Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors  improves identifying women with dense breasts at high breast cancer risk.We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk , whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk . Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623–0.654, vs. C-statistic 0.614, 95% CI 0.598–0.630, respectively; Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone. TM, Hologic, Inc., Marlborough, MA, USA; and VolparaTM, Volpara Solutions/Matakina Technology, Wellington, New Zealand) and publicly available  software that can be used in clinical practice. Breast cancer associations appear to be similar with qualitative and quantitative measures examined [Many studies using both qualitative and quantitative breast density measures have found women with high breast density (greater amount of breast and connective tissue compared with fat) are at increased breast cancer risk . The Breexamined –5.TM, VolparaTM) is modest [BI-RADS breast density is estimated visually and reflects density quantity, distribution, and parenchymal pattern, whereas quantitative measures algorithmically assess absolute dense breast volume. The correlation of clinical BI-RADS density with quantitative dense breast volume (Quantras modest –8. Discos modest . ConversAbout 50% of women with dense breasts are at low to average breast cancer risk (5-year risk ≤ 1.67%) . AccuratWe evaluated the independent contribution and improved discriminatory accuracy of adding dense breast volume to the Breast Cancer Surveillance Consortium (BCSC) risk prediction model , which ihttp://www.bcsc-research.org/) [Study participants were in one of three case-control studies nested within large prospective breast imaging cohorts. The San Francisco Mammography Registry (SFMR) and Vermont Breast Cancer Surveillance System (VBCSS) participate in the National Cancer Institute-funded BCSC (ch.org/) . Both rech.org/) .n = 1052) or the Vermont Cancer Registry and pathology databases (n = 288) from January 2007 through November 2012 with a raw digital screening examination at least 6 months prior to diagnosis with no upper time limit were included. Two control subjects (n = 2678) without prior breast cancer were matched to each case on age within 5 years, race, date of screening examination within 1 year, mammography machine, and facility. The Mayo Clinic cohort obtained “for processing” digital images from Selenia machines from April 2008 through December 2013 from women in the tristate region of Minnesota, Iowa, and Wisconsin. Incident invasive breast cancers from this region reported to the Mayo Clinic tumor registry through December 2013 (n = 380) were included. Approximately three control subjects (n = 1008) without prior breast cancer were matched to each case on age within 5 years, race, state of residence, date of screening examination within 1 year, and mammography machine. We ensured that each control subject had at least one normal screening mammogram on or after the corresponding case’s diagnosis date. Our total sample consisted of 1720 invasive cases and 3686 control subjects.The SFMR obtained “for processing” digital screening examinations done with Selenia machines (Hologic Inc.) at four facilities since 2006, and the VBCSS obtained them from one facility for screenings done since 2007, which serve as the underlying screening mammography cohort for each registry. Incident invasive breast cancers reported to the California Cancer Registry  at the time of mammography and from self-report or medical record review (height and weight) for the Mayo Clinic cohort. BMI was calculated by dividing weight in kilograms by height in square meters. Race/ethnicity was coded using the expanded definition currently used in the Surveillance, Epidemiology, and End Results program and U.S. vital statistics . Age at first live birth was dichotomized as nulliparous or age ≥ 30 years for first live birth compared with age at first birth < 30 years.Practicing radiologists classified breast density as part of routine clinical practice at the time of mammography interpretation using the BI-RADS® density categories : a = almWe compared frequency distributions of demographics and risk factors between cases and control subjects. Dense breast volume quartiles were defined using control subjects from all sites. Within each BI-RADS category, the proportion of cases and control subjects within each dense breast volume quartile was calculated. Spearman’s correlation coefficient was calculated to assess the association of BI-RADS density categories with continuous dense breast volume.Conditional logistic regression stratified on matched set was used to examine the effects of BI-RADS density and dense breast volume on invasive breast cancer risk. Associations were summarized with ORs and 95% CIs, and with AUROC or C-statistics, which accounted for the matched study design. Differences in C-statistics between models were tested using the SEs estimated from 5000 bootstrap samples. Models were adjusted for age, race/ethnicity, first-degree family history of breast cancer, history of benign breast biopsy, and BMI (continuous). We used the second BI-RADS category as a reference to allow for estimations of risk at the lowest and highest categories and because it is the most prevalent category. Study heterogeneity was evaluated by including an interaction term between study site and breast density measurement. Initially, we fit a model evaluating the association among all possible combinations of BI-RADS density and quartiles of dense breast volume in reference to women with scattered fibroglandular density and