We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo—DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists’ visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48–55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5–4.4) and a 3.6 (95% CI 1.4–9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists’ visual assessment as both point estimate and lower bound of 95% CI  were above the predefined visual assessment threshold (AUC 0.571). AUC for interval  cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists’ visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity. In addition, breast density assessment performed by radiologists (using Breast Imaging-Reporting and Data System-BI-RADs)4 is time consuming and prone to poor reproducibility, making its application in organized screening challenging.Mammographic breast density (BD) is the absolute amount or percentage of fibro glandular tissue in the breast. It is an established risk factor for breast cancer (BC) and an important determinant of screening sensitivity as it may hamper cancer detection in mammograms, i.e., the masking effect. Radiologist visual assessment, an area-based method, commonly used for judgment of breast density, is subject to several important limitations, such as high subjectivity and intra- and inter-observer variability6, or if they would benefit from a shorter mammography screening interval. Low reproducibility of mammographic density measures may result in conflicting recommendations and inaccurate risk stratification which could increase the probability of an interval BC.Consequently, there are important clinical and research repercussions. Breast density is being used to stratify women based on their breast cancer risk to decide if they need further imaging assessment in clinical trials, such as ultrasound or MRI7, WISDOM8 and My Personalized Breast Screening (MyPeBS)9. MyPeBS is an international randomized, multicentre study aimed to assess the effectiveness of a risk-based breast cancer screening strategy  compared to standard screening . BD evaluation plays a central role in the MyPeBS trial as mammographic density contributes to the risk assessment in the intervention arm and it determines whether women should receive supplemental ultrasound.The potential of personalized BC screening based on individual risk over universal “one-size fits all” screening has been recognized and several randomized trials have been launched to address this issue, such as Tailored Breast Screening Trial (TBST)13; these include approaches based on area-based and volumetric measures, and in some cases include mammographic pattern, however variations in assessment methodology make direct comparisons of risk prediction challenging19. DenSeeMammo (DSM) has been validated for density assessment and shows a higher degree of agreement than that reported in studies on other vendors’ automated volumetric assessment tools20. However, the software has not yet been validated for prediction of breast cancer risk and for assessing the risk of masking .Several semi-automated and fully automated methods for reproducible assessment of breast density have been developed and evaluatedThe primary aim of this study was to evaluate a recently developed automated system for breast density assessment (DenSeeMammo) and its ability to stratify breast cancer risk, by comparing discriminative accuracy for BC risk prediction to a non-inferiority threshold defined on the basis of visual assessment by radiologists. The secondary aim was to estimate how the fully automated BD assessment is able to measure the masking effect.21, Florence study17 and PROCAS case–control study18, with a total of 14,267 women 48–55 years old, of whom 322 developed breast cancer. A summary of studies included is provided in Supplementary Table 1 while the flowchart of study participants is presented in Fig. This study represents a pooled analysis of images collected within the RETomo trialA cohort study including 15,952 women who had their first screening digital mammography aged 49–54 years old during the period 2006–2013. Women were routinely reinvited after 2 years, and the study follow-up lasted 2.5 years. Only 5359 2D full-field digital mammography images were available for breast density analysis since some images were performed with mammography equipment (GE 2000D) which is not supported by DenSeeMammo software.A cohort study including more than 27,000 women aged 45–70 years old attending screening between March 2014 and July 2017 and who had already participated in at least one round of the Reggio Emilia screening program. Women aged 45–49 years were routinely reinvited after 1 year and women aged 50–74 years after 2 years; the study follow-up lasted 2.5 years. Of the 48–55 year old women density assessment was available in 8332 women.A cohort study initially including 44,658 women with full field digital mammograms and attending both first and subsequent screening rounds from October 2009 to March 2015. Women are routinely reinvited every 3 years. A subset of 576 women in whom breast cancer was detected at time of entry to the study plus controls  in the age range 48–55 years were available for breast density assessment with automated software in this study.No exclusion criteria other than those that were study-specific were applied. For women who had several mammograms, the first mammogram was analysed. Pooled analysis was conducted on individual anonymized data.For all three studies local Ethics Committee approval had been obtained and informed consent given by all woman participating in the relevant study. Ethics approval for the PROCAS was through the North Manchester Research Ethics Committee (09/H1008/81). ReTomo was approved by the provincial Ethical Committee . The Ethics Committee of the Florence district gave their approval for the Florence study on 12 September 2017 (n.11630_oss). All methods involving human participants were performed in accordance with the Declaration of Helsinki.Cranio-caudal (CC) and mediolateral oblique (MLO) pairs (left and right breast) of 2D full-field digital mammography (FFDM) of all women recruited were obtained from Picture Archiving and Communication Systems (PACS) and processed by DSM. Ten women were excluded as it was not possible to obtain suitable images. Images were from different vendors and systems . Detailed description of image acquisition is provided in Supplementary Table 4. With DenSeeMammo, all assessments are based on BI-RADS 5th Edition which takes into account percent density and density distribution in the breast to reflect risk of masking4. DenSeeMammo received 510(k) FDA clearance in 2017 and in 2018 for automatic breast density evaluation on GE and Hologic equipment.DenSeeMammo 1.2 —is a Food and Drug Administration (FDA) approved, fully automated software for assessing breast density providing a BI-RADS category density grade. DenSeeMammo handles processed ‘for-presentation’ images extracted from DICOM files as input . It provides results on a per patient basis, using the maximum density category of the two breasts. The method is based on a comparison with databases containing images previously scored by the Mammography Quality Standards Act (MQSA) radiologists using BI-RADS 5th EditionCancers included in the analysis were: (1) prevalent cancers, i.e., cancers detected at inclusion to the study, (2) incident cancers i.e., cancer not detected at the study inclusion mammogram but occurred at a subsequent screening round including (3) interval cancers i.e., cancer diagnosed after a negative screening mammogram and before the next screening mammogram. The main endpoint is incident cancer . We also report prevalent cases and all cases , since including only incident cancers in women undergoing screening overestimates the ability of density to predict cancer risk. A substantial proportion of undetected prevalent cancers in women with dense breasts at baseline become symptomatic or detectable later during follow-up, while in women with fatty breasts, they are more efficiently detected by the baseline mammography.The main analysis was the Area Under the Receiver Operating Characteristic curve (AUC) for DSM software’s prediction of incident breast cancer risk, calculated from all the women for which we had available 2D mammography suitable for DSM assessment. This analysis was performed only on incident and interval cancers, because in its practical application in screening, risk stratification is meaningful only for women who have not been diagnosed with a cancer.We also report the odds ratios for incident, interval, prevalent and all cancers. The association with all cancers may better reflect the strength of the aetiological link between density and breast cancer risk as it is not affected by the bias of excluding more efficiently prevalent cancers in fatty breasts than in dense breasts. The difference in the strength of association between prevalent and interval cancers can be interpreted as an indirect measure of the software’s ability to identify the masking effect of breast density: if the relative risk of having interval cancers in dense breasts vs. non-dense breasts is higher than the relative risk of having prevalent screen detected cancers, this means that the breast density assessment was able to identify part of the masking effect due to breast density.Descriptive statistics were used to present the distribution of women and cancer cases within centres and breast density categories. The association between breast density assessed by DSM and BC risk was estimated using logistic regression and odds ratios (OR) with 95% confidence intervals (95% CI). Due to the small number of cancers in BI-RADS A category, the sum of the two lowest BI-RADS categories (A and B) was considered as a reference category. To take into account the different study designs of the PROCAS case–control study and the other two cohort studies, for the analysis of risk of all cancers on pooled data, we applied a sampling probability weight to the controls of the PROCAS study in a ratio 1:15 compared to cancers, as estimated from the risk observed in the underlying cohort of the PROCAS study. Given that models adjusted for age and study centre yielded similar results as crude models, the results of the crude model are presented.As a single measure of accuracy, i.e., ability of the automated system to discriminate between cases and controls, area under the receiver operating characteristic curve (ROC)—AUC with 95% CI, according to binomial exact distribution, was computed based on the four BI-RADS categories.22. In the Florence study, consisting of 15,952 mammograms classified by radiologists’ visual assessment in the four BI-RADS categories, the AUC for predicting incident cases was 0.579 .23. Expected value of the software AUC in the Florence study was 0.637 17. With an expected sample size of about 300 cancers and an underlying cohort of about 25,000 women aged 48–55, applying the same BI-RADS specific risk of cancer observed in the Florence study17, the power to exclude a non-inferiority