determined on digital mammograms, and total breast cancer risk. An overview of their design and results is given in Additional file As mentioned, no other studies have examined the ability of automated volumetric breast density assessment methods to identify women with a high risk of interval breast cancer. However, from a breast cancer screening perspective, this is important to know, since women with a high risk of interval breast cancer might benefit most from supplemental screening. Area-based breast density measures, such as BI-RADS density and the semi-automatic Cumulus thresholding method have been studied in relation to interval cancer risk. In these studies density was found to be more strongly related to the risk of an interval cancer than to the risk of a screen-detected cancer –26, 29. Likewise, in our study percentage dense volume seems to be a better marker for interval breast cancer risk than dense volume. When looking at total breast cancer risk, this difference between percentage dense volume and absolute dense volume seems less pronounced. An explanation for this finding could be that the amount of dense tissue is etiologically relevant for breast cancer risk, and that when this dense tissue is occupying the larger part of the breast volume, these women will run a higher risk that a tumor will be masked at mammography and later diagnosed as interval cancer. If on the other hand, this same amount of dense tissue is accompanied by a large amount of fat tissue, large parts of the mammogram may still be well “readable” for cancer detection, and the probability that this tumor is detected at the screening examination will become higher.Besides masking, another possible explanation for the relatively larger number of interval breast cancers in women with dense breasts is tumor aggressiveness. It is possible that tumors in dense breasts are more aggressive (grow faster) than tumors in nondense breasts. However, although some studies report that breast density is more strongly related to estrogen receptor (ER)-negative tumors than to ER-positive tumors, this was not confirmed in other studies –34.When interpreting our results, it should be kept in mind that we excluded prevalent breast cancer cases . The proportion of interval cancers is therefore larger (43%) in the current study than in the Dutch biennial screening program (26%). As interval cancers are relatively more common in dense breasts, the association between breast density and total breast cancer risk is probably somewhat overestimated. However, this is not the case for analyses separated for screen-detected and interval breast cancers.In most European countries women are screened from the age of 50 years. In the USA most women are screened from the age of 40 years. In addition, biennial screening is common in European countries, while in the USA many women are screened yearly. We therefore looked at interval cancers diagnosed within the first year after a screening mammogram. We observed that 22% of the interval cancers in the group with the lowest breast density was found in that first year, while this was 60% in the group with the highest breast density, indicating an even stronger association between breast density and risk of interval breast cancer within the first year after screening.A limitation of our study is that we could not compare the association between the automated volumetric density measurements and breast cancer risk with those of other commonly used breast density measurements such as BI-RADS. The reason for this is that BI-RADS density readings are not routinely performed in the Dutch breast cancer screening program. Previous studies directly comparing automated density measuring methods to BI-RADS density readings and other breast density measurements in relation to breast cancer risk identified similar positive associations for all density measurements, although they may differ in their classification of which women have dense breasts and which women have not , 35, 36.Another limitation is that at the time the mammographic examinations were performed, the CC view was not a routine view in our screening program. The CC view was only taken when women had their first screening examination, or on indication, e.g. because of high breast density or suspect lesions. Although for robustness of volumetric breast density estimates it may have been preferable to calculate density on MLO and CC views, in our study we did this on the MLO view only, to prevent effect estimates from being biased by the selective availability of the CC views.A final limitation is that we only had information about age as an important confounder. Therefore, adjustment of the risk estimates for other confounders was not possible. The inability to adjust for parity and other reproductive factors may have overestimated the risk estimates somewhat both for dense volume and percent dense volume. Not adjusting for BMI is expected to have led to an underestimation of the risk estimate of percentage dense volume in particular, but all in all, the risk estimates for total breast cancer risk are quite comparable to what has been published in other papers on volumetric breast density –18, 37. Strengths of our study are the large cohort, the availability of unprocessed digital data for all its participants and its follow up since 2003 when digital mammography screening was introduced in the Netherlands. Due to linkage with the Netherlands Cancer Registry, which covers all residents of the Netherlands and is more than 95% complete, we have virtually complete information not only on screen-detected, but also on interval breast cancers.We found that automated breast density measures can be used for breast cancer risk assessment and in particular, for the risk of a tumor that is not detected at the breast cancer screening. Automated measures are especially useful in programs where BI-RADS is not routinely determined or to improve reproducibility.Additional file 1: Figure S1.Pt p trend: this was determined by adding the categorical measures as a continuous measure into the model, PDV percentage dense volume, DV dense volume, Per SD per standard deviation. *Absolute dense volume measures are adjusted for nondense (breast fat) volume. (DOCX 185 kb)Associations between mammographic density measures and breast cancer risk (second category used as reference). In the Cox proportional hazards analyses age was used as the underlying time scale. Additional file 2: Figure S2.Pt p trend: this was determined by adding the categorical measures as a continuous measure into the model, PDV percentage dense volume, DV dense volume, Per SD per standard deviation, SDC screen-detected cancer, IC interval cancer. *Absolute dense volume measures are adjusted for nondense (breast fat) volume. (DOCX 890 kb)Mammographic measures and screen-detected, and interval breast cancer risk (second category used as reference). Lunn and McNeil method was used for competing risk analysis. Additional file 3: Table S1.Overview of associations between volumetric breast density measures (measured with Volpara) and breast cancer risk. Overview of studies that also determined the association between Volpara density grade categories and breast cancer risk. (DOCX 23 kb)


Autopsy studies demonstrate the prevalence pool of incidental breast cancer in the population, but estimates are uncertain due to small numbers in any primary study. We aimed to conduct a systematic review of autopsy studies to estimate the prevalence of incidental breast cancer and precursors.Relevant articles were identified through searching PubMed and Embase from inception up to April 2016, and backward and forward citations. We included autopsy studies of women with no history of breast pathology, which included systematic histological examination of at least one breast, and which allowed calculation of the prevalence of incidental breast cancer or precursor lesions. Data were pooled using logistic regression models with random intercepts (non-linear mixed models).cancer and atypical hyperplasia , but not invasive cancer . The estimated mean prevalence of incidental cancer or precursor lesion was 19·5% .We included 13 studies from 1948 to 2010, contributing 2363 autopsies with 99 cases of incidental cancer or precursor lesions. More thorough histological examination  was a strong predictor of incidental in-situ Our systematic review in ten countries over six decades found that incidental detection of cancer in situ and breast cancer precursors is common in women not known to have breast disease during life. The large prevalence pool of undetected cancer in-situ and atypical hyperplasia in these autopsy studies suggests screening programs should be cautious about introducing more sensitive tests that may increase detection of these lesions. Breast cancer is common  and its atypical ductal and atypical lobular hyperplasia (ADH and ALH respectively) in addition to ductal, and lobular carcinoma in-situ (DCIS and LCIS respectively).Autopsy studies may be used to estimate the size of the prevalence pool of incidental cancer (and pre-cancerous lesions) among people not known to have specific cancers during life. The prevalence pool of incidental prostate cancer, for example, has been estimated as 5% at age < 30 years rising to 59% by age > 79 years ; and incIn this study we aimed to update the estimated size of the prevalence pool (reservoir) of incidental breast cancer and precursor lesions at autopsy, and identify factors that were associated with increased prevalence of these lesions.The review protocol was not registered.invasive and in situ cancer), and precursor lesions  diagnosed on histopathology.We included