second-quartile dense breast volume. Differences in breast cancer association among combinations of BI-RADS density and quartiles of dense breast volume were evaluated by including a multiplicative interaction term. Then, separate models were evaluated that included BCSC clinical risk factors, with and without BMI, plus one or more measure(s) of breast density: BI-RADS density, continuous dense breast volume, and BI-RADS density plus continuous dense breast volume. Continuous dense breast volume was log-transformed and divided by its SD.To examine the absolute risk associated with quartiles of dense breast volume, we incorporated the adjusted ORs associated with dense breast volume into the BCSC model. ORs for each quartile from the fully adjusted model were standardized for attributable fraction so that they measured increased or decreased risk relative to average risk. We calculated the estimated 5-year absolute risk from both the original model and the updated model for each subject and summarized these absolute risks across BI-RADS and dense breast volume categories. For those with heterogeneously or extremely dense breasts, the proportion of control subjects with risks ≥ 2.5% were calculated for each model. These proportions were then used to calculate the net gain in reclassification for the updated risk model including dense breast volume.P values < 0.05 were considered to be statistically significant.Analyses were performed using SAS version 9.4 software . Two-sided statistical tests were used, and r = 0.38 (95% CI 0.34–0.42) for cases and r = 0.31 (95% CI 0.29–0.34) for control subjects.We compared 1720 women with invasive breast cancer with 3686 matched women without breast cancer with breast density measured, on average, 2.4 years (range 6 months to 6 years) before cancer diagnosis. Women with invasive cancer were more likely to be nulliparous or ≥ 30 years of age at first live birth, to have a family history of breast cancer, to have heterogeneously or extremely dense breasts, and to have higher mean dense breast volume (Table P = 0.75).In multivariable models that included clinical risk factors in the BCSC risk model with additional adjustment for BMI, compared with women with BI-RADS scattered fibroglandular density and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk , whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk  .In multivariable models that included risk factors in the BCSC risk model with additional adjustment for BMI, women with BI-RADS extremely dense breasts were at 2.45 times higher risk than women with scattered fibroglandular densities (Table P = 0.02) (Table P < 0.001). Adding quartiles of dense breast volume to a model with BCSC risk model factors and BMI had less discrimination than a model with continuous dense breast volume  (model results not shown).Adding continuous dense breast volume to a model with BCSC risk model factors showed improved discrimination of case status  on the basis of quantity and distribution of breast density . The BI-r tissue , 23. Winr tissue  showed tr tissue –26. Our Among women with dense breasts, 24% are at high risk of an interval cancer (>1 per 1000 screening examinations) and warrant discussions of supplemental imaging . IntervaTM) and publicly (LIBRA) [This is the largest study to date involving an examination of the combination of an automated quantitative measure of dense breast volume and BI-RADS breast density on digital mammography examinations and their independent contribution to breast cancer risk. We examined Volpara’s automated dense breast volume measure. Other commercially (Quantra (LIBRA)  availabl (LIBRA) –32. Thus (LIBRA) . Our pop (LIBRA) ; as suchWe found that the combination of automated quantitative and qualitative clinical assessments of breast density can better identify women with dense breasts at high breast cancer risk than either measure alone. In future studies, researchers should assess automated dense breast volume measures in combination with automated parenchymal complexity features, as well as qualitative breast density measures, because both may measure different aspects of breast density than quantitative measures when assessing breast cancer risk and screening outcomes .


The increasing use of genome sequencing with patients raises a critical communication challenge: return of secondary findings. While the issue of what sequencing results should be returned to patients has been examined, much less attention has been paid to developing strategies to return these results in ways that meet patients' needs and preferences. To address this, we investigated delivery preferences  for individual genome sequencing results among women diagnosed with breast cancer at age 40 or younger.We conducted 60 semistructured, in‐person individual interviews to examine preferences for the return of different types of genome sequencing results and the reasons underlying these preferences. Two coders independently coded interview transcripts; analysis was conducted using NVivo 10.The major findings from the study were that: (1) many participants wanted sequencing results as soon as possible, even at the time of breast cancer diagnosis; (2) participants wanted an opportunity for an in‐person discussion of results; and (3) they put less emphasis on the type of person delivering results than on the knowledge and communicative skills of that person. Participants also emphasized the importance of a results return process tailored to a patient's individual circumstances and one that she has a voice in determining.A critical goal for future transdisciplinary research including clinicians, patients, and