threshold of 0.571 was expected to be over 99%.For the power calculation we used AUC of VolparaDensity’s automated system  from the Florence study as an expected value of software AUC. VolparaDensity is a commercial volumetric breast density method that operates on ‘for processing’ mammogramsIn total, 14,267 mammograms from the same number of women, aged 48–55 years old, were available for density assessment, out of whom 322 had cancer  , out of which 35 were interval cancers (26 in RETomo and 9 in Florence study) Table . The disAbility to predict risk for both incident  and interval cancers  was better for DSM than for radiologist visual assessment as both point estimates and lower bounds of 95% CI were above the predefined visual assessment threshold (0.571)  than for those in the two lowest BI-RADS categories , while the risk for interval cancer was almost four times higher for women in the abovementioned categories   and the RETomo study   to predict risk of breast cancer in the pooled analysis of three European studies. Positive association of automatically assessed breast density and risk of incident, interval (masking effect) and prevalent breast cancers and all cancers was observed in the pooled data, and no important heterogeneity was observed among the studies.17. It was also similar to those reported previously and estimated by using Volpara software19. Although these studies found positive associations of both automatically and visually assessed BD with breast cancer risk, their automated systems were inferior to BI-RADS visual assessment in the discrimination of cancer risk. Astley et al. also showed that the average of two radiologists estimates of breast density recorded on Visual Analogue Scales (VAS) was a stronger predictor for breast cancer, compared to four different automated systems18. In our study overall DSM accuracy outperformed the single radiologists’ visual breast density assessment, suggesting that DSM or other software with similar performance could be used in screening, thereby overcoming some of the organizational barriers that impede the radiologist’s visual assessment without the risk of decreasing accuracy.Accuracy of DSM in breast cancer risk prediction was acceptable in our study with an AUC (0.589), not inferior to, and actually slightly higher than, the radiologist’s visual assessment observed in Florence24, the most studied and one of the best performing quantitative volumetric assessment method18, other studies performed on the same set of mammographic images with direct comparison with other automated systems are necessary to answer whether DSM is a valid alternative to other automated systems.This study did not aim to provide direct comparison with other automated software and Volpara was used as the only automated software available for the power calculation. Although a recent study demonstrated that DSM has a comparable or even better degree of agreement with expert radiologists’ visual assessment than Volpara18 and a HR of 8.3 in the study by Wanders et al.25. Considering only incident cases overestimates the true prediction performance of the software; of note is that both studies are case–control studies and for the latter, density was evaluated at the time of cancer detection, augmenting additionally the ability of the software to predict breast cancer. When evaluating risk of overall cancers, i.e., considering also prevalent cases, our results (OR 1.8) are in agreement with results of studies utilising other automatically assessed breast density software. In particular, prediction of cancer was very similar to the estimates of Puliti et al. who reported 2.0 times higher risk of invasive BC in women within the highest breast density category compared to those in the three lower categories17, as estimated by VolparaDensity software in the entire Florence cohort. It must be noted that only a small part of available cancers and mammograms were included in both of the density evaluation studies, because only one third of the Florence cohort used for validating Volpara software was also suitable for processing by DSM. With the limitation of the difference in age of the participants, screening frequency and breast density distribution, other studies reported similar results for the association between fully automatically assessed BD and risk of cancer, with OR ranging from 1.7 to 2.3 when using Volpara19, 1.9 and 3.9 Quantra16, 2.2 Densitas18 and 2.5 for a method using ImageJ14.As expected, excess risk was higher for incident cancer (OR 2.6) than for overall cancers (OR 1.8) in our study. Data for incident cancers were less consistent between studies ranging from an OR of 2.8 in the PROCAS study26 and lower than estimates of Destounis et al. (RR = 4.0)27, Wanders et al. (RR = 4.4 in the highest BD category vs. 0.7 in the lowest BD category)28 and Puliti et al. (RR = 5.0)17. It is hard to distinguish to what extent the interval cancer rate occurred truly due to masking and what part is attributed to rapidly evolving cancers, especially without information on size, grade and molecular subtypes of cancers. Nevertheless, our data and the previous literature clearly show that the proportion of interval cancers is higher in dense breasts than in fatty breasts28. One bias affecting our study is that part of the REtomo cohort, i.e., women aged 48–50, were invited for rescreening after 12 months. The chance of having an interval cancer in the first year after a negative screen is low and it is worth noting that dense breasts were slightly more prevalent in women that have been re-invited after 12 months . Odds ratio for interval cancer in our study (OR 3.6) was somewhat higher than that of Moshina et al. (OR 2.9)Our study has certain limitations which should be addressed. We defined the non-inferiority threshold using the radiologist’s visual assessment of breast density as standard. This was decided because routine evaluation by one radiologist is available in clinical practice in most screening programs. On the other hand, this standard is less accurate than that used in many studies, i.e., the judgement by a panel of radiologists; this latter option is a better gold standard with which to compare and has higher accuracy than what is usually available in routine practice. Furthermore, the threshold has been defined based on a single centre cohort, which may not be representative of performance in other centres. While in the PROCAS study women with breast cancer and controls were matched for major confounding variables, such as parity, body mass index and menopausal status, in the RETomo and Florence studies this was not possible, which hinders us from drawing conclusion about an association between BD and cancer risk and from generalising our results to other centres. Although the software works on “for presentation” images and is multivendor, not all the stored images were suitable for evaluation.20, but not to optimize risk prediction. This choice guarantees comparability with previous studies and makes meaningful the choice of the non-inferiority threshold but could be reconsidered when applying BD for risk stratification, because continuous values contain valuable additional information.Both the metrics which we used for evaluation (AUC and OR computed in logistic regression) are very sensitive to the breast density threshold used. The DSM adopted threshold is calibrated to reproduce the distribution of BI-RADS categories in the general screening populationFinally, our study included only women of peri-menopausal age, when BD changes rapidly, decreasing in most women as they age; how much the predictivity observed in this age group can be generalised to older women should be assessed in a further study. Another potential selection bias could have been introduced by selecting only available images . Although this selection did not occur at random, these reasons are less likely to be associated with breast density and outcome in a population-based screening programme.DenSeeMammo, a new fully automated software for breast density assessment, is non inferior to radiologist’s visual assessment in the prediction of breast cancer risk. Automatically assessed breast density was strongly associated with incident and more strongly with interval cancers, indicating the ability to capture the masking effect of BD.Supplementary Information.


Long‐term breast cancer survivors are at risk for cardiotoxicity after treatment, but there is insufficient evidence to provide long‐term (~10 years) cardiovascular disease (CVD) screening recommendations. We sought to evaluate a tri‐modality CVD screening approach.This single‐arm, feasibility study enrolled 201 breast cancer patients treated ≥6 years prior without CVD at diagnosis. Patients were sub‐grouped: cardiotoxic (left‐sided) radiation (RT), cardiotoxic (anthracycline‐based) chemotherapy, both cardiotoxic chemotherapy and RT, and neither cardiotoxic treatment. Patients underwent electrocardiogram (EKG), transthoracic echocardiogram with strain (TTE with GLS), and coronary artery calcium computed tomography (CAC CT). The primary endpoint was preclinical or clinical CVD.2 test, p = 0.58/p = 0.15).Median age was 50 (29–65) at diagnosis and 63 (37–77) at imaging; median interval was 11.5 years (6.7–14.5). Among sub‐groups, 44% had no cardiotoxic treatment, 31.5% had cardiotoxic RT, 16% had cardiotoxic chemotherapy, and 8.5% had both. Overall, 77.6% showed preclinical and/or clinical CVD and 51.5% showed clinical CVD. Per modality, rates of any CVD and clinical CVD were, respectively: 27.1%/10.0% on EKG, 50.0%/25.3% on TTE with GLS, and 50.8%/45.8% on CAC CT. No statistical difference was seen among the treatment subgroups  breast cancer survivors; over half had clinical findings warranting follow‐up and/or intervention. This provides early evidence that cardiac screening may be of value to a wider group of long‐term breast cancer survivors than previously recognized. A single patient may receive chemotherapy, surgery, radiation, HER‐2 targeted therapy, and up to a decade of hormonal therapy. Non‐chemotherapy, systemic treatments, such as HER‐2 targeted therapy , and aromatase inhibitors can adversely impact the heart, however, are not individually considered long‐term “high/increased risk” by current guidelines.Given the range of late cardiotoxicities known to occur after breast cancer treatment, we hypothesized that a multi‐modality screening approach would be needed. Three non‐invasive, minimal risk screening studies  were chosen. We hypothesized that these tests would be feasible and appropriate for widespread screening (if ultimately indicated).22.1This study was conducted under an IRB‐approved protocol and registered at ClinicalTrials.gov (NCT03235427). Using our institution's cancer registry, patients diagnosed with in‐situ or invasive breast cancer between 2004 and 2011 were identified. Overall, 1,144 were mailed brochures, 916 of these called for potential enrollment, 531 screened negative or were unavailable after 3 attempts, and 299 found eligible and willing, and 201 participated. Inclusion