autopsy studies of adult women (>age 18 years) who had no history of pre-existing breast disease and which included a systematic histological examination of at least one breast. We excluded studies that did not report the women’s age, or methodically examine the breast microscopically. The principal outcomes were rates of incidental breast cancer . To identify further papers for inclusion in the review we ran forward citation searches and checked the references of all papers identified by the search for inclusion in the review. Finally, we repeated our original search to identify any additional papers published during the period of data collection.exp. Breast Neoplasms/exp. Breast/pa [Pathology]Breast Diseases/pa [Pathology]((breast or mammary) adj3 ).tw.or/1–4Autopsy/(autopsy* or autopsies or postmortem* or post-mortem* or post mortem*).tw.6 or 75 and 8We planned a priori sub-group analyses for the following risk of bias study characteristics: consecutive versus non-consecutive case selection, population based versus hospital based studies, and the possibility that breast cancer discovered at autopsy may have caused death. We also planned subgroup analysis of the following pathology validity characteristics: intensity of pathological examination (average number of sections submitted for histopathology per case); whether the histopathology reporting method used international standards (such as WHO ); and peGoogle Translate. Disagreements were resolved through discussion with two further authors (CDM and PG).Two authors (KB and ET) independently checked the titles and abstracts of all articles retrieved from Medline and Embase searches, and the full text was obtained if either author judged the article potentially relevant. The same two authors then independently checked all the full text articles for eligibility. Foreign language papers were translated into English using Two authors independently extracted data for English papers (KB and ET) and non-English papers (CDM and KB) using standardized forms. We counted only one cancer or precursor lesion diagnosis per woman – in the case where more than one diagnosis was reported we chose the one of the highest grade/stage . We also only counted lesions which were not diagnosed during life – where this was uncertain, we were conservative and did not count the lesion. Disagreements were decided through discussion. We extracted data at the study level on: cancer and precursor prevalence, age, year that autopsies were performed, and the validity measures as described above. Where available, we also extracted data at the within-study level for women <70 years and ≥70 years on: cancer and precursor prevalence.invasive breast cancer, in-situ breast cancer (DCIS and LCIS), and atypical hyperplasia (ADH and ALH). We pooled data from all studies using logistic regression models with random intercepts to represent the distribution of underlying cancer/precursor prevalences between different populations. This type of model also allowed for the nested structure of the data for the within-study analysis of age-specific prevalence estimates. We used the model to examine the impact of the validity characteristics on the prevalence estimates. SAS 9.4 was used for all analyses. The NLMIXED procedure was used to build the models, as has been recommended [Our main summary measures were the prevalence of previously undiagnosed breast cancer or precursors: ommended .We identified 1925 abstracts from Medline and Embase (search date 8th April 2016): 87 papers were retrieved for full text review; 71 of which did not meet our selection criteria ; atypical hyperplasia (ADH and ALH); and any of these lesions; were 1·1% , 0·0% , 3·4%  and 7·4%  respectively, Table The median prevalence of: invasive cancer, in-situ cancer, atypical hyperplasia) and all categories combined, are presented in Table p < 0·001 in each model). More thorough pathology examination yielded greater prevalences of in-situ cancer, atypical hyperplasia and all categories combined , but not invasive cancer (OR = 1·1), Fig. cancer in studies that used consecutive cases (p = 0.06), and of all categories combined for more recent studies (p = 0.09), but this disappeared after adjusting for thoroughness of pathology examination (p = 0.11 and p = 0.37 respectively). There was a higher prevalence of invasive cancer, , for the study (contributing two datasets) [Predictors for incidental prevalence of each category (atasets)  reportincancer and atypical hyperplasia, with modelled adjustment upwards for less thorough studies, was 8·9% and 9·8% respectively. The overall mean cancer and precursor prevalence, with adjustment of in situ and precursor estimates upwards for less thorough studies, (no adjustment for invasive cancer), was 19·5% (0·85% + 8·9% + 9·8%).The mean prevalence of invasive cancer was 1·5%. After excluding the study in which the breast cancer discovered at autopsy may have contributed to death , it was When we repeated the analysis, limiting it to six studies with separate data on women <70 years and ≥70 years, there were insufficient data to perform a direct within-study comparison while adjusting for thoroughness of pathology examination.Our systematic review of 13 studies (14 datasets) in ten countries over six decades of 2363 autopsies and 99 cases of incidental cancer or neoplastic precursor lesions, found that incidental breast cancer and its precursors are common in women not known to have breast disease during life. The estimates from these autopsy studies represent the best available evidence to answer the important question on the size of the prevalence pool of incidental breast cancer and precursor lesions.cancer in-situ and atypical hyperplasia). However the smaller prevalence pool of incidental invasive cancers appeared easier to find, with even the least thorough studies reporting cases. Unlike invasive cancer which was unrelated to the number of sections submitted for pathology examination, the estimated prevalence of cancer in-situ and atypical hyperplasia was strongly related to the thoroughness of microscopic examination. The odds of finding in-situ cancer and atypical hyperplasia were 127 times and 21 times higher respectively, in the studies where at least 20 sections were examined compared to those where less than 20 sections were examined. We could find no other clear predictors, including age, although this may be attributed to a paucity of data on women ≥70 years.The majority of incidental lesions appear to be precursors for invasive cancer  during life. Although the primary studies were conducted over a long time period (from 1948 to 2010), all but one were conducted in largely unscreened populations. The most thorough studies were conducted in the 1970s and 1980s, and included both hospital and forensic studies.Limitations to this review include variation in the prevalence of incidental breast cancer and precursors across the studies, which could in part be due to underlying differences within the populations studied. Pathologists may have differing thresholds for classifying lesions  and diffThe size of the prevalence pool of incidental invasive breast cancer in unscreened populations may be used to provide an approximate lower bound for the extent of overdiagnosis associated with mammography screening: true overdiagnosis rates are likely to be at least this large. Our estimate of the prevalence of incidental invasive cancer, at 0·85%, is much less than the current life-time prevalence of invasive cancer for women in the USA of 12·4% , or the The excess lifetime prevalence of breast cancer in a regularly screened population may be used to provide an approximate upper bound for the extent of overdiagnosis associated with mammography screening: true overdiagnosis rates are likely to be no larger than this. The lifetime prevalence of invasive breast cancer in the USA in 1975–1977 (prior to the introduction of screening) was 9.4% (1 in 10.6 women). Since 1987 after roll-out of nation-wide mammography screening the life time prevalence has been stable at around 12.5% (1 in 8 women). Some of the increased risk in more recent times is because women are now less likely to die of other causes and because of changing risk factor levels, but the main explanation appears to be increased detection through mammography screening . The expFor in-situ breast cancer, our estimate for the prevalence pool of incidental lesions is ~9%, much higher than the current life-time prevalence of ~2·0% , and lifcancer in-situ and atypical hyperplasia in these autopsy studies suggest caution for screening programs. First, as new breast screening technologies become more sensitive , it is likely that the proportion of overdiagnosed women will increase. Protocols for more intense biopsy sampling of screen detected abnormalities, or enhanced biopsy methods such as stereotactic vacuum-assisted core biopsy, are also likely to further increase overdiagnosis rates. Accordingly, new technologies and biopsy protocols should evaluate whether any increased sensitivity is for clinically important or overdiagnosed cancers, for example by examining interval cancer rates in randomised comparisons of alternative screening technologies [The large pool of undetected nologies . Second,nologies . The connologies .This review has confirmed that there is a large prevalence pool of incidental breast cancer and precursor lesions present across all ages. Policy-makers, researchers, clinicians and women invited to undergo screening need to be aware of the extent of overdiagnosis so breast cancer detection can be improved and women do not undergo treatment for an inconsequential finding detected through breast cancer screening.