communication researchers may be to develop decision‐making processes to help patients make decisions about how they would like various sequencing results returned. While the issue of what genome sequencing results should be returned to patients has been examined, much less attention has been paid to developing strategies to return these results in ways that meet patients' needs and preferences. To address this, we investigated delivery preferences  for individual genome sequencing results among women diagnosed with breast cancer at age 40 or younger. The major findings from the study were that: (1) many participants wanted sequencing results as soon as possible, even at the time of breast cancer diagnosis; (2) participants wanted an opportunity for an in‐person discussion of results; and (3) they put less emphasis on the type of person delivering results than on the knowledge and communicative skills of that person. Whole exome and genome sequencing are rapidly being introduced into clinical settings  and BRCA2 (OMIM: 600185) , genome sequencing can be beneficial to surveillance and surgical decisions . The YWBCP cohort is 91% Caucasian; mean age at diagnosis is 35 years and mean time since diagnosis is 7 years at time of enrollment. Because we planned to conduct in‐person interviews, only those YWBCP participants in the St. Louis region were contacted by e‐mail, letter, and e‐newsletter. Interested women contacted the study team to schedule an appointment for an interview.We conducted qualitative semistructured in‐person interviews to examine how, when, and from whom women diagnosed with breast cancer at age 40 or younger would like to receive individual genome sequencing results, focusing on secondary findings. We recruited a purposive sample of 60 adults from an existing nationwide cohort of women diagnosed with breast cancer at age 40 or younger, the Young Women's Breast Cancer Program ; women with low or moderate family history of breast cancer and no identified BRCA1/2 mutation (n = 15); women carrying a known deleterious BRCA1/2 mutation (n = 14); and women who had not received genetic testing (n = 16). Family history of breast cancer was scored by an experienced genetic counselor and classified as strong ; moderate ; or low . Study participants were not informed of their family history score.In order to investigate the preferences of different subgroups of women within the YWBCP cohort, we used a targeted sampling strategy that was stratified by family history of breast cancer, having received prior genetic testing for n Patton . The fouWe developed a semistructured interview guide based on existing literature, which was refined based on initial interviews. The interviews began with an introduction to the topic of genome sequencing and a few general questions. Then, open‐ended questions addressed interest in six possible types of secondary results that could be returned from genome sequencing, variants that: (1) related to risk of a preventable or treatable disease; (2) risk of an unpreventable or untreatable disease; (3) affected treatment response; (4) uncertain significance (VUS); (5) carrier status; and (6) no health meaning . Each type of possible secondary result was described with examples before the related questions. After discussion of participants' interest in each type of possible result, participants were asked a series of open‐ended questions to explore how they would like individual genome sequencing results delivered, by whom, and when for both research and clinical contexts. These questions about delivery preferences  were the focus of this analysis. We asked participants to think about their delivery preferences overall and then about differences by type of result.Two trained master's‐level research staff conducted the interviews, which lasted about 90 min. Interviewers were free to follow the flow of conversation and vary the question order. They were encouraged to probe on responses using follow‐up questions to elicit more detail. All interviews were digitally recorded and transcribed verbatim. Each participant received a $50 gift card for her participation.The analysis presented here focused on data regarding how, when, and from whom the participants preferred that individual genome sequencing results be delivered. We conducted a directed thematic analysis of the qualitative interview data  carried at least one deleterious mutation. Mean age at diagnosis was 37 years (range 27–40); mean time since diagnosis was 9 years. About 75% had a college degree or higher.The current ages of participants ranged between 33 and 64 years. Most (97%) were Caucasian. The majority (73%) had received prior genetic testing for Face to face so there would be the opportunity to ask questions. I would not wanna get it in the mail. I would not wanna get it on the phone. I would want a setting… where there's someone there who can tell me what was found and explain it to me; and explain to me what this means; and be available to answer questions that I have.”“Most participants (83%) preferred receiving individual genome sequencing results in person, as part of a discussion. This preference for in‐person delivery was often so that the participant could ask questions, thereby getting a better understanding of the information. For example, Participant 50 explained her preference as:Um, I would like to meet with someone simply because I know I would have questions, that's very confusing to me and I would need explanation …. of the significance of the information.” (Participant 5)Another participant emphasized the potentially confusing nature of the results and need for explanation as underlying her preference for in‐person delivery: “Definitely in person …… Because I think that it carries a lot of potential anxiety with it, so I think that having that one‐on‐one contact with somebody who really understands it and could kinda talk you through it … I mean that would definitely be my preferred method. …… Not just receiving it in a letter, and feeling really upset and not knowing what it means.”