required aged 18–65 at diagnosis, ≥6 years since diagnosis, and no history of heart disease at diagnosis.2.2Enrollment occurred between 6/2017 and 7/2018, all patients provided written informed consent. Of the 201 enrolled, 200 had sufficient data for analysis. The enrollment aim was 200 patients on a 2:1 radiated to non‐radiated ratio over 2 years. Left‐ and right‐sided radiation patients were enrolled in roughly equal distribution (1:1). We hypothesized left‐sided, direct radiation to the heart would have a different risk than right‐sided treatment based on radiation dosimetry with standard breast/chest wall tangential field arrangements employed at our institution during the participants' treatment interval; internal mammary nodes (IMNs) were not standardly treated. We aimed to enroll 25% minority participants. Enrollment was limited to 18–65 years old at diagnosis to reduce confounding from age‐associated CVD. Recruitment began with patients with longer follow‐up (≥10 years) which created a non‐equal distribution around the mean, thus, the median is used in describing the cohort.2.32 (based on screening guidelines at the time of the study design), (2) radiation treatment of the left breast/chest wall, (3) both 1 and 2.During screening, patients provided information regarding oncologic treatments. In almost all cases, medical records were reviewed by study staff with the patient on the phone. Treatments included: surgery, radiation, chemotherapy, hormonal therapy, HER‐targeted agents , and other treatments . High‐risk cardiotoxic treatments were defined as: (1) anthracycline‐based chemotherapy of greater than three cycles completed and/or known total dose of ≥300 mg/m2.4CVD was defined as prior myocardial infarction, angina, heart failure, valvular disease, wall motion abnormality, CAD, arrhythmia, pericardial disease, cardiac surgery, percutaneous coronary intervention, pacemaker or defibrillator implantation or arrhythmia ablation. Patients with cardiac risk factors without CVD at diagnosis were eligible. Potential CVD risk factors included: age at diagnosis, age at imaging, time interval from diagnosis to imaging, hypertension, smoking status, obesity, recurrence of breast cancer, chronic kidney disease, targeted therapy, hormone therapy, dyslipidemia, atrial fibrillation, history of stroke or transient ischemic attack (TIA), breast cancer treatment laterality, diabetes mellitus, family history of premature CAD (men <45 and woman <55), and ethnicity. Ethnicities were grouped as: African‐American/black, Caucasian/white, and other.2.5Imaging studies were evaluated with pre‐specified criteria, informed by national guidelines Figure .14, 12.6Echocardiograms were interpreted by two qualified board‐certified echocardiologists, COCATS Level III, Fellows of the American Society of Echocardiography, and currently hold or have held the title of Medical Director Echocardiography for a quaternary hospital system. For echocardiography, the decision to make a finding preclinical was based on minimal to mild cardiac findings that would likely not require treatment, based on cardiac expertise, but may require follow‐up with primary care physician over time. Clinical findings  were those with significant cardiac finding(s) and or at least moderate disease which likely would require follow‐up and actionable management, especially when associated with a history of breast cancer treatment. Further explanation of criteria is available in Table 2.7Electrocardiograms were interpreted by two well‐qualified board‐certified cardiologists. The decision to make an EKG finding preclinical was based on mild cardiac findings that would likely not require treatment, based on cardiac expertise, but may reasonably require follow‐up with a primary care physician over time. Clinical findings  were those with significant cardiac finding(s) which likely would require cardiology follow‐up and possible actionable management, especially when associated with a history of breast cancer treatment. Further details are in Table 2.8CAC CT scans were interpreted by a single board‐certified cardiologist with additional certification by the Board of Cardiovascular Computed Tomography, who held the title of Director of Cardiovascular Magnetic Resonance Imaging and Computed Tomography for a quaternary hospital system. For CAC CT, the decision to make a finding preclinical was based on a minimal to mild cardiac findings that would likely not require treatment, based on cardiac expertise, but may require follow‐up with primary care physician over time. Clinical findings (based on established disease states and cardiac expertise) were those with significant cardiac finding(s) including coronary calcium (Agatston score >0) or at least moderate disease which likely would require follow‐up and actionable management, especially when associated with a history of breast cancer treatment. Further information in Table 2.9The study was designed as a single‐arm, feasibility trial to assess CVD screening in long‐term breast cancer survivors. There were no prior data to inform the predicted incidence of CVD in the planned population using proposed multi‐modality imaging; formal power calculations were not planned. Study parameters were outlined with a biostatistician prior to patient enrollment. Given the expected study duration, potential participant pool, and study resources available, a 200 patient cohort was selected.2.10The presence of preclinical or clinical CVD on any imaging modality was the primary endpoint.2.11Clinical CVD was assessed as a secondary endpoint. We also evaluated for an association between cardiotoxic radiation and/or anthracycline‐based chemotherapy and cardiac disease. Other potentially cardiotoxic treatments such as hormonal therapy and targeted agents were evaluated with univariable and multivariable analysis.2.12Analysis tested for association between cardiotoxic treatments and CVD using a Chi‐Squared test. Secondary objectives included the assessment of an association between risk factors and CVD or treatment group. Associations between categorical risk factors and CVD or treatment group were analyzed using Fisher's exact test. Associations between continuous risk factors and CVD or treatment group were analyzed using the Wilcoxon Rank Sum test or the Kruskal‐Wallis test for three or more groups. Potential risk factors (see Cardiovascular Risk Factors) were analyzed in univariate analysis. Multiple logistic regression analysis was performed to test if treatment type and risk factors were significant predictors of CVD. A backward elimination method was used for the multiple logistic regression analysis to produce a model with only treatment group and significant predictors.2.13Participants underwent the assessment of weight and abdominal circumference and completed EKG (GE MAC 5500 EKG System), TTE with GLS (EPIQ Ultrasound system), and CAC CT  on a single day. CAC CT studies were performed using non‐contrast, prospectively gated 320‐multidector volumetric acquisitions  with standard parameters for Agatston calcium score quantification. Total and per vessel  CAC was quantified. Strain on TTE was included as an exploratory factor as there was insufficient data to recommend for or against its use in long‐term breast cancer survivors. Evaluation was performed by three senior cardiologists . Patients with findings of congenital heart conditions  were marked as “unrelated.”33.1n = 88) had no cardiotoxic treatment, 31.5% (n = 63) had cardiotoxic radiation only, 16% (n = 32) had cardiotoxic chemotherapy only, and 8.5% (n = 17) had both (Table Median age at breast cancer diagnosis was 50 years (range 29–65) and 63 years at the time of imaging (range 37–77). Median interval from diagnosis to imaging was 11.5 years (range 6.7–14.5). Overall, 44%  , 82.5%/58% (cardiotoxic radiation), 75%/38.7% (cardiotoxic chemotherapy), and 82.4%/35.3% (both); rates were not statistically different between groups (χ) Figure .3.3p < 0.0001), age at imaging (p < 0.0001), and hypertension (p = 0.022) were each associated with CVD , or for every 5 years, a 1.76 increased CVD risk. Cardiotoxic radiation , cardiotoxic chemotherapy , and both  were not significant risk factors.Univariate analysis revealed that age at diagnosis , 28% of patients had minor EKG abnormalities and 6% had major abnormalities; clinically relevant EKG abnormalities were independently associated with an increased risk of cardiovascular events and mortality.Electrocardiograms are widely accessible, cost‐effective, and a potential screening method for asymptomatic patients at risk for CVD.CAC CT has also been shown to serve as a valid and reliable test for detecting CVD.The most common screening method for evaluating cancer therapy‐related cardiac dysfunction is TTE.Recently, GLS has been evaluated for use in cancer patients.Over the past decade, the European Society for Medical Oncology (ESMO) (2012) and the American Society of Clinical Oncology (ASCO) (2017) released guidelines to help direct post‐treatment cardiac screening for adult cancer survivors.There is a scant study of breast cancer patients who did not receive cardiotoxic agents within oncology literature. We postulated that this group would serve as an internal control within our study as they were not exposed to these treatment‐related CVD risk factors. Surprisingly, we observed high rates of CVD among this group, beyond that previously seen in untreated, age‐matched peers.It may well be that simply being a breast cancer survivor increases cardiac risk. Recent data from a SEER analysis of 300,000 cancer survivors (>65 years), including ~35% breast cancer survivors, showed that they were more likely to develop new cardiovascular morbidity compared to controls with no prior cancer diagnosis.This study has several limitations. To our knowledge, the CAROLE Study is the first to employ the described tri‐modality screening approach, thus baseline rates of expected CVD were not established nor were formal power calculations conducted. Our assessments of cardiac disease were uniform, pre‐determined, and utilized standard practices whenever possible Table , howeverParticipants were treated from 2004 to 2011; since then, radiation techniques have significantly reduced cardiac dose . After 2009, lower mean heart doses are expected; however, this represents a small subset of our cohort.While our results suggest the value of screening breast cancer survivors, this finding should be further confirmed with a group of matched non‐oncology patients. Prospective studies are currently underway but long‐term data are not expected for ~10 years. Based on our data and other recent literature, we hypothesize that most women treated for breast cancer ‐including those without cardiotoxic chemotherapy or radiation‐ are at increased risk of CVD. We postulate that further study is needed to ensure long‐term survivors are appropriately screened for CVD risks and disease.None.This study was approved by the Northwell Health IRB.Fig S1Click here for additional data file.Table S1Click here for additional data file.