In animal models, long-term moderate energy restriction (ER) is reported to decelerate carcinogenesis, whereas the effect of severe ER is inconsistent. The impact of early-life ER on cancer risk has never been reviewed systematically and quantitatively based on observational studies in humans.We conducted a systematic review of observational studies and a meta-(regression) analysis on cohort studies to clarify the association between early-life ER and organ site-specific cancer risk.PubMed and EMBASE (1982 –August 2015) were searched for observational studies. Summary relative risks (RRs) were estimated using a random effects model when available ≥3 studies.RE all ages = 1.28, 95% CI: 1.05–1.56; RRRE for 10–20 years of age = 1.21, 95% CI: 1.09–1.34). Men exposed to early-life ER  had a higher prostate cancer risk than those not exposed . Summary relative risks were not computed for colorectal cancer, because of heterogeneity, and for stomach-, pancreas-, ovarian-, and respiratory cancer because there were <3 available studies. Longer duration of exposure to ER, after adjustment for severity, was positively associated with overall cancer risk in women (p = 0.02). Ecological studies suggest that less severe ER is generally associated with a reduced risk of cancer.Twenty-four studies were included. Eleven publications, emanating from seven prospective cohort studies and some reporting on multiple cancer endpoints, met the inclusion criteria for quantitative analysis. Women exposed to early-life ER  had a higher breast cancer risk than those not exposed (RREarly-life transient severe ER seems to be associated with increased cancer risk in the breast (particularly ER exposure at adolescent age) and prostate. The duration, rather than severity of exposure to ER, seems to positively influence relative risk estimates. This result should be interpreted with caution due to the limited number of studies and difficulty in disentangling duration, severity, and geographical setting of exposure. Energy restriction (ER) without malnutrition has been reported to be the most effective dietary intervention to decelerate aging related diseases –4, incluad libitum has not consistently been associated with the same protective effect and may instead have adverse effects on carcinogenesis . In. In47]. The estimated level of caloric intake was retrieved from historical references included in the prospective studies’ reports, and ranged from 220 kcal/day  to 1660 Methodological quality assessment according to the NOS indicated that the total number of points assigned to each cohort study ranged between 6–7 on a 0–8 scale . Most stThree or more studies on ER and site-specific cancer risk were available for breast cancer , prostatRE = 1.28, 95% CI: 1.05–1.56, I2 = 49.89%; p = 0.08 for Cochran’s Q test) . We refrained from pooling relative risk estimates for ER exposure between 0–10 years of age, because the Cochran's Q test indicated statistically significant between-study heterogeneity   . ER exporan’s Q) . The stum et al., could nom et al., and mortm et al.,. A drop m et al.,) during m et al.,. Similarm et al.,.RE = 1.16, 95% CI: 1.03–1.30; I2 = 0%; p = 0.84 for Cochran’s Q)  .In contrast, two ecological studies reported findings suggesting an inverse association between ER and prostate cancer incidence  and mort2 = 90.02%; p< 0.001 for Cochran’s Q test), ,  , inverseQ test),  and womeQ test), . The stuQ test),  Tables , 5957, 5There are two prospective studies on early-life ER and stomach cancer mortality . The firOne cohort study on adolescent exposure to ER during the Dutch Hunger Winter and pancreatic cancer risk reported no associations in men and women  46]..46].There are two cohort studies reporting on early-life ER and lung cancer risk . The stuAge-period-cohort analyses in ecological studies have indicated reduced testicular cancer incidence rates, interrupting a trend of increasing incidences over time, for cohorts born during the Second World War in Norway, Sweden, and Denmark, but not in Finland  50–52, , 58, 60.One cohort study on adolescent exposure to ER during the Dutch Hunger Winter and ovarian cancer risk showed no association in women  64]..64].p = 0.07) and women (p< 0.001) (p< 0.001) but not in men (p = 0.08) (p = 0.54) and women (p = 0.20)  was associated with a (borderline) increased overall cancer risk in men (< 0.001) . The ass = 0.08) . Particu = 0.08) . Severit = 0.20) . Yet, ov = 0.20) .The epidemiological evidence for a sustained effect of transient (pre)adolescent ER on site-specific cancer risk has been inconclusive and not been reviewed or quantified previously. In this systematic review and meta-analysis of observational studies, the pooled results of cohort studies indicate that women exposed to ER  during childhood and adolescence have a 28% increased breast cancer risk. Also, pooled results from cohort studies indicate that exposure to ER  during childhood and adolescence is associated with a 16% increased risk of prostate cancer. Summary risk estimates for colorectal-, stomach-, pancreatic-, ovarian- and respiratory cancer could not be calculated due to the limited number of studies available or study heterogeneity. Meta-regression analyses were conducted across all cancer sites and suggested that a longer duration of exposure (in months) to early-life ER is (borderline) associated with increased cancer risk in women and men. Particularly, in women, between-study heterogeneity was explained by the duration of early-life ER. The associations remained statistically significant in women after adjusting for severity of exposure.Of note is that the results from the meta-regression analysis are exploratory and should be interpreted with caution given that for women only 6 cohorts with 13 risk estimates were included, and for men only 4 cohorts with 10 risk estimates, resulting in a limited power to discriminate between different covariates. The meta-regression analysis showed that the effect sizes in women tended to increase with a decrease of daily caloric intake. This trend was not significant, however. The lack of cohort studies that have investigated more moderate exposures to early-life ER may have obscured a possible relation.The most obvious finding emerging from this review is the inconsistency of the observed associations between early-life ER and site-specific cancer incidence obtained from various types of human observational studies. Ecological studies suggest either no effect or decreased site-specific cancer risk after transient exposure to severe early-life ER, whereas, prospective cohort studies suggest no effects or increased site-specific cancer risk. There are several potential reasons for the discrepancies between observational studies such as the unique historical contexts and residual confounding from baseline geographical differences in cancer incidence and from other exposures related to war-related uncontrolled ER.ad libitum may have adverse effects on carcinogenesis[e.g. weight and height, during the WW-II years in Europe are approximately reflecting the prevailing nutritional conditions in those countries [The unique historical settings of the observational studies are associated with geographic location and with the duration and severity of ER. Certain aspects of ER, i.e. the timing of exposure , 66, 67,nogenesis, 20, 22 nogenesis, 24. Mosnogenesis–49. Alsonogenesis, 4. Typinogenesis. In contnogenesis, accompanogenesis, 56–60. ountries , 72. Theountries .e.g. food-producing ‘rural’ areas versus non-food producing ‘urban’ areas, was employed as a proxy for unrestricted vs. restricted energy intake [Robsahm et al. [i.e. urban and rural areas) often coincide with the groups that are contrasted in terms of ER. This may mask a true effect of ER on outcome and may thus have caused attenuation of any true inverse associations that may now remain unobserved or even be reversed revealing positive associations. This potential bias may have resulted in the observation that ER is accompanied with an increased risk of breast and prostate cancer in the meta-analysis. Therefore, caution is warranted in interpreting the results from observational epidemiologic studies on early-life ER in relation to cancer.Another potential reason for the difference in findings between observational studies is that many prospective studies do no account for existing baseline differences in absolute cancer incidence across exposure groups. In prospective studies, often a geographical contrast within a country, y intake , 46, 64.m et al.  observedFurthermore, exposure to war-related ER is potentially accompanied with other risk factors for cancer, such as stress, which may explain the observed positive associations between more severe early-life ER and cancer risk, and thereby contribute to the difference in findings between cohort and ecological studies. For example, it has been reported that post-traumatic stress disorder in exposed Jewish children during Holocaust suffering from severe ER was associated with increased breast cancer risk .ad libitum food consumption are supportive[ad libitum conditions [Some findings from the limited number of animal studies that have investigated the cancer-related effects of transient severe ER early in life followed by upportive, 20, 22 upportive, 75 and upportive, 77, conupportive. It has upportive, throughupportive and humaupportive. In contupportive, 80, 81 upportive which maupportive, 83, thenditions . When ERnditions  and resunditions , 86. Thenditions –28. ThisIt seems that a negative energy balance in childhood and adolescence may impact on cancer occurring much later in life. However, the heterogeneity of