“A second common reason for wanting in‐person delivery of results was the desire for help in managing the anxiety that could be generated by the information. For example, Participant 28 said:I am a reader, so I would love a big fat pack of information that I can read through at my own pace” (Participant 40). However, many of these participants saw a written document as a supplement to an in‐person discussion. For example, Participant 12 said:I'd like a combination of something in writing, as well as a verbal discussion with somebody to help make it relevant and understand the key things. … But I want to have somebody discuss it all with me because, one, you can always read into things that aren't there, two, you may miss things, and three; it's a matter of helping to manage expectations … as well as to put it in perspective.”“About 31% of participants wanted to receive results in a written document: “I think it's helpful to have both a human contact and something printed you could take home and read through again. And then … you could call if you had further questions just to explain the, you know, scientific part of it…”For some participants, the preference for both in‐person and written delivery of results reflected a desire for a follow‐up process to answer additional questions. Participant 55 described her ideal process as: “I think it depends on the news that's being delivered. I think the ideal, for something complex, where you'd have to really discuss the implications, is a different kind of delivery.” In general, these participants felt that in‐person discussions were more important for results with high relevance, complexity, or personal impact, whereas written reports were thought to be more suitable for results with less personal relevance or importance to disease risk. For example, in discussing results for variants related to risk of an unpreventable or untreatable disease, Participant 11 said: “My fears are kinda the Parkinson's and that sort of, or the Alzheimer's. I guess if there was maybe news to deliver or something along those lines, maybe from a counselor, um, backed up by a report.” Participant 51 described her thought process as:And if there's something very delicate, for example, if one of my variations shows that, this women, it's amazing she hasn't keeled over from a heart attack already. That, then I'd better get a phone call. If it's not devastating, I guess, I'm okay getting a packet in the mail. But if there's information and it's very sensitive and could be very devastating to me or somebody in my family, I want somebody to call me in.”“ Some participants had differences in preferences for how the results would be delivered based on the type of result. Participant 14 said: “My doctor. So we would both know and if I had questions.” In contrast, Participant 53 said: “Probably a genetic counselor. Because that's their business. They know the most about it.” For a few participants, the best person to deliver the results depended upon whether the results related to cancer or not. Participant 11 commented “I guess if it was cancer related from the oncologist…. I guess if it was maybe I'd say anything other than cancer, then maybe by the primary care.”In response to the open‐ended questions about who they would like to deliver individual results from genome sequencing, participants gave a variety of responses, including a researcher (34%), primary care provider (32%), and genetic counselor (31%). For example, Participant 8 responded: “someone who understands what the results mean and can answer my questions.” Similarly, Participant 29 said “any of [the possible professionals] as long as they kinda knew what they were talking about, I'd be fine with it. Somebody that knows the information.” In addition to the information delivery characteristics identified by most participants , a few participants highlighted the importance of compassion and empathy in the person delivering the results. For example, Participant 32 said “someone that has compassion about the situation and not just … a core scientist, but someone that is good with the delivery, good bedside manner.” Another commented “I think just somebody who's knowledgeable and caring, who's good at talking to people” (Participant 28).However, rather than focusing on a specific role , most participants focused on the ideal characteristics of the person who would deliver results. Across participants, the same three characteristics emerged as important: knowledgeable about genome sequencing, ability to explain the results in a way they could understand, and allowing an opportunity to ask questions. For example, Participant 22 commented “I wouldn't mind if (my doctor) gave it to me and then referred me to somebody that really understood it better than they did, where they could explain it better or answer questions that my doctor might not be able to” (Participant 24). However, others felt that the person delivering the results should be able to explain the information and answer questions without having to refer the patient on. For example, Participant 52 said:“Someone that's knowledgeable about the topic, I mean, obviously like a genetic counselor. You can't get any better than that. Somebody that can answer questions, that's knowledgeable. … A long time ago, I had a test ran. … It was some silly thing that a primary care physician did. And then they gave me the results, and it was just like, ‘If you have any questions then you can go to this, you know, website or call this person.’ I'm like, ‘are you kidding me?’… I mean if you're gonna tell me that I'm gonna have a life‐threatening disease that can't be prevented or treated, I want you be able to explain to me… without having to refer me to someone else.”In a few cases, participants felt that a team approach might be beneficial. For example, one participant said “I didn't get [genetic information] at the beginning but I would take it now. As soon as they come out with this, as soon as, it's available.” Another said “I guess as soon as possible if they are interested in getting that information… Because it might help in the decision‐making process for screening, prevention, treatment decisions” (Participant 45). The most common reasons stated for this timing preference were so that women could gain more knowledge and feel better informed and so that they could make the best choices for themselves and their families. Participant 39 explained how sequencing results might impact a range of possible decisions:Because when I was first diagnosed, I just had, I didn't obviously have the genome sequence, but I had genetic counseling and I found out I carried the gene. It made me more susceptible for ovarian cancer. It was kind of like an aha moment, like I need to do something to prevent that. If there's something that they can do to prolong their life, then I think it is beneficial. Just knowing that, it also made my oncologist start doing, with the history of pancreatic cancer, they started doing the pancreatic tumor levels just to keep an eye on them to see what my borderline starting range was, to see, five months ago they went up. What happened? Should we start watching her more closely in that area? I think yeah, in the beginning would be good.”“Almost all participants thought that sequencing results should be delivered at the time of diagnosis with breast cancer, or if the information was not available then, as soon as possible. For example, Participant 6 commented “I think anything you can share would be really, really helpful. I think it's key to understand that, for different points in your life, people need different information and different treatments, because you wanna be super‐aggressive when you're young. You wanna live a long time. You get it when you're 75, you know, it's a different path, very different. Absolutely, as soon as possible.”“Some participants linked a need for information from genome sequencing to their young age at breast cancer diagnosis, stating that younger cancer patients will have different information needs than older patients. For example, Participant 32 said:Another participant commented:Well, I think it's important to learn at a young age, because, I think, then they can start planning. I think, um, you know, they can get the options, because some women could maybe have eggs frozen, if they think they're gonna have their ovaries removed. They might choose to have a prophylactic mastectomy, when they could have reconstruction, without waiting until after the fact, and not being, you know, able to have all the options. So, I think it's helpful, when you're younger, because you have more options (Participant 41).“I mean when you're diagnosed, anything you can learn to make those treatments more applicable to who you are and what your body makeup is, I think that would be great.” (Participant 32). Participant 28 described a process for delivery of different types of results over time:Well, I mean, … the part where, about how you respond to medication and treatment would be right up front. That might be the most useful place for that particular piece of the information, because it seems like it make a huge difference in, you know, maybe the course that they would take. Um, and then maybe, um, I mean some of the other pieces could come later. I just know that there's so much information you get right at first, and it's such a total shock that you don't take in a lot, really.”“Some participants did distinguish between different types of results in considering when the information should be returned. For these participants, results relevant to treatment response were seen as most salient at the time of diagnosis: “There is so much, you're bombarded with so much information at the beginning of your diagnosis, it might be a little bit scary. So, um, I guess the oncologist or whomever is deciding the treatment plan should probably, um, check to see how, uh, mentally, uh, strong and what kind of support system that that young breast cancer, um, patient has. And, um, you know, maybe they'd be able to see if she'd be able to handle all the information at one time, because there's just so much information at the beginning that maybe, uh, maybe they should wait to see when it kind of calms down, so.”“In addition to type of result, some participants also believed that the optimal time for results delivery would depend upon factors such as the patient's prognosis and her coping and available social support. Participant 57 commented:Or at least give them that opportunity to pursue that at the beginning, even before surgery and things like that. I mean, I guess give them the opportunity, and then it has to be a, you know, a personal decision if they wanna do that, because they need to take into consideration all‐I guess themselves and their families and what it might mean, and that sort of thing (Participant 11).”