Among males, trunk fat:trunk fat free mass ratio, trunk fat:leg fat mass ratio and  were positively associated with colon cancer risk. None of the body fat measures was associated with risk of ovarian cancer or colorectal cancer in women.All of the body fat measures were positively associated with invasive postmenopausal breast cancer risk (hazard ratios (HR) for the uppermost quintile (Q5) versus the lowest quintile (Q1) ranged from 1.32 (95% CI: 1.09–1.60) for waist circumference (WC) to 1.56 (1.28–1.90) for BMI). Trunk fat mass index (HRThe findings of this study suggest that the current normal weight category based on BMI includes individuals who are at increased risk of some obesity-related cancers. Hence, estimates of cancer risk associated with BMI may be subject to misclassification.Adiposity is recognised as a risk factor for at least 13 different types of cancer.4 Interestingly, an increasing number of epidemiological studies has suggested that excess body fat and metabolic dysfunction may contribute to increased risk of some obesity-related cancers among individuals who are classified as normal weight (i.e. BMI 18.5–24.9 kg/m2). In a prospective study in the Women’s Health Initiative cohort and an earlier Canadian case-control study, general and/or central adiposity were positively associated with risk of postmenopausal breast cancer5 and endometrial cancer6 among normal weight women. Moreover, in the few epidemiological studies, which have examined the associations between various metabolic dysfunctions and cancer among normal weight individuals, those with hyperinsulinemia7 and/or metabolic syndrome, common sequelae of excess body fat, were observed to have increased risk of breast cancer,8 colorectal cancer9 and endometrial cancer.6 Together, these studies provide evidence to suggest that despite being classified as normal weight according to BMI, individuals with excess body fat  and the resulting metabolic disturbances may have increased cancer risk.8Excess adiposity, particularly centrally, is purported to induce inflammation and metabolic dysfunctions which can promote carcinogenesis.10 magnetic resonance imaging (MRI), computer tomography (CT) and bioelectrical impedance analysis (BIA) are used, not only to evaluate overall body fat, but also to analyse body fat composition centrally and peripherally. MRI, CT and, to a lesser extent, DXA are among the most robust methods for assessing body fat composition.10 However, these methods are not commonly used in epidemiological settings due to their high cost. BIA, a less robust method for measuring body fat composition than the aforementioned methods, is a relatively simple, non-invasive, inexpensive technique.11 This procedure not only assesses overall body fat, but also yields measures of body fat percent as well as levels of body fat in the central and peripheral regions.11 Given the putative importance of body fat in influencing cancer risk, we used data from the UK Biobank, a large prospective study in which BIA measures of body fat composition were obtained for virtually all participants, to examine the association of measures of overall, central, and peripheral adiposity with risk of four of the most commonly diagnosed obesity-related cancers, namely, postmenopausal breast cancer, endometrial cancer, ovarian cancer and colorectal cancer, among normal weight individuals.In addition to BMI, which is utilised as a proxy for overall body fat, dual energy X-ray absorptiometry (DXA),http://www.ukbiobank.ac.uk).14 The study was approved by the North West Multi-center Research Ethics Committee, the National Information Governance Board for Health and Social Care in England and Wales, and the Community Health Index Advisory Group in Scotland. All participants provided written informed consent.The UK Biobank cohort comprises 503,317 participants, aged 40–69 at enrolment, who were recruited between 2006 and 2010 from across 22 centres located throughout England, Wales, and Scotland (http://www.ukbiobank.ac.uk/resources/). Further details on the ascertainment of the aforementioned variables are presented in the eMethods  and trunk fat mass index (TFMI16) were calculated by dividing weight (kg), whole body fat mass (kg.) and trunk fat mass (kg.), respectively, by the square of standing height (m2). Waist to hip ratio (WHR) was calculated by dividing waist circumference by the corresponding hip circumference.At the enrolment visit, the Tanita BC418MA body composition analyser  was used to measure the participants’ whole body fat mass, whole body fat percentage, whole body fat free mass, trunk fat mass, trunk fat percentage, trunk fat free mass and leg fat mass. The participants’ waist and hip circumferences were measured using Wessex nonstretchable sprung tape measures , and standing height was determined using the Seca 202 device . BMI, body fat mass index -10 code: C50), endometrial (ICD 10 code: C54.1), ovarian (ICD 10 code: C56) and colorectal (ICD 10 code: C18-C20) cancers. In the UK Biobank, cancer diagnoses are ascertained through linkage to national cancer registries in England, Wales and Scotland. For the present study, complete follow-up was available through 31 March 2016 for England and Wales and 31 October 2015 for Scotland.N = 811); (2) did not have a BMI in the normal range ; (3) did not have BIA measures (N = 2176) or (4) had a history of cancer except for nonmelanoma skin cancer (N = 7734). As hormone replacement therapy (HRT) use is known to attenuate the associations of adiposity with risk of female reproductive cancers ,18 we excluded from analyses of these cancers current HRT users or those whose HRT status was unknown (N = 9450). For breast cancer, women who were premenopausal or had unknown menopausal status were also excluded , leaving a total of 1051 cases and 51,678 noncases. For endometrial cancer, we also excluded women with a history of hysterectomy or those with unknown hysterectomy status , leaving a total of 155 cases and 77,583 noncases, while for ovarian cancer, we also excluded women with a history of bilateral oophorectomy or those with unknown oophorectomy status (N = 4631), leaving a total of 170 cases and 83,212 noncases. After exclusions, for colorectal cancer, a total of 843 cases and 149,085 noncases remained for analysis.Participants were excluded from the study if they (1) withdrew consent (19), ethnicity , alcohol consumption , height ; physical activity , sex  and smoking status . For female reproductive cancers, the models were additionally adjusted for age at menarche , parity and age (years) at first live birth combined , HRT status (never/former), age (years) at menopause , family history of breast cancer ) and mammogram screening . For colorectal cancer, models were additionally adjusted for sex , family history of bowel cancer , history of diabetes , red meat intake , processed meat intake , fruits and vegetables intake , vitamin D supplement use , folate supplement use , and use of ibuprofen  and aspirin . A missing value indicator was included for variables with missing values  with risk of the cancers of interest .Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for the associations of the body fat measures with risk of the cancers of interest. The body fat measures were analysed following categorisation by quintiles (sex-specific quintiles for the body fat measures were based on the distribution of the body fat measures in the noncases). To make comparisons between the HRs for the associations between the body fat measures and the cancers of interest, we also estimated the HRs per standard deviation (SD) increase in the body fat measures. Time to diagnosis of the selected cancers was the underlying timescale. Participants were followed up from their date of enrolment until the date of cancer diagnosis (except for non‐melanoma skin cancer), date of withdrawal from the study, date of death, or until the end of follow-up, whichever came first . All models were adjusted for age at recruitment , socioeconomic status . Further, there is some evidence that the association between body fat and risk of colorectal cancer varies by sex.24 Hence, we also conducted the analyses separately for men and women. We included an interaction term in the regression models and tested its coefficient using the Wald test in order to determine whether sex is an effect modifier of the associations between the body fat measures and risk of colorectal cancer.Excess adiposity, risk of endometrial cancerP values for trend (P-trend) were estimated by including the ordinal variables for the body fat measures as continuous variables in the regression models and testing their coefficients using Wald tests.The proportional hazards assumption was not violated as evidenced by testing using Schoenfeld residuals. To address the possibility of reverse causation, we conducted sensitivity analyses in which we excluded participants with a follow-up time of two years or less.P values were two-sided.All statistical analyses were performed using Stata 16.1 . All After a median follow-up time of 7 years (interquartile range: 6.4–7.7 years), 1051, 155, 170 and 843 incident postmenopausal breast, endometrial, ovarian and colorectal cancer cases had been ascertained. In the overall analytical cohort, participants with FMI and TFMI in the uppermost quintiles had higher BMI, WC, WHR and red meat intake than those in the lowest quintiles but had lower physical activity levels and fruit and vegetable intake  and a relatively high WC  were also positively associated with risk of breast cancer in postmenopausal women , but the trends in risk across quintile levels were not statistically significant . In analyses restricted to colon cancer, the ratio of trunk fat to trunk fat free mass , the ratio of trunk fat mass to leg fat mass , and WC  were positively associated with risk of colon cancer among men , also had increased risk of breast cancer.25 In the present study, we also showed that even among normal weight women, central adiposity (i.e. being in the highest quintile for WC > 80.3 cm or WHR > 0.83), but not general adiposity, was positively associated with risk of endometrial cancer, particularly among postmenopausal women. This result is consistent with the findings of a previous study in which central adiposity (WC > 88 cm) was associated with a significant increase in the risk of endometrial cancer among normal weight women.6 A recent study conducted in the UK Biobank cohort, which included the entire BMI range, also observed positive associations between WC and WHR with risk of endometrial cancer.20 In contrast to the findings for postmenopausal breast cancer and endometrial cancer, we did not observe any association between the body fat measures and risk of ovarian cancer. As suggested by previous studies, it is probable that excess adiposity influences specific histological subtypes of ovarian cancer, namely borderline serous, invasive endometrioid and invasive mucinous tumours.26 However, we could not evaluate the associations between the body fat measures and risk of the aforementioned histological subtypes of ovarian tumour due to the relatively small number of cases with each histological subtype.Using BIA-derived and anthropometric measures of adiposity, we showed that general and central/peripheral adiposity were positively associated with risk of invasive breast cancer among normal weight postmenopausal women. These findings are in accord with those of a recent prospective study in which an approximate doubling in the risk of developing invasive breast cancer was observed among normal weight postmenopausal women with relatively high DXA-derived measures of whole body fat mass and trunk fat mass.1 However, in the present study, there was no association between the body fat measures and risk of colorectal cancer among normal weight men and women combined. Existing evidence suggests that the association between body fat and risk of colorectal cancer is stronger among men than women.27 In line with this, our study showed relatively weak, but positive associations between some measures of central adiposity and risk of colorectal and colon cancer among men. However, none of the measures was associated with risk of colorectal cancer among women. Based on our review of the literature, we are not aware of any previous studies of the associations of body fat measures with risk of colorectal cancer in normal weight individuals. Therefore, further studies are warranted to confirm our results.Excess body fat is considered to be a risk factor for colorectal cancer.In analyses in which we estimated the HRs per SD in order to make comparisons between the associations for the BIA-derived measures and the anthropometry measures, the associations