observational studies to date makes it difficult to draw conclusions. This raises the question on how to proceed in this field. Molecular epidemiological approaches within existing studies may contribute to better insight into the mechanisms that may be at play. However, epidemiologic data regarding the mechanisms underlying an association between early-life ER and human site-specific cancer risk are scarce, because exposure to ER is rarely available in observational studies and few studies are large enough to allow for small subgroup analyses. In addition, tissues and molecular markers to investigate mechanisms are not commonly available. Tumor material, stored in pathology labs, can offer new opportunities for ongoing large-scale epidemiological studies since the tumors may provide molecular signatures of a carcinogenic process that started years ago.Epigenetic changes are thought to be an early step in the carcinogenic process, typically environmental influences on epigenetics are most prominent during childhood and adolescence, the time frame of susceptibility to epigenetic/transcriptional modulations that undergo establishment and maturation , 88. TheIn general, it seems that severe transient ER in the absence of a nutritious diet is associated with increased cancer risk in the breast (for ER exposure at adolescent age) and prostate. Evidence for associations between severe transient ER early in life and risk at other cancer sites is limited. In the meta-analysis of the prospective cohort studies, the duration, rather than severity of exposure to early-life ER, seems to positively influence relative risk estimates. Results should be interpreted with caution due to the limited number of studies and difficulty in disentangling duration, severity and geographical setting of the exposure. For exposure to less severe ER, a decreased association with cancer risk is generally observed, although this is derived only from ecological studies. This raises the question on how to proceed in this field. Molecular epidemiological approaches within existing studies may contribute to explain in part the variation in disease risk across sites providing better insight into the mechanisms that might be at play.S1 Table(PDF)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Bioidentical estrogen and progesterone are supplements whose molecular structures are identical to what is made in the human body, while synthetic supplements are ones whose structures are not. After the Women’s Health Initiative found that the combined use of the synthetics conjugated equine estrogen (CEE) and medroxyprogesterone acetate (MPA) increased breast cancer risk, prescriptions for synthetic HT declined considerably. Since then there has been an increased interest in bioidentical HT; today there are a plethora of websites touting their benefits. However, no peer-reviewed articles support these claims. We performed a retrospective study with the objective of verifying the We searched The Northwestern Medicine Enterprise Data Warehouse for women who initiated HT use after age 50. Women who did not take any HT drug after age 50 served as controls. Nine HT protocols were investigated for breast cancer risk.CEE Alone is associated with decreased breast cancer risk (HR = 0.31), Other Synthetic Estrogen Alone is associated with increased breast cancer risk (HR = 1.49), Bioidentical Estrogen Alone is associated with decreased breast cancer risk(HR = 0.65), CEE & MPA is associated with reduced breast cancer risk (HR = 0.43), and CEE & MPA is associated with reduced breast cancer risk relative to Bioidentical Estrogen & Progesterone (HR = 0.25).Significant results include CEE & MPA is superior to bioidentical HT as far as breast cancer risk. Furthermore, this combination is associated with decrease of breast cancer risk, contrary to previous findings. Additional retrospective studies are needed to confirm our results.Our results indicate  By the 1990s it became popular for women to use hormone therapy (HT) to ease symptoms of menopause such as hot flashes and night sweats, which persist in more than half of women for more than seven years . HT remacase-control study that investigated the effect of the popular synthetic conjugated equine estrogens (CEE). This study investigated the effect of chronic disease incidence among postmenopausal U.S. women with a prior hysterectomy. Over 5,000 women in the HT group took a daily dose of CEE for an average of about 6 years. The researchers followed them for several years, and their outcomes were compared to more than 5,000 in the control group who took no HT. With a p-value of 0.02 the study indicated that CEE had a protective effect against breast cancer. Relative to controls the Hazard Ratio was 0.77 . Anderson et al. [To address the risks associated with estrogen, the Women’s Health Initiative (WHI)-Estrogen-Alone Trial was initiated in 1992 –7. This n et al.  also repprospective HT study that followed 1,084,110 UK women aged 50–64 years between 1996 and 2001. Of those 48,386 took equine estrogen only, and 56,322 took estradiol. With a p-value < 0.0001 relative to controls the Hazard Ratio concerning breast cancer risk for the equine oestrogen group was 1.29 , and the Hazard Ratio concerning breast cancer risk for the estradiol group was 1.24 . The MWS results are similar to estimates derived from a collaborative reanalysis of 51 epidemiological studies in 1997 [The Million Women Study (MWS)  was a prretrospective HT study analyzing Finnish women older than age 50 years using oral or transdermal estradiol , oral estriol , or vaginal estrogens  for at least 6 months during 1994–2001. They were followed for breast cancer with the aid of the Finnish Cancer Registry to the end of 2002. The standardized incidence ratio for breast cancer, when estradiol was used for less than 5 years, was 0.93 , and, when estradiol was used for 5 years or more, was 1.44 .Lyytinen et al.  did a reretrospective HT study investigating Finnish women who used combined estradiol-progestogen therapy. The standardized incidence ratio for breast cancer with estradiol-progestogen for women who used the therapy for 3–5 years was 1.31, and for women who used it for 10 or more years was 2.07.Lyytinen et al.  also didprospective study investigating cancer risk factors in 98,997 women born between 1925 and 1950 [A difficulty with the MWS study and the Finnish studies is that they grouped all estradiol medications. Yet some are bioidentical and some are synthetic. For example, estradiol acetate is bioidentical and ethinyl estradiol is synthetic. The most notable study explicitly investigating bioidentical HT is the E3N-EPIC study, which is a French and 1950 . Very feFollowing the publication of the results of the combined CEE and MPA investigation in the WHI study, prescriptions for this combination of drugs declined considerably in the USA . Since tOur hypothesis was that bioidentical HT is associated with less breast cancer risk than CEE & MPA and other synthetic HT.Santen et al.  state “DThe NMEDW is a joint initiative across the Northwestern University Feinberg School of Medicine and Northwestern Memorial HealthCare. The NMEDW was used to search for data. To approximately include only women who took hormone for the purpose of HT, only women older than age 50 were included. For these women, all drugs taken by them after age 50 were retrieved. Women who took any HT drug after age 50 were used as cases; women who did not take any type of HT drug after age 50 were used as controls. After obtaining the study cohorts and drugs, breast cancer events were obtained from ICD9 codes from the database. The earliest date of HT usage was a woman taking MPA on 1/1/1983.For each patient, the last event of immunization, treatment, procedure, consultation, outpatient service, nurse visit, or office visit was recorded as the last contact date. For the women who did not develop breast cancer before the last contact date, the time to censoring was the number of days from the women’s 50th birthday to the last contact date. For the women who developed breast cancer before the last contact date, the time to event was the number of days from the 50th birthday to the breast cancer diagnosis date.CEE: Conjugated equine estrogen, Premarin; Other Synthetic Estrogen: Cenestin, Enjuvia, Menest, Esterified Estrogen, OrthoEst, Estropipate, Piperazine Estrone Sulfate, Ethinyl Estradiol, Ogen, Estinyl, Ethinyloestradiol, Ethinylestradiol; MPA: Medroxyprogesterone acetate, Amen, Cycrin, Provera, MPA, Depo-Provera; Other Synthetic Progesterone:, Norgestrel, Norethindrone, Ortho-Micronor, Micronor, Nor-QD, Norethindrone acetate, Aygestin, cyproterone acetate, CPA, Androcur, Retroprogesterone, Trengestone, Reteroid, Retroid, Retrone, Nomegestrol Acetate, Promegestone, Chlormadinone Acetate, CMA, Levonorgestrel, Drospirenone, Desogestrel, Norgestimate; Combined CEE and MPA: Premphase, Prempro; Other Combined Synthetics: Femhrt, Diane, Diane-35, Alesse, Desogestrel and Ethinyl Estradiol, Desogen, Norethindrone and Ethinyl Estradiol, Jevantique, Jinteli, Ethinyl Estradiol & Levonorgestrel, Levlen, Levlite, Lybrel, Levora, Nelova, Ortho-Cyclen, Ovral, Preven EC, Sronyx, Tri-Levlen, Norgestrel-Ethinyl Estradiol, Ortho-Cyclen, Ortho Tri-Cyclen, Previfem, Sprintec, Ortho Evra, Evra; Bioidentical Estrogen: Estradiol Oral, Estrace Oral, 17 Beta Estradiol, Micronized Estrogen, Estradiol Acetate Oral, Estradiol Hemihydrate Oral, Gynodiol Oral, Femtrace, Innofem, Estradiol Acetate Oral, Delestrogen, Estradiol Valerate, Estradiol Cypionate, Depo-Estradiol, Estradiol Transdermal, Alora, Climara, Esclim, Estraderm, Vivelle, Vivelle-Dot, Dot, Estrogel, Estrasorb, Estradiol