“Because the optimal timing for results delivery was thought to vary for different patients, some participants emphasized that patients should have a voice in planning for results delivery.In this analysis, we examined delivery preferences for genome sequencing results among women diagnosed with breast cancer at a young age, focusing on secondary findings from sequencing. The major findings from the study were that: (1) many of the participants wanted individual sequencing results as soon as possible, even at the time of breast cancer diagnosis; (2) participants wanted an opportunity for an in‐person discussion of results, not simply a report; and (3) they put less emphasis on the type of person delivering results than on the knowledge and communicative skills of that person. While we observed some differences in delivery preferences by type of sequencing result, as described above, the different participant subgroups had quite similar preferences. This suggests that clinical differences between patients might not be the most important factor to consider in designing strategies for return of secondary findings, and that the characteristics of various secondary findings are likely more important.For the timing of results delivery, we found that participants overwhelmingly thought that the results from genome sequencing should be returned at the time of breast cancer diagnosis, and if the information was not available then, as soon as they were available. This preference was based on the importance of feeling informed, particularly as women make choices about treatment. Some participants also mentioned the importance of young women receiving as much information as possible for making lifestyle choices. Some participants did prioritize information salient to treatment response as most critical to receive at the time of diagnosis, with the timing of return of other types of results as more flexible depending on the woman's personal circumstances. It is important to note that, currently, the results from single gene or panel testing typically take two to three weeks from the time of testing, and the turnaround time for whole genome sequencing is likely to be longer. This may limit the utility of genome sequencing results for immediate treatment planning and is something that would be important to discuss with patients at the time the test is ordered. In addition, at the time of diagnosis, it would likely be important to discuss with patients that results from tumor profiling may be informative for treatment decision making.This preference for receiving sequencing results as soon as possible, even at the time of breast cancer diagnosis, contrasts with prior literature and many expert recommendations. In a study of participants in a clinical sequencing protocol, the majority strongly preferred an iterative process for return of results  were similarly identified (Yu et al. This preference is concordant with common current practices in return of genome sequencing results. A survey of genetic counselors showed that patients primarily receive results from sequencing in person (Machini et al. Research is therefore needed to examine approaches to supplement in‐person delivery of genome sequencing results and patient preferences related to these approaches. For example, as suggested by the participants in this study, return of high impact results may be most critical for in‐person delivery by a genetic counselor. However, some types of results may eventually be returned by a primary care provider or interactive, web‐based format. Many physicians today lack specialized genetics training and are thus unprepared to deliver genome sequencing results (Guttmacher et al. The findings should be interpreted in light of the study's limitations. We examined how women diagnosed with breast cancer at a young age would prefer to have sequencing results delivered, but subsequent studies are required to test cognitive, affective, and behavioral responses to different strategies for returning these results. Our participant sample was largely Caucasian and preferences may differ by race and ethnicity (Meiser et al. Despite these limitations, this study adds to our understanding of preferences for return of results from genome sequencing, including secondary findings, among women diagnosed with breast cancer at a young age. Overall, women preferred receipt of the information as soon as possible with a strategy of in‐person delivery of results by a clinician with specialized genetic knowledge. However, another theme that emerged through these findings is the importance of making this a process tailored to the woman's individual circumstances and one that she has a voice in determining. A critical goal for future research will be to develop processes to help patients make decisions about how they would like various results from genome sequencing returned and assist patients in distinguishing between results related to the primary indication for testing and secondary findings that are not related to this indication. One possible approach would be shared decision making, in which providers and patients decide together. An alternative approach would be that providers would participate in education and discussion with patients about their options but that patients would have autonomy to make the decision about return of results. An important direction for future research would be to examine different decision‐making approaches for this issue. A transdisciplinary approach in which communication and patient education experts partner with patients, genetic counselors, clinical geneticists, and other health care providers to develop and test innovative approaches to decision making and return of results will allow us to meet patients' preferences and needs.The authors have no conflicts of interest to declare.