per SD increase in the BIA-derived measures were comparable in magnitude to those for BMI, suggesting that the BIA-derived measures were not better indicators of risk of the cancers of interest than the anthropometry measures. Further, in models additionally adjusted for BMI, the observed associations of quintiles of BIA-derived measures of general and/or central/peripheral adiposity with risk of breast and endometrial cancers were substantially attenuated. Additionally, in analyses of WC and WHR after categorisation using standard cut points, their positive associations with risk of postmenopausal breast cancer and endometrial cancer  were attenuated but remained significant after adjustment for BMI. Among men, the positive association of WC (based on standard cut points) with risk of colorectal cancer was strengthened after adjustment for BMI. These findings imply that BMI only partly explains the association between central adiposity and risk of postmenopausal breast cancer, endometrial cancer and colorectal cancer  among normal weight individuals. Our findings, however, should be interpreted with caution, as the proportion of cases with central adiposity based on standard definitions for WC and WHR (i.e. WC ≥ 88 cm or WHR > 0.85 for women and WC ≥ 90 cm or WHR > 0.90 for men) was relatively small. Hence, confirmatory studies with a larger number of events are needed as our findings may be due to chance.28 This increase in adiposity may increase susceptibility to developing cancer. However, the mechanisms underlying the associations of the body fat measures with risk of postmenopausal breast cancer, endometrial cancer and colorectal  among normal weight individuals are incompletely understood. Nevertheless, excess adiposity, particularly central adiposity, is associated with various metabolic perturbations which can promote cancer development. For example, excess adiposity leads to hyperinsulinemia which acts, in turn, to suppress circulating SHBG levels. Reduction of SHBG has been observed in normal BMI postmenopausal women with excess body fat,5 and leads to elevated levels of free circulating hormones, including oestrogens and testosterone, which have been linked to tumour cell proliferation and other carcinogenic processes.4 Further, insulin can activate the Ras/Raf/MAPK and PI3K/Akt/mTOR pathways that may increase cell proliferation and increase the risk of tumour formation.4 Relatively high levels of body fat, particularly in the central region, can also induce inflammatory responses, which can promote cancer development.29As men and women age, certain physiological changes such as increased adiposity and redistribution of body fat, particularly in the central region, may occur.30 However, in the current study and in a previous study among normal weight women from the Women’s Health Initiative cohort,5 peripheral adiposity was shown to be associated with increased risk of postmenopausal breast cancer. Of note, fat in this region has been positively correlated with levels of estrone31 and leptin,33 both of which are believed to induce carcinogenesis.34 However, further studies are needed to determine whether peripheral body fat can influence breast cancer risk among normal weight individuals.Peripheral adiposity is associated with more favourable metabolic health than abdominal adiposity, with greater insulin sensitivity and a more favourable inflammatory profile, and may therefore be associated with lower cancer risk than central adiposity.2 Further, the predictive equations used in BIA assessments are population-specific and device specific, and therefore, BIA is most appropriate in individuals with the same characteristics as those of the reference population.10 BIA is also a less accurate method than DXA for determining % body fat, whole body fat mass, and lean body mass among lean individuals.36 However, in a recent study within the UK Biobank, BIA-derived fat mass was shown to have a strong correlation with DXA-derived fat mass.37 Our study has several other limitations. WC and BIA-derived trunk fat do not discriminate between visceral and subcutaneous fat, and therefore, are not ideal markers of visceral fat. For endometrial and ovarian cancer, the number of cases within some strata were relatively small, and, hence, findings for these cancer sites may be due to chance. Given that UK Biobank participants are healthier than the general population,14 our findings may not be representative of the general population. Further, for endometrial cancer, colorectal cancer among men, and to a lesser extent, for postmenopausal breast cancer in women, it is possible that the observed associations were partly due to reverse causality as they were attenuated after exclusion of participants with two years or less of follow-up; however, attenuation of the associations may also have been due to loss of power resulting from a reduced sample size. Finally, we were unable to examine the associations between the body fat measures and risk of the cancers of interest by tumour stage and by hormone receptor subtype (for breast cancer) because information on these characteristics is not available in the UK Biobank.This is the largest known prospective study to date that has assessed the associations between various body fat measures and risk of obesity-related cancers among normal weight individuals. Further, we were able to assess these associations using direct measurements of body fat composition (i.e. BIA-derived measures) which were made by trained staff using standardised procedures. Although BIA can more reliably assess body composition than anthropometric measures such as BMI, WC and WHR, its accuracy is affected by factors such as the participants’ hydration status, nutritional status, physical activity level and body temperature.In conclusion, the findings of this study suggest that the current normal weight category based on BMI includes individuals who are at increased risk of some obesity-related cancers. Hence, the current categorisation of individuals as normal weight according to their BMI may need to be re-evaluated in order to better characterise their risk of obesity-related cancers.Supplementary Tables


What is the risk of benign breast diseases (BBDs) and how are they associated with age, hormonal breast cancer risk factors, and family history of breast cancer?In this cohort study of 61 617 women in Sweden, incidence rates for distinct BBD subtypes in individuals 25 to 69 years of age were estimated. The study found that the risk of the most common BBDs by hormonal factors was influenced by age and that family history of breast cancer was associated with the risk of proliferative and nonproliferative BBDs.These results indicate that age and hormonal factors influence the risk of BBDs and that a better understanding of BBDs is important to improve our understanding of breast diseases as a whole. Benign breast diseases (BBDs) are common and associated with breast cancer risk, yet the etiology and risk of BBDs have not been extensively studied.To investigate the risk of BBDs by age, hormonal factors, and family history of breast cancer.This retrospective cohort study assessed 70 877 women from the population-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) who attended mammographic screening or underwent clinical mammography from January 1, 2011, to March 31, 2013, at 4 Swedish hospitals. Participants took part in a comprehensive questionnaire on recruitment. All participants had complete follow-up through high-quality Swedish national registers until December 31, 2015. Pathology medical records on breast biopsies were obtained for the participants, and BBD subtypes were classified according to the latest European guidelines. Analyses were conducted from January 1 to July 31, 2020.Hormonal risk factors and family history of breast cancer.For each BBD subtype, incidence rates (events per 100 000 person-years) and multivariable Cox proportional hazards ratios (HRs) with time-varying covariates were estimated between the ages of 25 and 69 years.A total of 61 617 women within the mammographic screening age of 40 to 69 years  at recruitment with available questionnaire data were included in the study. Incidence rates and risk estimates varied by age and BBD subtype. At premenopausal ages, nulliparity (compared with parity ≥3) was associated with reduced risk of epithelial proliferation without atypia  but increased risk of cysts . Current and long (≥8 years) oral contraceptive use was associated with reduced premenopausal risk of fibroadenoma , whereas hormone replacement therapy was associated with increased postmenopausal risks of epithelial proliferation with atypia , fibrocystic changes , and cysts . Furthermore, predominantly at premenopausal ages, obesity was associated with reduced risk of several BBDs , whereas family history of breast cancer was associated with increased risk .These results suggest that the risk of BBDs varies by subtype, hormonal factors, and family history of breast cancer and is influenced by age. Better understanding of BBDs is important to improve the understanding of benign and malignant breast diseases. This cohort study investigates the risk of benign breast diseases in women by age, hormonal breast cancer risk factors, and family history of breast cancer. Moreover, previous studies9 on BBDs have often been small, inconsistent in their pathological disease classification, and conducted before mammographic screening programs were introduced. Consequently, to our knowledge, there are no up-to-date studies of the incidences of distinct BBD subtypes, and risk factors have not been well established. Furthermore, given that the risk of BBD diagnosis extends throughout several decades in a woman’s life, it is important to understand how age influences the risk.Benign breast diseases (BBDs) are common throughout a woman’s lifetime, from early reproductive life to the postmenopausal part of life, making it a potential health concern to a large number of women.13 Of importance, a history of certain BBDs is also a risk factor for breast cancer; benign proliferative disease with or without atypia increases the risk approximately 4- and 2-fold, respectively, whereas it is less clear whether nonproliferative diseases affect the risk.17 In addition, family history of breast cancer influences the risk of breast cancer after a BBD diagnosis, and the risk may be further elevated in younger women.15 Therefore, it is important to improve our understanding of benign vs malignant breast diseases for breast cancer risk assessment.Hormonal factors  are well known to affect a woman’s breast cancer risk, but little is known regarding hormonal factors and BBDs. Factors such as early menarche, regular and short menstrual cycles, nulliparity, older age at first birth, use of oral contraceptives and hormone replacement therapy (HRT), and high postmenopausal body mass index (BMI) are associated with a higher breast cancer risk, whereas longer breastfeeding duration and higher premenopausal BMI are associated with a reduced risk.14 categorized BBDs according to their association with breast cancer risk. However, more detailed classification of the diseases is needed in accordance with recent pathological guidelines.18 Indeed, BBDs cover a group of various noninvasive breast conditions, including but not limited to, epithelial proliferation with atypia (EPA) or epithelial proliferation without atypia (EP) (hyperplasia), fibroadenomas (firm breast masses that often affect younger women), papillomas , adenosis (enlargement of the lobules), calcifications, fluid-filled cysts, and fibrocystic changes (FCCs) (composed of cysts and solid lesions).20A previous studyIn this study, we aimed to determine the incidence and risk of in-detailed classified BBDs in 67 617 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort. Information on breast biopsy specimens was extracted from pathology medical records and thoroughly classified into BBD subtypes according to the latest European guidelines. Furthermore, we investigated how hormonal factors and family history of breast cancer are associated with the risk for BBDs, with special interest in how the risk is influenced by age.21 Between January 1, 2011, and March 31, 2013, women who attended the national mammographic screening program or underwent clinical mammography at 4 Swedish hospitals located in Stockholm (Södersjukhuset) and Skåne County  were invited to participate. In 2011, all women 40 to 69 years of age were invited for screening, which was extended to those 70 to 74 years of age in 2012.22 A total of 70 877 women were recruited to the cohort.21 On recruitment, participants answered a comprehensive web-based questionnaire on reproductive and lifestyle factors. The ethical review board at Karolinska Institutet in Stockholm approved the KARMA study, and the approval applied to use of the data in the current study. All participants signed informed consent forms at recruitment.21 All data were deidentified. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.KARMA is a population-based and well-characterized Swedish screening cohort.26 Furthermore, records on breast biopsies (including cytologic fine-needle aspirations) are retrieved from the regional pathology medical record system SymPathy. Diagnoses in the Swedish Cancer Register and SymPathy are classified by the International Classification of Diseases for Oncology (ICD-O)27 and Systematized Nomenclature of Medicine.28 Registry-based data and information from SymPathy are continuously updated, every 2 years, for the KARMA participants. The current analyses were conducted from January 1 to July 31, 2020.Detailed information of the KARMA participants before and after inclusion to the study is available through national and regional registries. All residents in Sweden have a unique personal identification number, which enables linkage to high-quality national registries of high validity and essentially complete coverage, including the Swedish Cancer Register and the Swedish Cause of Death Register.29 and in consultation with an experienced breast pathologist (L.S.). The following BBD subtypes were included: EPA , EP , adenosis (including sclerosing adenosis), papilloma, calcifications, fibroadenoma, FCCs , cysts, inflammation (chronic and granulomatous), and nonepithelial tumors . For more details, see eMethods and eTable 1 in the Diagnoses from SymPathy were categorized into 10 BBD subtypes according to the latest European pathology guidelines of breast diseaseEach BBD subtype was analyzed separately, with the diagnosis of the analyzed BBD subtype as the outcome of interest. When a participant was diagnosed with the same BBD subtype multiple times, only the first diagnosis was considered. Given that the mammographic screening age was 40 to 69 years at the start of KARMA, all women 40 to 69 years of age at recruitment with available questionnaire data were included in the study. Follow-up started retrospectively at the age of 25 years and ended at diagnosis of the analyzed BBD, breast cancer diagnosis, death, or end of follow-up after December 31, 2015 (right censoring). Women who were older than 25 years at the start of SymPathy were followed up from 1979 (left truncation).30 For more details on the covariates, see eMethods in the The incidence rates for the BBD subtypes were estimated by calculating the events per 100 000 person-years between the ages of 25 and 69 years. The risk of BBDs by hormonal factors and family history of breast cancer (defined as mother or sister diagnosed with the disease) was assessed by Cox proportional hazards regression with age as the time scale, adjusting for KARMA unit, birth cohort, and educational level. Family history of breast cancer was additionally adjusted for number of sisters. The participants were followed up in 3 age groups, 25 to 44 years, 45 to 54 years, and 55 to 69 years, defined as premenopausal, perimenopausal, and postmenopausal ages, respectively. Hormonal factors included reproductive factors , BMI , and use of oral contraceptives and HRT. When applicable, the covariates were allowed to change over time (time-varying covariates).P < .05 was treated as statistically significant, and all statistical tests were 2-sided. Data preparation and analyses were performed in R statistical software, version 3.5.2 . Survival analyses were conducted using the R functions coxph and tmerge in package survival.The HRs and their 95% CIs were summarized in tables and forest plots. A total of 61 617 women within the mammographic screening age of 40 to 69 years  at recruitment with available questionnaire data were included in the study. Of these, 5341 had been diagnosed with a BBD . BaselinIncidence rates (events per 100 000 person-years) varied by age and BBD subtypes . FibroadThe incidence rates for the other BBDs were relatively low with a maximum of 40 for adenosis, 41 for papilloma, 31 for calcification, 12 for inflammation, and 26 for nonepithelial tumors per 100 000 person-years B. Hence,Regular menstrual cycles, compared with irregular cycles, were associated with significantly increased risk of fibroadenoma at premenopausal ages  . HoweverAt premenopausal ages, being nulliparous vs having given birth to 3 or more children was significantly associated with a reduced risk of EP  but a significantly increased risk of cysts  . HoweverOverall, obesity (BMI >30) was associated with reduced risk of all BBDs, except EP  . At premThe risk of fibroadenoma at premenopausal ages was significantly associated with current  or previous  use of oral contraceptives for 8 or more years . FurtherPostmenopausal ever HRT use was significantly associated with an increased risk of EPA , FCCs , and cysts  . That asFamily history of breast cancer was significantly associated with increased risk of BBDs .At premenopausal ages, family history of breast cancer was associated with double the risk of EPA  and increased risk of FCC  and EP . The risk of EP was also increased at perimenopausal ages . At both premenopausal and perimenopausal ages, family history of breast cancer was associated with an approximately 40% increased risk of cysts . Furthermore, family history of breast cancer was associated with the risk of fibroadenoma in all age-groups .This cohort study of 61 617 women presents up-to-date incidence rates for BBDs and found that the associated risks of the most common BBDs cover a wide and relatively young age range. Furthermore, the study found that hormonal factors, well known to be associated with breast cancer risk, were associated with the risk of BBDs, as was family history of breast cancer, and that, as hypothesized, the risk was influenced by age.1 Moreover, reproductive and lifestyle factors have changed over time. The mean age at first birth is considerably higher today, and as was observed in this study, parity-related factors, such as number of births and age at first birth, were associated with the risk of BBDs. Mammographic screening enables more frequent detection of BBDs, and a possible interpretation is that the incidence rates reported in this study are higher than what older available studies have found.5 However, this study’s estimates are lower than the estimation from autopsy studies suggesting that every second woman will have a BBD,2 which indicates that some BBDs may not be diagnosed, such as benign lesions that do not appear as well-defined solid tumors.It is difficult to directly compare the incidence rates in this study with those in older studies. For instance, several distinct benign conditions were often combined into the less well-defined diagnosis of fibrocystic breast diseases.2 and breast cancer,12 obesity generally is associated with a reduced premenopausal risk, with the exception of EP. Of interest, the risk reduction extended to older age groups for FCCs and cysts in this study, which may suggest that certain cysts generally are more difficult to detect in breasts with a larger amount of fat tissue. However, menstrual cycle patterns and BMI were reported only at the time of recruitment. Furthermore, this study and others2 have not found any association between age at menarche and the risk of BBDs, and irregular menstrual cycles have been found to be both more and less common in women with BBDs.32 In this study, having regular menstrual cycles was associated with an increased premenopausal risk of fibroadenoma, and at postmenopausal ages, a history of regular cycles was associated with an increased risk of EPA but a reduced risk of fibroadenoma. Furthermore, consistent with the findings in this study, oral contraceptives are generally protective against BBDs, especially with longer duration of use, whereas HRT increases the risk.2 It is, however, debated whether the protective effect and increase in risk extend to atypical disease and how different doses affect the risk.37 This study found a protective association between fibroadenoma and long use of oral contraceptives (≥8 years) at premenopausal ages, whereas HRT was associated with an increased postmenopausal risk of EPA, fibroadenoma, FCCs, and cysts. Thus, additional studies are needed to determine how exogenous hormone use affects the risk of BBDs.These findings suggest a heterogenous BBD risk pattern by the traditional hormonal breast cancer risk factors and that the risk of BBDs varies among the analyzed age groups. As observed in this and previous studies on BBDs2 was the associated reduced risk of EP in nulliparous premenopausal women, which suggests that being pregnant is, in the short term, associated with an increased risk of nonatypical proliferative benign disease. In contrast, previous studies2 have mainly observed higher risk of BBDs in nulliparous women, similar to breast cancer. In addition, previous studies3 have generally not found any associations with age at first birth or breastfeeding duration, which in this study were associated with the risk of fibroadenoma at premenopausal ages and FCCs at perimenopausal ages. Notably, in premenopausal women, being nulliparous was associated with an increased risk of cysts when compared with parity of 3 or more but a reduced risk when compared with being uniparous, which might indicate that several full-term pregnancies are needed to reach a protective effect against developing cysts.A finding inconsistent with previous studies17 and it is not clear whether nonproliferative diseases impact the risk. To identify individuals susceptible to breast changes, it would be helpful to ascertain which women with a BBD diagnosis are at elevated risk of breast cancer and who is not, which could include information on the woman’s history of hormonal factors.Furthermore, this study found that family history of breast cancer, a strong breast cancer risk factor, was associated with proliferative and nonproliferative diseases, predominantly at premenopausal ages. Proliferative benign diseases are associated with an increased risk of breast cancer; however, risk estimates vary among studies,This study has limitations. Smaller studies are able to have more detailed, prospectively assessed pathological data, although such data come with lower statistical power and poorer disease classification. Use of the KARMA study and the Swedish personal identification numbers allowed the unique combination of quality-registry data, questionnaire data, and pathology medical records on breast biopsies, which made it possible to analyze a large number of biopsy-confirmed BBDs in a large number of women and to follow up the participants for a long time. The BBD diagnoses obtained from SymPathy comprise biopsies taken on referral after mammographic screening as well as after clinical examination, but information on mode of detection is not available. However, most BBD diagnoses in patients between the ages of 40 and 69 years will be from mammographic screening. As with all survey studies, self-reported retrospective questionnaire data are of varying quality; however, the educational level in Sweden is high. The KARMA cohort is population based because all women attending mammographic screening were invited; however, even though mammographic screening attendance in Sweden is high, the women needed to have actively chosen to participate in the study. Women with higher educational level or family history of breast cancer are more likely to accept the invitation and attend mammographic screening and to agree to participate in studies. Furthermore, the large number of statistical tests performed in this study generates a risk of false-positive results by random chance; thus, the findings need to be replicated in other large cohort studies.2 but also highlight new findings that need further investigation. Indeed, the etiology of BBDs is a subject that has been relatively unexplored during the last decades. Further work on BBDs with updated classification is needed to determine risk factors for BBDs and to understand the differences and associations between benign and malignant breast diseases.This cohort study estimated the incidences of BBDs and found that the risk of BBDs is associated with hormonal factors and age. Interestingly, the risks of proliferative and nonproliferative BBDs were associated with family history of breast cancer but not with all traditionally recognized hormonal breast cancer risk factors. The results for hormonal risk factors are to some extent consistent with previous studies