Transdermal, Transdermal Estradiol, FemPatch, Menostar, Elestrin, Divigel, Evamist, Estrasorb, Minivelle; Bioidentical Progesterone: Micronized Progesterone, Prometrium, Prochieve, Crinone. We searched for records containing any of these drugs. Any record whose drug name or decription contained the word ‘cream’ or ‘vaginal’ was discarded.There are many types of HT drugs available. Accounting for so many different types of drugs in the study would impair the study power. To solve this problem, the HT drug names were grouped into 8 categories. The names of drugs included in each category are as follows. Women were assigned to the following 9 HT protocols:CEE AloneMPA AloneOther Synthetic Estrogen AloneOther Synthetic Progesterone AloneBioidentical Estrogen AloneBioidentical Progesterone AloneCEE and MPAOther Synthetic Estrogen and Synthetic ProgesteroneBioidentical Estrogen and Bioidentical ProgesteroneSome women followed more than one HT protocol. Such women were assigned to the protocol of largest duration.Drug dosage was often not available. Furthermore, even if we could stratify the data by usage, there would likely not be enough users in any particular usage category to reach statistical significance. So, we have made the assumption that each of the 9 categories has a similar distribution of drug usage, and so our comparison is a comparison of HT protocols ranging over similar distributions. This is one of several weaknesses of a retrospective study relative to a case-control study or prospective study. However, a big advantage of a retrospective study is that is much less costly in terms of dollars and time. This matter is discussed more in the Discussion Section.p-values were computed using the one-sided log-rank test, where the null hypothesis is that the area under the survival curve for the control group is smaller than or equal to the area under the survival curve for the HT group. Additional studies were performed comparing HT protocols, and hazard ratios and p-values were computed in the same manner.Using Cox regression, hazard ratios, were computed for each of the HT protocols relative to the control group. Furthermore, This paper is a result of the PROTOCOL TITLE: A New Generation Clinical Decision Support System, which was approved by Northwestern University IRB #: STU00200923-MOD0006.CEE Alone, and Bioidentical Estrogen Alone provided a significant protective effect against breast cancer. The first result is consistent with the results of the WHI study. Other Synthetic Estrogens Alone is significantly associated with increased risk of breast cancer. Finally, CEE & MPA together is also significantly associated with decreased breast cancer risk. Bioidentical Progesterone Alone and combined Bioidentical Estrogen & Bioidentical Progesterone were both shown to be associated with increased risk of breast cancer, but the results were not significant. In this study, we mined 12,404 HT cases and 27,642 controls. The mean follow-up time for cases and controls are 15.4 (SD = 9.7) years and 17.8 (SD = 11.1) years. CEE & MPA is associated with less breast cancer risk than both other synthetics and bioidentical hormones. However, our data dates back to 1983, and bioidentical HT did not see substantial usage until after the results of the WHI appeared in the early part of the 21st century. Breast cancer diagnosis had improved by this time; the possibility exists that the population of women on bioidentical HT is skewed towards receiving better diagnostics, making breast cancer diagnoses appear more frequently in this group, even if they are not more likely to get breast cancer. To account for this possibility, we repeated the study using only women (cases and controls) who were first prescribed any medication after 01/01/2004. Other Synthetic Estrogen & Synthetic Progesterone is associated with increased risk of breast cancer. The results in CEE Alone remain significant for all data and for data after 01/01/2004, when we consider only cases that used HT at least 5 years. Furthermore, the results indicate that the protective effect increases with longer use. The results concerning Other Estrogen Alone again indicate that it is associated with increased risk, but the results are not significant. The results concerning Bioidentical Estrogen Alone remain that it is associated with decreased risk both based on all data and based on data after/01/01/2004, but the results are significant only for all the data. The results concerning combined usage of CEE & MPA remain that it is associated with decreased risk both based on all data and based on data after/01/01/2004, but the results are significant only for all the data. One oddity is that the results for combined Other Synthetic Estrogen & Progesterone reverse the association from increasing risk to decreasing risk. However, these results are not significant.Of interest is the effect on breast cancer of long-term usage of HT. Tables CEE & MPA directly to the combined use of Bioidentical Estrogen & Bioidentical Progesterone. CEE & MPA is significantly associated with less breast cancer risk than the bioidentical hormones, regardless of whether we include all women, or only women after 01/01/2004 and regardless of whether we consider all usage or only usage greater than 5 years. However, the results including only women after 01/01/2004 and with usage > 5 years are not significant. We compared the combined use of CEE Alone to be the best estrogen alone therapy as far as breast cancer risk, and CEE & MPA together to be the best combined therapy. In fact, Other Synthetic Estrogen Alone was associated with an increased risk in breast cancer relative to non-HT users. Bioidentical Estrogen Alone was associated with a decreased risk of breast cancer, but not as much as CEE Alone. In a head-to-head comparison, the combined use of CEE & MPA was shown to be significantly associated with less breast cancer risk than combined bioidentical hormones.We conducted a retrospective study investigating various HT protocols using 12,404 HT cases and 27,642 controls. We found bioidentical HT is associated with less breast cancer risk than CEE & MPA. However, it does support that Bioidentical Estrogen Alone is associated with less risk than Other Synthetic Estrogen Alone.The results of our study negated our hypothesis that CEE & MPA (p = 0.0002). Furthermore, the only bioidentical protocol that was shown to be significantly associated with decreased breast cancer risk was Bioidentical Estrogen Alone (p = 0.00076). Other bioidentical protocols were found to be associated with increased risk, but the results were not significant. However, we grouped all bioidentical estrogens and progesterone together. Anyone interested in forwarding the usage of a particular bioidentical HT protocol should conduct a study investigating that particular protocol before making further claims about its efficacy.As far as bioidentical hormones, our results significantly support that a woman’s chances of breast cancer are greater on bioidentical HT than they are on CEE & MPA to be associated with reduced breast cancer risk, whereas the WHI initiative showed the opposite. There are numerous possible explanations for this discrepancy. First, the WHI study concerned women with a prior hysterectomy. Second, our study concerned only women visiting Northwestern Medical Complex. There could be life style factors in this population that affect the outcomes. Finally, our study includes many women who more recently took CEE and MPA than the WHI. Perhaps the composition of these drugs has changed in this century.We found the combined use of CEE & MPA usage to affect breast cancer risk. For example, CEE & MPA might be protective only in ER positive women.Our study was retrospective, and has all the weaknesses of retrospective, including not making drug dosage a variable. Nevertheless, retrospective data are increasingly available, making studies like ours possible without the cost and time involved in case-control studies and prospective studies. Retrospective studies can be highly supportive of a result if many such studies obtain the same results. So, to substantiate our results, we need additional retrospective studies. First, we need studies using other databases to see if our results are duplicated. Second, we can perform retrospective studies investigating how patient variables such as HER2 and ER status interact with The question remains as to why CEE, with or without MPA, would be associated with reduced breast cancer. One possibility is as follows. Tamoxifen binds to estrogen receptors, but functions mainly as an antagonist, over-riding the agonist activity. CEE, which is a mixture of conjugated estrogens and steroid precursors, may function similarly with both agonist and antagonist elements. The differential effects mainly on bone and vascular tissue with estrogenic activities towards breast tissue argues for this dichotomous functionality.There is a great deal of hype concerning the advantages of bioidentical hormone replacement therapy, with no real evidence indicated it is safer. In this study, we conducted a retrospective study showing that it is indeed not safer as far as breast cancer risk. One should be dubious of the unwarranted claims that bioidentical is safer, and that either case/control studies or more retrospective studies are needed before making any claims one way or the other.S1 DatasetThe dataset label “Dataset1” contains all the data, and the dataset labeled “Dataset2” contains data only for these records pertaining to women who initiated drug usage after 01/01/2004.(ZIP)Click here for additional data file.