Texture patterns have been shown to improve breast cancer risk segregation in addition to area-based mammographic density. The additional value of texture pattern scores on top of volumetric mammographic density measures in a large screening cohort has never been studied.Volumetric mammographic density and texture pattern scores were assessed automatically for the first available digital mammography (DM) screening examination of 51,400 women (50–75 years of age) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. The texture assessment method was developed in a previous study and validated in the current study. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. All screen-detected breast cancers diagnosed at the first available digital screening examination were excluded. During a median follow-up period of 4.2 (interquartile range (IQR) 2.0–6.2) years, 301 women were diagnosed with breast cancer. The associations between texture pattern scores, volumetric breast density measures and breast cancer risk were determined using Cox proportional hazard analyses. Discriminatory performance was assessed using c-indices.p value for trend <0.001), for quartile (Q) 4 compared to Q1). The c-index of texture was 0.61 (95% CI 0.57–0.64). Dense volume and percentage dense volume showed positive associations with breast cancer risk (HR 1.85 (95% CI 1.32–2.59)  and HR 2.17 (95% CI 1.51–3.12) , respectively, for Q4 compared to Q1). When adding texture measures to models with dense volume or percentage dense volume, c-indices increased from 0.56 (95% CI 0.53–0.59) to 0.62 (95% CI 0.58–0.65) (p < 0.001) and from 0.58 (95% CI 0.54–0.61) to 0.60 (95% CI 0.57–0.63) (p = 0.054), respectively.The median age of the women at the time of the first available digital mammography examination was 56 years (IQR 51–63). Texture pattern scores were positively associated with breast cancer risk (hazard ratio (HR) 3.16 (95% CI 2.16–4.62)  contains supplementary material, which is available to authorized users. Many countries have a breast cancer screening program . The intBesides mammographic density, mammographic texture patterns have also been shown to be associated with breast cancer risk, and also to improve breast cancer risk segregation in addition to area-based mammographic density –21. ThesAll these texture features are manually designed and selected and will only capture mammographic, risk-prone patterns to the extent the feature designs were relevant. This problem is normally overcome by initially using large banks of potential features but only maintaining the informative ones in the final classification system , 23. HowTherefore, the aim of this study was to determine the association between a previously developed deep-learning-based texture score  alone anIn the Netherlands, women aged 50–75 years have been invited for mammographic breast cancer screening every other year from 1989 and onwards. Approximately 80% of the women attend the screening program . Since 2The research ethics committee of the Radboud University Nijmegen Medical Centre declared that this study does not fall within the remit of the Medical Research Involving Human Subjects Act. Therefore, this study could be carried out (in The Netherlands) without approval by an accredited research ethics committee.We selected each woman’s first unprocessed (raw) digital mammography examination. All mammograms were taken using Lorad Selenia DM systems . During the first examination in the screening program, both craniocaudal (CC) and mediolateral oblique (MLO) views are always acquired. In subsequent rounds the MLO is the standard view and an additional CC view is taken only when indicated . Information during follow up was obtained through the screening registration system and through linkage with the Netherlands Cancer Registry to obtain complete information on both screen-detected and interval breast cancers. Screen-detected breast cancers were defined as breast cancers diagnosed on the basis of diagnostic work-up of an “abnormal” screening examination. Interval breast cancers were defined as breast cancers diagnosed within 24 months after a screening examination that did not lead to recall (negative mammogram), and before the next scheduled screening examination. The median time between the first available digital screening mammogram and breast cancer diagnosis was 3.7 years  for screen-detected breast cancers and 2.2 years  for interval cancers. Both invasive and ductal carcinoma in situ breast cancers were used for analyses.We excluded all screen-detected breast cancer cases that were diagnosed based on the first digital screening examination, to minimize the number of breast cancer cases in the study that were diagnosed based on the same mammogram as was used for breast density and texture score assessment.The texture measure used in this study was previously developed using a selection of women with and without breast cancer who had one or more digital mammographic screening examinations at the Preventicon screening unit between 2003 and 2011 . TherefoThe data were obtained through the registry of a breast cancer screening program in which mammograms are routinely collected. Therefore, besides age, no additional information was available about the women.Absolute dense volume (DV) and percentage dense volume (PDV) were automatically assessed from unprocessed mammograms of the left and right breasts, using Volpara Density  . We usedThe