Higher physical activity levels are associated with lower breast cancer-specific mortality. In addition, the metabolic syndrome is associated with higher breast cancer-specific mortality. Whether the physical activity association with breast cancer mortality is modified by number of metabolic syndrome components (cardiometabolic risk factors) in postmenopausal women with early-stage breast cancer remains unknown.Cardiovascular risk factors included high waist circumference, hypertension, high cholesterol, and diabetes. Breast cancers were verified by medical record review. Mortality finding were enhanced by serial National Death Index queries. Cox proportional hazards regression models were used to estimate associations between baseline physical activity and subsequent breast cancer-specific and overall mortality following breast cancer diagnosis in Women’s Health Initiative participants. These associations were examined after stratifying by cardiometabolic risk factor group.P < 0.001) but not with breast cancer-specific mortality . The physical activity and all-cause mortality association was not significantly modified by cardiometabolic risk factor number.Among 161,308 Women’s Health Initiative (WHI) participants, 8543 breast cancers occurred after 9.5 years (median) follow-up in women, additionally with information on cardiometabolic risk factors and physical activity at entry. In multi-variable analyses, as measured from cancer diagnosis, higher physical activity levels were associated with lower all-cause mortality risk (hazard ratio [HR] 0.86, 95% confidence interval [CI] 0.78–0.95, trend Among women with early-stage breast cancer, although higher antecedent physical activity was associated with lower risk of all-cause mortality, the association did not differ by cardiometabolic risk factor number. Metabolic syndrome is a clustering of metabolic dysfunctions that includes at least three of the following: high waist circumference, triglycerides, blood pressure, and fasting blood glucose; and lower high-density lipoprotein cholesterol. Among other associated morbidities, the metabolic syndrome has been associated with higher risk of breast cancer-specific mortality , 2.Higher physical activity levels, compared to lower levels, measured prior to breast cancer diagnosis have been associated with statistically significantly lower all-cause mortality among women diagnosed with breast cancer , 4. HighThe Women’s Health Initiative (WHI) recruited 161,308 post-menopausal women aged 50–79 with anticipated 3-year survival from 40 US clinical centers between 1993 through 1998. Details of the WHI have been reported Physical activity was assessed by questionnaire. Participants were asked about walking outside the home by duration and by speed categories; metabolic equivalent task (MET) values were assigned for walking . For recreational physical activity, participants were asked how often and for how long they exercised at various intensity levels. Vigorous-intensity activities included aerobics, jogging, tennis, and swimming laps while moderate-intensity activities included biking, exercise machine use, calisthenics, easy swimming, and dancing. The information on walking and recreational physical activity was used to generate a hours/week (h/wk) variable which was combined with MET values for each activity.to generate the final MET-h/wk variable, as previously described .A convenience construct was used to assess metabolic syndrome components, referred to here as “cardiometabolic risk factors” which were determined at entry and included: (1) measured high waist circumference, (2) measured high blood pressure, (3) history of high cholesterol, and 4) history of diabetes, as previously described  history . High chIn the WHI clinical trials, participants were followed for clinical outcomes every 6 months during the 8.5-year (median) intervention period, then annually. WHI observational study participants were followed annually throughout. Breast cancers were initially confirmed with medical record review at the clinical centers by trained physician adjudicators with final confirmation and coding at the clinical coordinating center. Reports of death and cause of death were verified by medical records or death certificate review at the clinical coordinating center . Mortali2, # of metabolic risk factors, hormone receptor status, grade, HER2/NEU number of examined lymph nodes, number of positive lymph nodes, tumor size, and stage) in univariate analyses. Effect modification with cardiometabolic risk factors was assessed by adding the cross-product term of physical activity and cardiometabolic risk groups to the multivariable model.Cardiometabolic risk groups were categorized into 0, 1–2, and 3–4 components as incorporated in previous analyses . AssociaAt entry, women with three or four cardiometabolic risk factors, compared to those with no risk factors, were older, have higher BMI, were more likely to be Black, report fair/poor health, to have diabetes, higher blood pressure and report less physical activity Table .Table 1BP < 0.0001)  Table .Table 2AP < 0.001) but were not associated with lower breast cancer-specific mortality . After stratifying by cardiometabolic risk factor groups, the association between higher physical activity at entry and lower all-cause mortality following breast cancer diagnosis was found only in participants with 1–2 metabolic risk factors. There was no statistically significant interaction between cardiometabolic risk factor group and associations of physical activity and breast cancer outcomes.Physical activity levels and cardiometabolic risk factors were determined at study entry. Incident breast cancers were diagnosed 9.5 years (median) from entry and were subsequently additionally followed for 9.5 years (median) after breast cancer diagnosis. In the multivariable models Table , higher In prior WHI reports, both higher physical activity levels  and feweTo our knowledge, no study has examined the relationship between physical activity, cardiometabolic risk factors, and mortality among women with breast cancer. Current study findings support the established inverse association between physical activity and all-cause mortality among breast cancer survivors  as curreP = 0.02) [P = 0.01, compared to women with 0 or 1–2 risk factors) [In contrast, cardiometabolic risk factors modified the effect of a dietary intervention on breast cancer outcomes, as evaluated in the WHI randomized Dietary Modification (DM) trial. In the WHI DM trial, all 48,835 participants were free of prior breast cancer at entry and were randomly assigned to a low-fat dietary-pattern (40%) or a usual diet comparison group (60%). Changes associated with the dietary intervention included reduced fat intake; increased fruit, vegetable, and grain consumption, and weight reduction . While t = 0.02) . In a sefactors) . Thus, iThere is a study which could assess relationships among physical activity, cardiometabolic risk factors, and breast cancer outcome. The ongoing Women's Health Initiative Strong and Healthy (WHISH) pragmatic physical activity intervention trial has completed randomization of 49,331 postmenopausal women testing whether a physical activity intervention reduces major cardiovascular events and all-cause mortality . A seconCurrent study strengths include the large sample size, prospective study design, use of a previously developed physical activity assessment tool, long follow-up and centralized, adjudicated breast cancer incidence and mortality outcomes. The study has limitations. First, the observational design precludes causal inferences. Second, reliance on questionnaire data for diabetes and cholesterol and reliance on baseline cardiometabolic risk factors and recreational physical activity assessment are limitations as well. Regarding the interval between cardiometabolic risk factor assessment, in a prior analysis in a WHI subgroup, metabolic syndrome status measured earlier in time (more years prior to breast cancer diagnosis) was more predictive of breast cancer risk compared to determinations made closer to breast cancer diagnosis , 18.In summary, the association of higher physical activity level with lower mortality risk in postmenopausal women with breast cancer did not differ significantly across cardiometabolic risk factor groups. Future studies could consider examining changes in cardiometabolic risk factors and physical activity patterns over time among women with breast cancer.


This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers.This case–control study of 1204 women aged 47–73 includes 599 cancer cases  and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection ; node-negative cancers; node-positive cancers, and all cancers vs. controls.p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD .FGV, VBD, VAS, and DG all discriminated interval cancers (all FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk. The aim of stratified, or risk-based, breast cancer screening –5 is to Breast density (BD) reflects the amount of glandular and fibrous connective tissue compared with the amount of fatty tissue in the breasts, as seen on a mammogram. BD has three attributes that support use in stratification of population screening. First, increased BD, conditional on age and body mass index (BMI), is a strong risk factor for breast cancer ; second,To fill this gap in the literature, we compared women diagnosed with cancer  to disease-free women with respect to BD. We measured BD using automated BD assessments and radiologists’ quantitative visual BD assessments to compare the predictive ability of each BD assessment method. We hypothesised that quantitative BD can predict interval cancers and node-positive screen-detected cancers in order to serve as an imaging biomarker with the potential to personalise breast cancer screening.Ethical approval for the establishment and use of the OPTIMAM image database  was obtaIn the National Health Breast Screening Programme (NHSBSP), women aged 50–70 years are invited for screening every three years, with an age extension being piloted in a randomised controlled trial of women 47–73 years conducted from 2009 to 2022 . We specThe images were acquired on five Hologic Selenia systems, two Hologic Selenia Dimensions systems , one GE Senographe Essential system  and one Sectra MDM-L30 . All the digital mammograms in the study were de-identified. Both unprocessed and processed images were collected, when available. To be included in the study, women needed at least one negative digital mammogram prior to the screening mammogram that detected their cancer or the diagnostic mammogram that diagnosed their interval cancer. For the screen-detected cancers, the prior mammogram was used in the study in order to provide an assessment, whether by the radiologist or quantitative imaging, that was ‘blind’ to the cancer. Selection of controls for each case followed a prescribed protocol. Cancer free controls were selected based on the same equipment and ‘date of acquisition’ as the cases. For screen-detected cases, ‘date of acquisition’ was the date of screening examination at which time the cancer is detected. For interval cancers, there were no screening images for detection of cancer (by definition), so ‘date of acquisition’ was date of prior screening images for that individual. From the group of controls meeting these requirements for each case (machine and ‘date of acquisition’), the closest available age was selected. This resulted in 99.4% of cases and controls being within 4 years of age. Because of the limited normal cases in the OPTIMAM database at the time of case/control selection, a one-to-one match protocol was not possible for all. In total, 542 cases had matched controls and 57 cases did not. Thus 63 unmatched controls were included. Matching on other characteristics (e.g. ethnicity or BMI) was not possible because such variables were not available. All the controls were followed up and remained cancer free for at least 3 years. Pathological data were collected from England’s National Breast Screening System.n = 239) were selected, and node-negative cases (n = 360) were selected randomly to complete the case set.We required adequate statistical power for comparison of controls with two specific subgroups of cases: interval cancers and node-positive cancers. For both these case groups, we posited that ~20% of controls and 30% of cases would be in the highest density category. Estimating that the total number of controls would be at least double the number of cases in either of these subgroups, 291 cases would give 90% power and 216 cases would give 80% power. We, therefore, aimed to have at least 216 cases in each subgroup. Anticipating that, for some cases and controls, the unprocessed mammograms