Community misconception of what causes cancer is an important consideration when devising communication strategies around cancer prevention, while those initiating social marketing campaigns must decide whether to target the general population or to tailor messages for different audiences. This paper investigates the relationships between demographic characteristics, identification of selected cancer risk factors, and associated protective behaviours, to inform audience segmentation for cancer prevention social marketing.n = 3301) are derived from Cancer Council New South Wales’ 2013 Cancer Prevention Survey. Descriptive statistics and logistic regression models were used to investigate the relationship between respondent demographic characteristics and identification of each of seven cancer risk factors; demographic characteristics and practice of the seven ‘protective’ behaviours associated with the seven cancer risk factors; and identification of cancer risk factors and practising the associated protective behaviours, controlling for demographic characteristics.Data for this cross-sectional study  and country of birth  were weak predictors of identification and of protective behaviours. Identification of a cancer risk factor as moderate/large was a significant predictor for five out of seven associated cancer-protective behaviours, controlling for demographic characteristics.These findings suggest a role for both audience segmentation and whole-of-population approaches in cancer-prevention social marketing campaigns. Targeted campaigns can address beliefs of younger people and men about cancer risk factors. Traditional population campaigns can enhance awareness of being overweight, alcohol consumption, and poor vegetable and fruit intake as cancer risk factors.The online version of this article (doi:10.1186/s12885-017-3540-x) contains supplementary material, which is available to authorized users. In Australia, around one-third of cancer cases are due to six modifiable lifestyle risks: tobacco use, ultraviolet radiation exposure, inadequate diet, overweight and obesity, alcohol consumption, and lack of physical activity . This reOne obstacle that challenges cancer prevention is the existing community scepticism that cancer can be prevented,  despite Compared to tobacco and sun exposure, there is much lower recognition of the carcinogenic effects of the other main established lifestyle causes of cancer, especially obesity, physical inactivity and alcohol –9. Sun eCommunity perceptions of avoidable cancer risk factors are a vital consideration in the development of communication strategies for cancer prevention . Public Social marketing applies commercial marketing strategies to modify social behaviours for the benefit of the community, including those behaviours related to public health , was calculated for these respondents. Fifty respondents (1.8%) with BMIs of less than 18.5 (‘Underweight’) were excluded from analysis. Respondents with BMIs of 18.5 to less than 25.0 were coded as ‘Healthy weight’ . Respondents with BMIs of 25.0 or greater were coded as ‘Overweight’ . These BMI ranges for ‘Underweight’, ‘Healthy weight’ and ‘Overweight’ follow Australian Healthy Weight guidelines /[(height in metres)ght’ n = 53, 59.2%Risk behaviour: AUDIT-C score of four or more for men and three or more for womenn = 1361, 55.3%). Scores of 0–3 for men and 0–2 for women were coded as ‘Lower-risk alcohol intake’ .The AUDIT-C (Alcohol Use Disorders—Identification Test—Consumption) score measures alcohol consumption on a scale from 0 to 12 and is designed to detect heavy drinking or alcohol abuse . QuestioRisk behaviour: Eating fewer than five serves of vegetables per dayn = 255, 10.3%) and ‘Less than 5 vegetable serves’ . This follows Australian government guidelines for recommended daily vegetable intake [There were 2474 answers to the question ‘If a serve of vegetables is equal to half a cup of cooked vegetables, one medium potato or one cup of salad, how many SERVES of vegetables do you eat each day, on average?’ with responses coded as ‘5 or more vegetable serves’  were coded as ‘2 or more fruit serves’ and 1057 (42.7%) were coded as ‘Less than 2 fruit serves’. This follows Australian government guidelines for recommended daily fruit intake .Seven logistic regressions were conducted to investigate the relationship between respondent demographic characteristics and identification of each of the seven cancer risk factors as either ‘Moderate/large’ or ‘None/slight’ (with ‘Don’t know’ responses excluded). A second set of logistic regressions was conducted to examine the relationship between respondent demographic characteristics and the seven ‘protective’ behaviours outlined in the previous section. Finally, a third set of regressions were conducted to determine whether respondents who identified a particular cancer risk factor as ‘Moderate/large’ were more likely to practise the associated ‘protective’ behaviour than were respondents who identified the risk factor as ‘None/slight’, controlling for demographic characteristics.Figures Figures p-values in the second column, with p-values of less than 0.05 considered to be significant. Significant values are shaded.The relationship between respondent characteristics  and odds of identifying each cancer risk factor as ‘Moderate/large’ as opposed to ‘None/slight’ are shown in Table Controlling for other factors, women were more likely than men to identify UV exposure, smoking, passive smoking, and drinking alcohol as moderate/large cancer risk factors. In comparison to respondents aged 18–29 years, those aged 50 years and over were more likely to identify five of the seven items as moderate/large risk factors. Those aged 30–49 years were less likely than those aged 18–29 years to identify being overweight as a moderate or large risk factor, but more likely to identify not eating enough vegetables and not eating enough fruit. Those living in regional areas of NSW were less likely than those resident in metropolitan area (Sydney) to identify being overweight as a moderate/large cancer risk factor. Those with post-school education were more likely than those with a Year 10 education or less to identify smoking as a moderate/large cancer risk factor, while those educated to Year 11 or 12 were less likely to identify being overweight. No significant results were found by country of birth.Table Those living in regional areas of New South Wales were more likely to practise sunsafe behaviour and to eat five or more daily vegetable servings, but less likely to be within the healthy weight range. Respondents with post-Year 10 education were more likely to be non-smokers and to avoid passive smoke, those with Year 11 or 12 or a university degree were more likely to be within the healthy weight range, and those with a university degree were more likely to consume two or more servings of fruit daily. Respondents born outside Australia were more likely to be within the healthy weight range and to eat two or more daily fruit serves.Table Risk-factor identification as moderate/large was the greatest correlate for three of the seven ‘protective’ behaviours  and was significant for two others (‘Sunsafe’ and ‘2+ fruit serves). It was not a significant correlate for ‘Healthy weight’ nor ‘5+ vegetable serves’.We ran other logistic-regression models to test whether identification of a cancer risk factor remained correlated with its associated ‘protective’ behaviour even when controlling for identification of other risk factors. We found that only identification of UV exposure was associated with sun protective behaviour; identification of none of the other six cancer risk factors was associated with sun protective behaviour. Similarly, only identification of drinking alcohol as a risk factor was associated with lower-risk alcohol intake. Identification of both smoking cigarettes and passive smoking as risk factors were associated with being a non-smoker and avoiding passive smoking, but not any other identifications. Broadly speaking, this indicates that behaviour and beliefs about risk and are specific to those single domains.No risk factor identifications were correlated to being a healthy weight nor with consuming five or more vegetable serves daily. In contrast, identification of passive smoking, being overweight, drinking alcohol, not eating enough vegetables, and not eating enough fruit were all significantly associated with eating two or more serves of fruit daily.This is one of the first studies to use multivariate analysis to examine the association between awareness of cancer risk factors and health behaviours across seven lifestyle-modifiable areas . Increasing age was a significant predictor of all cancer protective behaviours and a range of demographic characteristics were associated with awareness and behaviours related to other cancer risk factors. After controlling for demographic factors, except for healthy weight and vegetable consumption, awareness of cancer risk was a significant predictor of the associated cancer protective behaviour.p-value of 0.001 (not shown).The lack of correlation for healthy weight may have been because of the complexity of contributors to maintaining a healthy weight, and lack of self-awareness of weight status , 21. TheCancer prevention activities require a range of complementary approaches. There are a plethora of health promotion models and theories that guide many successful public health campaigns, and risk factor awareness is known to be important in influencing attitudes and intentions , 23. ParOur findings suggest a role for audience segmentation and whole-of-population approaches in social marketing campaigns for cancer prevention. The link between each risk factor and associated behaviour was consistently significant amongst participants aged 50 years or more but this was not the case for other groups. Women, older people and to a lesser extent those with higher education (see Table Our study had limitations. The survey was only conducted in one Australian state, and out of 30,179 initial email invitations, 10.9% of invitees completed the survey. While this may imply a bias, previous research has shown that response bias is minimised when considering correlation between variables in multivariate models,  as is thStudy participants self-reported their behaviours. Thus there is the possibility of social desirability bias; that is, that respondents over-reported the positive behaviours measured. We also do not know what people had in mind when they identified risks as none, slight, moderate or large. We did not measure physical activity risk or behaviours, nor ask about dietary fibre and red and processed meat as other factors associated with cancer risk. Further study including these data would enhance our understanding of the relationship between these factors and healthy weight and nutrition behaviours.This study shows that there is high community awareness of UV exposure, smoking, and passive smoking as cancer risk factors. There is lower awareness of the link between cancer and being overweight, alcohol intake, and low fruit and vegetable consumption. Males and younger respondents are less likely to practise the behaviours that help protect against cancer. Since there is a strong correlation between awareness of cancer risk factors and practising the associated protective behaviours, these findings suggest a role for both audience segmentation and whole-of-population approaches in cancer-prevention social marketing campaigns. Targeted campaigns can address beliefs of younger people and men about cancer risk factors. Traditional population campaigns can enhance awareness of being overweight, alcohol consumption, and poor vegetable and fruit intake as cancer risk factors.


The purpose of this study was to evaluate long-term risk of subsequent breast cancer and mortality among women diagnosed with in situ breast cancer, in relation to family historyThe clinical behavior of in situ breast cancer between 1980 and 2004. We used standardized incidence ratios (SIRs) to measure the relative risk of subsequent invasive or contralateral in situ breast cancer and standardized mortality ratios (SMRs) for relative risks of death.Using the population-based Swedish Multi-Generation and Cancer Registers we identified 8111 women diagnosed with in situ breast cancer, the cumulative 10-year and 20-year risk for subsequent contralateral or ipsilateral invasive cancer was approximately 10 % and 18 %, respectively. The risk of subsequent invasive breast cancer was increased more than 4-fold (SIR 4.6 (95 % CI 4.2 − 4.9)) among women with in situ breast cancer as compared to women in the general population and the risk of contralateral in situ breast cancer was increased almost 16-fold (SIR 16.0 (95 % CI 13.2–19.1)). Having a family history of breast cancer increased the risk of contralateral invasive breast cancer by almost 50 % (incidence rate ratio 1.5 (95 % CI 1.0–2.0)). Women under forty years old at diagnosis, without family history, had a 7-fold increased risk, and those with a family history had a 14-fold increased risk for subsequent invasive breast cancer with SIRs of 7.2 (95 % CI 4.8–10.5) and 14.3 (95 % CI 7.4–25.0), respectively. The overall risk of death in women with in situ breast cancer was significantly increased by 30 % compared to the general population but was highly dependent on the occurrence of a second invasive cancer event (SMR 1.3 (95 % CI 1.2–1.4)).Among women diagnosed with in situ breast cancer, a positive family history increases the risk of contralateral invasive breast cancer by almost 50 %. The risk of subsequent invasive breast cancer and mortality is substantially higher in younger women, which should be taken into account when planning their treatment and follow up.Among women with The online version of this article (doi:10.1186/s13058-016-0764-7) contains supplementary material, which is available to authorized users. Several factors have been associated with invasive recurrences, including patient characteristics [Women with l breast –12. Morepulation –9, 13–16eristics , 5, 8, teristics , 5, 17 aeristics , 18, 19.eristics –22.in situ breast cancer is considered to be at most only marginally increased, but remains less well-characterized and, with few exceptions, studies are often limited by short follow up and non-population-based designs [in situ breast cancer, in relation to family history.The risk of death from breast cancer in women diagnosed with  designs , 23. In We combined data from the Multi-Generation Register  with the Swedish Cancer Register, the Cause of Death Register and the Total Population Register for data on emigration. These registers were merged using the unique national registration number that all Swedish citizens receive at birth or immigration. Linkages provide complete follow up with of cancers, vital status, date and cause of death, and dates of immigration and emigration. It also provides links between children and parents through their respective national registration numbers.in situ breast cancer from 1980 onwards [in situ breast cancer is reported as a new event, as are new in situ breast cancers in the contralateral breast. Local relapses are not recorded, neither are new ipsilateral in situ events. The register does not distinguish ductal from lobular in situ breast cancer before 1990 and contains no information on tumor stage or treatment. Ipsilateral in situ breast cancer was excluded due to the increased probability of being underreported in women with previous in situ breast cancer.The Swedish Cancer Register is a nationwide, population-based register that contains information on virtually all diagnosed cancers in Sweden since 1958 and is considered almost complete for invasive cancer –26 and oin situ breast cancer. Women with any previous invasive or in situ breast cancer were excluded, as were women with invasive breast cancer diagnosed concurrently with the first in situ breast cancer. Family history of breast cancer was defined as having at least one first-degree relative diagnosed with invasive breast cancer at any point in time. For all women, we collected information on family history of breast cancer and all second primary cancers including type of cancer, laterality and date of diagnosis. Because of incomplete information on laterality and in situ breast cancer registration prior to 1980 [in situ breast cancer diagnosed in the period 1980 to 2004. Our final study population consisted of 8111 women in the Swedish Multi-Generation Register, diagnosed with first in situ breast cancer between 1 January 1980 and 1 January 2005.Thus, we defined subsequent breast events as ipsilateral or contralateral invasive or a contralateral  to 1980 , we restin situ breast cancer), all women were followed from the date of their first in situ breast cancer diagnosis and continued until a subsequent breast cancer, emigration, death, or end of follow up, whichever came first. We estimated standardized incidence ratios (SIRs), i.e., the ratio of the observed to the expected number of breast cancers , as a measure of relative risk. The expected number of subsequent breast cancer events was calculated as the product of the person-years accumulated by women with in situ breast cancer by the age-specific and calendar-period-specific incidence of unilateral in situ/invasive breast cancer in the general population in the Swedish Multi-Generation Register.To estimate the risk of a subsequent breast event  among patients with in situ breast cancer to the risk among the general population, and were stratified by family history of breast cancer. SIRs of subsequent invasive breast cancer were calculated for the calendar period of the first diagnosis, age and time since first diagnosis. Poisson trend tests for monotonic trend of SIRs across calendar period, age and time since first diagnosis was performed [in situ breast cancer to estimate the independent effects of age, year and time since diagnosis and effect of family history on the risk of ipsilateral or contralateral invasive or contralateral in situ breast cancer.For all estimates for the contralateral breast, the background rate of erformed . We usedAs background rates of breast cancer vary considerably by age we also estimated excess additive risks (EARs), as the difference of observed numbers of subsequent invasive breast cancers and the expected numbers in the general population in the Swedish Multi-Generation register, as a measure of absolute risk for subsequent invasive cancer. EARs were estimated using a univariate Poisson model with an identity link function and the expected number of cases as the offset. The likelihood ratio test was used to calculate 95 % confidence intervals (CIs). The cumulative incidence was estimated using the life-table  method.in situ breast cancer diagnosis and type of subsequent breast event. For overall SMRs, subjects were followed from the date of the first in situ breast cancer diagnosis until the date of emigration, death, or end of follow up, whichever came first. In contrast, in the estimates of death by type of subsequent breast event, follow up was started at the diagnosis of that particular event. We calculated 95 % CIs assuming a Poisson distribution for the observed number of cases. All data preparation and analysis was done using the SAS statistical package, version 8.2 or higher . The regional ethical committee in Stockholm approved the study.The standardized mortality ratio (SMR), i.e., the ratio of the observed to the expected number of deaths, standardized by age and calendar period, was used as a measure of relative mortality. The expected number of deaths was calculated from the general population in the Swedish Multi-Generation register. SMRs were also stratified by family history, age at first in situ and 768 ipsilateral or contralateral invasive breast cancers). The proportion of subsequent breast events was similar in women with and without a family history . The average time from the first in situ breast cancer diagnosis to a second breast event was overall 5.6 years +/− 4.6 years.Patient characteristics are listed in Table in situ breast cancer. The risk of a subsequent ipsilateral or contralateral invasive breast cancer was increased more than fourfold (SIR 4.6 (95 % CI 4.2–4.9)) among women with in situ breast cancer as compared to women in the general population. The risk of contralateral in situ breast cancer was almost 16-fold increased ). Poisson regression analyses showed that women with a family history of breast cancer had almost 50 % increased risk of contralateral invasive breast cancer, compared to women without a family history of breast cancer (adjusted IRR 1.5 (95 % CI 1.0–2.0)).Table in situ breast cancer, the cumulative 10-year and 20-year risk of subsequent contralateral or ipsilateral invasive cancer was approximately 10 % and 18 %, respectively, while the cumulative 10-year and 20-year risk of subsequent contralateral in situ breast cancer was 1 % and 2 %, respectively  in 1980–1984, vs. 5.0 (95 % CI 3.9–6.5) in 2000–2004 (P for trend <0.001). In contrast, women with a family history did not have