might not be available, we obtained 599 cases in total, comprising 302 screen-detected cancers and 297 interval cancers. We sought to enrich the dataset for node-positive cases, so all available node-positive cases  in percent and 5th Edition Volpara Density Grade (DG) from the unprocessed images on the exam level. Volpara is a FDA-approved fully automated software to estimate volumetric breast density [Automated BD software  was used to calculate fibroglandular volume (FGV) in cm density , based o density . A modelIn addition, VBDmax is calculated as the denser VBD of the left or right breasts. Volpara software uses preset cut-off points of VBDmax (to mimic BI-RADS 5th Edition) and reports a study-level 5th Edition Volpara Density Grade (DG), where DG a: 0 ≤ VBD < 3.5%, DG b: 3.5% ≤ VBD < 7.5%, DG c: 7.5 ≤ VBD < 15.5%, DG d: VBD ≥ 15.5%). Typically, the Volpara Density Grades are denoted as VDG a/b/c/d. However, to avoid confusion between acronyms that designate ‘V’ as ‘volume’ or ‘volumetric’ the acronym DG is used throughout this paper, rather than VDG. Volpara software has been validated  and usedA radiologist (ESB), blinded to case–control status, was shown the images using MedXViewer  and asseWe took the continuous variables  and determined categorical quartiles using thresholds determined by the distribution for all cases and controls combined (excluding those missing raw images). DG is a categorical variable, already divided by the Volpara software into categories with pre-determined thresholds. We then estimated how these four categorical measures of BD  and how three continuous BD measures  discriminated between cases and controls. We estimated the effects of these BD variables on risk of cancer overall and on the risk of particular subsets of cancers  using logistic regression, adjusting for age. For each subgroup of cases, we used all controls as the comparator group.In addition, we carried out receiver operating characteristic (ROC) analysis, by estimating and comparing areas under the ROC curve (AUCs). We used the De Long et al.  method tOur study included 1204 subjects  in women aged 47–73 years old. Dates of mammograms included in this study ranged from 2010 to 2015 (Table Unprocessed images needed for automated BD measures were available for 429 (72%) cases and 418 (69%) controls. FGV-quartile, VAS-quartile, and DG predicted all cancers versus controls, while VBD-quartile did not (Table p < 0.01), node-positive  and combined cancers . All automated BD measures more consistently predicted interval compared to screen-detected cancers  with statistical significance Table . FGV-quaFor continuous BD measures , the differences in means between cases and controls were statistically significant for all, interval, node-positive, and combined cancers Table . The difAUC analysis Fig.  demonstrp < 0.01), node-positive (p < 0.01) and combined (p = 0.02) cancers, but only a moderate difference between BD measures for interval cancers (p = 0.06).There were clear differences between the AUCs of the three BD measures for all  and the highest risk 25% (4th quartile). Results showing the highest risk 25% (4th quartile) for all subcategories of cancers including screen-detected, interval, node-positive, and node-negative demonstrate that FGV captures at least as high a percentage of these cancers as VBD and VAS . Only VBD was significantly more predictive of node-positive than of node-negative cancers (p < 0.01), although all three measures had greater AUCs for node-positive than node-negative cancers. All three automated BD methods showed higher AUCs for (i) interval compared to screen-detected; (ii) combined compared to node-negative; and (iii) combined compared to screen-detected cancers. The AUC for interval cancers was significantly greater than the AUC for screen-detected cancers for VBD (p < 0.01) and VAS (p < 0.01), and suggestively so for FGV (p = 0.07).FGV, VBD and VAS were all significantly more discriminative of interval cancers than of screen-detected cancers , ROC analysis (captured by AUC), or number of cancers included in the highest risk category (4th quartile). Of note, for VBD and VAS, interval cancer prediction was significantly greater (by AUC) than screen-detected cancer prediction while FGV only showed a statistical trend. This phenomenon underscores the differential ability of FGV to discriminate screen-detected cancers, knowing that FGV has generally higher AUCs for virtually all comparisons  and tested several quantitative BD techniques . WandersWhatever the mechanism for measuring BD, women with high levels of BD have an increased risk of interval or node-positive cancers, motivating the need to augment the screening regimen. Women at high breast cancer risk but not at high masking risk, may benefit from increased mammography screening frequency. Women at high masking risk only or high cancer and masking risk, may be better served by screening with modalities supplementary to mammography, like MRI or ultrasound. In fact, there is interest in determining and targeting these different opportunities for improved screening outcomes (masking versus breast cancer risk) and modelling these strategies . In our The strengths of our study include our assessment of the discriminative ability of several measures of BD and risk of breast cancer. We also provide an important analysis of volumetric BD related to interval cancer risk , 35, 36 priori power calculation anticipated these missing images, which therefore should not have influenced our results or conclusions.We did not collect detailed information in relation to a number of covariates . We also did not have BMI, which is known to improve discriminatory capacity of quantitative BD measurements . As expeWe find that FGV has the potential to predict the important components of risk that may provide the foundation for stratified screening: risk of cancer, risk of aggressive cancer, and risk of masking effects. While any quantitative BD measure will undoubtedly be one variable among many predictive variables that will contribute to decisions about breast cancer screening, we believe that our analysis adds to the literature that will inform a more comprehensive model to be tested in the future. Our findings suggest that FGV may be a comparatively better imaging biomarker suited to provide guidance for more intensive stratified screening for mammography, such as a shortened screening interval. VBD, VAS and DG, by predominantly predicting interval cancers and node-positive cancers may selectively correlate with masking risk and be more suited to directing women to supplemental screening modalities other than mammography.Supplementrary Information


Sleep problems are more common in breast cancer survivors than those without a cancer history. Our goal was to examine the risk of fractures among breast cancers survivors who used prescription sleep aids.We conducted a retrospective cohort study of 21,346 adult women diagnosed with stage 0–III breast cancer between 2009 and 2016 and followed them through 2017. We examined person-year rates of fractures by sleep medication use and calculated adjusted hazard ratios (HR) and 95% confidence intervals (CI) with Cox proportional hazards models using time-dependent variables for sleep medications and covariate medications  adjusted for demographics, comorbidities, and tumor characteristics and cancer treatments.The sleep medication use was common (40%) in breast cancer survivors and was associated with a 33% increased risk of fractures . Further, in a sensitivity analysis based on new use of sleep medication, the fracture risk was even stronger .Given the high use of sleep medications and the high risk of fractures in breast cancer survivors, this study suggests that non-pharmacologic management of sleep problems might be considered as alternative therapy.The online version contains supplementary material available at 10.1007/s10549-021-06392-4. Additionally, the fracture risk associated with sleep aids persisted even after we accounted for bisphosphonate use. Furthermore, in a sensitivity analysis based on breast cancer survivors with new use of sleep aid medications, the fracture risk was 44% greater. To our knowledge, our study is the first to integrate comprehensive set of covariates in examining the fractures risk related to sleep aid medication use in a large community-based sample of breast cancer survivors. Our results suggest that prescription sleep aids increase the fracture risk in this vulnerable population.Reasons for our findings relate to the hypnotic properties of the sleep medications that linger through the day exacerbating the risk of falls. In addition, breast cancer survivors taking aromatase inhibitors (AIs) might also be vulnerable to this effect given that AI’s are associated with acceleration of bone loss and an increased risk of osteoporotic fractures. Due to the adverse bone effects associated with AIs, patients are given bisphosphonates to combat this, but our study did not find a marked protection from bisphosphonates. Similarly, other epidemiologic studies have not found a protective effect of bisphosphonates in reducing fractures risk –16.Our study has several strengths. The large sample size and comprehensive healthcare coverage enabled us to examine the fracture risk without concerns about variable healthcare insurance coverage that is known to affect cancer outcomes. Importantly, our analysis was based on filled prescriptions which ensured accuracy for both exposure and covariate drug ascertainment. Further, the study had sufficient long follow-up time to identify fracture risk, with a median of 4 years, ranging up to 9 years. Finally, we applied different analytic strategies to address various sources of bias in observational studies, and we conducted sensitivity analyses to assure the robustness of the conclusion. The HR estimates in our study accounted for demographics, comorbidities, tumor characteristics, cancer treatments, and concurrent medications. In the multivariable Cox proportional hazards model, medications were handled as time-dependent thus reducing drug exposure misclassification. Moreover, our sensitivity analyses restricted to women who were new users of sleep medications; this further strengthened the finding of the association between sleep medications and fracture risk. Lastly, about half of the breast cancer survivors were from minority groups, which enhances generalizability of our results to the larger California population. Thus, our carefully designed longitudinal analysis better addressed the adverse side-effects of prescription sleep medication in breast cancer survivors in real-world practices.n = 8,498), the association between fracture risk and sleep medications persisted—the risk of fractures was greatest in those with worse T-scores indicating bone problems. Although we adjusted for adjuvant endocrine therapy (AIs and tamoxifen) and examined a comprehensive set of covariates, including time-varying sleep medications, antidepressants, anti-anxiety drugs, residual confounding cannot be precluded in our study.Certain limitations must be considered. We did not have complete data on BMI, and those with lower BMI might be more likely to have fractures. However, BMI may not be correlated with sleep medication use, thus the effect of this confounder might be minimal. Based on randomized clinical trials data that favored AIs over tamoxifen, physicians might have preferentially prescribed AIs to older patients, or to those with concerns for thromboembolism . We focuIn summary, we determined that almost 40% of breast cancer survivors had used sleep aid medications and its use increased the risk of fractures by 33%. This study bridged the gap between studies that examined this question in other vulnerable groups such as older women, but did not specifically include breast cancer survivors. Thus, clinicians must consider the safety of prescribing sleep medications in this vulnerable group. Our findings are also relevant to medical oncologists because they are increasingly providing general care to their patients , 20. GivOur study demonstrates a large fraction of breast cancer survivors (40%) used prescription sleep medications, and the most common was lorazepam, a benzodiazepine. Given that new users of sleep medications had a 40% increased fracture risk, even after adjustment for bisphosphonate use, caution is warranted when treating breast cancer survivors for sleep disturbance. Non-pharmacologic therapy may be considered to manage the sleep disturbances in breast cancer survivors that arise from distress or due to the side-effects of cancer treatments.Supplementary file1 (docx 186 kb)Below is the link to the electronic supplementary material.