increased risk of subsequent invasive breast cancer over the study period  and 4.4 (95 % CI 4.1–4.8), respectively . Among women below 40 years, and who had a positive family history, the risk of subsequent invasive cancer was more than 14 times higher than in the general population, with a SIR of 14.3 (95 % CI 7.4–25.0).Overall, the relative risk of subsequent invasive breast cancer was almost twice as high for women under 40 years old at the first in situ disease in women with a positive family history.Given that the background rates of breast cancer are highly age-dependent, we estimated the EAR in relation to age at diagnosis. While the relative risk of a subsequent invasive breast event decreased with increasing age, in both women with and without a family history of breast cancer, the overall EAR was significantly increased but was similar in women below 40 years of age at diagnosis  as compared to women over 40  . In women with no family history there was a significant decline in both the relative and absolute risk over time, but this was not observed in women with a family history ). In contrast, women who were diagnosed with an invasive breast cancer event after in situ breast cancer were twice as likely to die as compared to women in the general population (SMR 2.1 (95 % CI 1.7–2.4)), with no significant differences between women with and without a family history of breast cancer.The overall risk of death in women with nt Table . Women win situ breast cancer was increased in women with a family history (SMR1.4 (95 % CI 1.2–1.8)) and in women without family history (SMR 1.3 (95 % CI 1.2–1.4)). Given that deaths were rare at younger ages, we compared mortality among women above and below age 50 years. Women below age 50 years at the first in situ breast cancer diagnosis and who were diagnosed with a second invasive cancer, had significantly higher mortality as compared to women over 50 years at diagnosis . The laterality of the second invasive event did not influence the risk of death significantly.The overall risk of death following in situ breast cancer have a considerably increased risk of invasive breast cancer and contralateral in situ breast cancer, compared to women in the general population, with young women facing the highest risk. Having a positive family history increases the risk exclusively for a contralateral invasive breast cancer by 50 % compared to not having a family history of breast cancer. The increased risk of invasive cancer persists over time, and at fifteen years after diagnosis the risk is still three times higher than in women in the general population. Meanwhile, the mortality for women with in situ breast cancer is the same as the general population, as long as invasive cancer does not occur.In this large population-based cohort, with data from nationwide, high-quality registers, we demonstrate that women diagnosed with In women with a positive family history, the risk of contralateral invasive breast cancer was more than four times as high as for women in the general population and almost 50 % higher compared to women with no family history of breast cancer. The observed increased risk is approximately twice as high as the risk of breast cancer that is faced by women without previous breast cancer, who have a positive family history of breast cancer. There are methodological issues that may account for these differences, because our estimates assume only one breast is at risk, with a corresponding lower expected rate.in situ cancer, as compared to women with no family history, is intriguing. We speculate that women with a positive family history were likely more prone to choose mastectomy than those without family history, which would reduce the risk of an ipsilateral cancer in these women. The reduced risk may also be a reflection of heterogeneity of the in situ breast cancer phenotype. Additional stratification into one, two or even three affected first-degree members to better quantify the hereditary component may have allowed a deeper understanding of these results.Two meta-analyses of familial risks of breast cancer report the relative risk associated with having a first-degree relative with breast cancer as 2.1 and 1.8, respectively , 29. TheRegardless of family history, women under 40 years of age at diagnosis had a significantly higher risk of subsequent invasive breast cancer compared to women above 40 years. These young women would experience an absolute excess risk ranging from about 8 events per 1000 person-years to as many as 15 events per 1,000 person-years depending on family history; this absolute excess risk decreases with increasing age only in women with a positive family history. Given that a younger woman with both high risk of a subsequent event and a longer life expectancy, which translates to higher cumulative risk, mastectomy may be considered to a greater extent in this patient population.in situ breast cancer changed during the study period, from symptom-detected to screening-detected, with better prognosis for the latter [The increased relative risk of subsequent invasive breast cancer by almost 60 % from the period 1980–1984 to the period 2000–2004, exclusively in women with no family history, may be related to a combination of screening and treatment patterns. A nationwide mammography screening program was introduced during the study period, which had complete national coverage by 1997 . Thus, te latter . Howevere latter . In compin situ breast cancer during the study period was performed according to regional and national guidelines [in situ breast cancer has been recommended whenever feasible. Several randomized trials, including the Swedish National DCIS study, have unanimously shown a decreased rate of ipsilateral in situ or invasive breast cancer recurrence through the addition of adjuvant radiotherapy after breast-conserving surgery [in situ [Treatment for idelines . Surgery surgery , 33–37, [in situ . Howeverin situ breast cancer, the risk for an invasive breast cancer was still almost three times higher than for women in the general population. This indicates that women diagnosed with in situ breast cancer have a lifelong increased risk, which needs to be taken into account when planning their follow up.During follow up, women with no family history of breast cancer had a gradually decreasing risk of subsequent invasive breast cancer with time since diagnosis. However, 15 years after the first in situ breast cancer as long as there was no second invasive event, but in women with a second invasive breast cancer the risk of death was doubled. There were no significant differences in mortality between women with and without family history of breast cancer. Young age of onset was an important predictor of death for women with in situ disease due to an increased risk for second invasive cancers and thus a substantially higher mortality, which should be taken into account when planning their treatment and follow-up.Overall, there was no increased risk of death for women with in situ some studies suggest that the benefits are even greater [in situ, and for lobular cancer in situ surgical or adjuvant treatment is still not recommended. One must weigh the benefits of endocrine therapy in reducing second breast cancer events against an increased risk of side effects. In a systematic review and meta-analysis the number needed to treat in order for Tamoxifen to have a protective effect against all breast events was 15 and it did not reduce the risk of all-cause mortality [In women with elevated risk of breast cancer, studies have shown that adjuvant endocrine therapy with a selective estrogen receptor modulator or an aromatase inhibitor reduces the risk by 40–50 % –40, and  greater , 41, 42.ortality .in situ breast cancer.Strengths of the current study include the population-based design, the large sample size, complete follow up and unbiased ascertainment of family history, cancers and death. To the best of our knowledge, this is the largest study to assess the impact of a positive family history of breast cancer on risk and mortality after in situ and lobular carcinoma in situ. With this stated, a previous Swedish case–control study has shown that the risk of a subsequent invasive breast cancer was equal after lobular and ductal carcinoma in situ breast cancer [in situ, family history does not increase the risk of invasive breast cancer [in situ and a positive family history, the risk estimates might be higher than shown in our study. During the study period, Sweden did not have a nationwide register on local recurrences and in the vast majority of regions, a second ipsilateral in situ breast cancer event was not reported. Therefore, new ipsilateral in situ cancer was not included in the study, as these events most probably would be underestimated.This study has a number of limitations. We have no information on the mode of detection, tumor grade or adjuvant treatment, and have not distinguished between mastectomies and breast-conserving surgery, or ductal carcinoma t cancer . One stut cancer , thus amin situ breast cancer, a positive family history of breast cancer increases the risk of contralateral invasive breast cancer by almost 50 %. The risk of subsequent invasive breast cancer and of mortality is substantially higher in younger women, which should be taken into account when planning their treatment and follow up.Among women with