deep-learning-based mammographic texture-based risk assessment was calculated from unprocessed mammograms using prototype software by Biomediq A/S as described by Kallenberg et al. . The deeThe left and right MLO views in the remaining independent validation subset of the Preventicon cohort were scored for texture-based risk using the framework above. The texture score for a single screening visit was obtained as the average of the left and right MLO texture risk scores. This scoring was performed such that both software and operator were fully blinded to cancer outcome during scoring. For each MLO view, the software extracted 500 randomly sampled patches within the fully compressed part of the breast tissue. To identify the fully compressed part of the breast, the geometry of the uncompressed breast is modelled as a semi-sphere, as has been proposed in the works of Highnam and Brady . AccordiAn example of mammograms from each of the four combinations of high or low texture score with high or low percentage dense volume is given in Fig. t test for normally distributed measures and the Mann-Whitney U test for non-normally distributed measures. Breast density measures were transformed using the natural logarithm (ln) to obtain normal distributions and Pearson correlation coefficients were determined to test correlation between breast measures and between age and breast measures.Age and breast measures (mammographic density and mammographic texture scores) were determined for the first available unprocessed digital mammography examination of each woman. In addition, the number of digital screening rounds and follow-up years were determined. We described our study population by the median and interquartile range (IQR) for each of these characteristics and tested whether these characteristics were significantly different in breast cancer and non-breast cancer cases. We used the two-sample Associations of continuous measures (per standard deviation (SD) increase, using normally distributed measures) and quartiles of density and texture scores with breast cancer risk were determined using Cox proportional hazards analyses. We calculated hazard ratios (HR) and their 95% confidence intervals (95% CI). Age was used as the underlying time scale. The entry time was defined as subject’s age at the time of the first available digital mammogram. Exit time was defined as one of the following options: (1) age at breast cancer diagnosis (event), (2) age at death (censoring), or (3) age at 2 years after the last digital mammogram performed before 1 January 2012 (censoring). The age used as the exit time was determined by the option that occurred first.We aimed to determine whether the previously described texture score is associated with breast cancer risk and has additional value, next to volumetric mammographic density measures, in distinguishing future breast cancer cases from non-breast cancer cases. To study this, we constructed several models. First, three Cox proportional hazard models were developed with dense volume, percentage dense volume, or texture as the determinant . With these models we could determine the ability of a density or texture measure alone to separate breast cancer from non-breast cancer cases. Thereafter, we constructed two additional Cox proportional hazard models. The first contained both dense volume and texture determinants (model 1a). The other model contained both percentage dense volume and texture determinants (model 2a). To determine the ability of the models to discriminate between breast cancer cases and non-cases, concordance indices (c-indices) were obtained for all models. The c-index can be seen as the fraction of “case - non-case” pairs for which the model correctly identified the breast cancer case. Across 2000 bootstrap samples, c-indices of models containing only a breast density measure (model 1 or 2) were compared to models containing both density measures and texture scores (model 1a or 2a) to test whether differences in c-indices were statistically significant.As the density and texture scores were expected to be strongly correlated, we prevented multicollinearity from occurring in models 1a and 2a by including the residuals of the texture scores regressed on breast density instead of the texture score itself. This “residual method” is often used in the field of nutritional epidemiology . ResiduaAdditionally, two extra Cox proportional hazard models were constructed in which the residuals of breast density (dense volume for model 3a and percentage dense volume for model 3b) regressed on texture were combined with the texture score. Using these models, we could determine whether breast density measures added some distinctive power to the texture score alone.The proportional hazards assumption was evaluated by Schoenfeld residual plots and log minus log plots, and the assumption was not violated. To examine the presence of a linear trend in HRs over the quartiles of breast measures, quartiles were added to the models as continuous variables.Finally, in a secondary analysis we also separately determined the associations between breast measures  and breast cancer for screen-detected and interval breast cancers. Statistical analyses were performed using SPSS version 22 and R version 3.2.0.N = 20) and for whom the screening examination date came after the date of death  (N = 10). This resulted in a dataset that was used for data analysis containing 51,400 women, of whom 301 women developed breast cancer and 51,099 women did not  from the aforementioned cohort were used for texture score development and therefore excluded from our analyses . Next, 2N = 51,400) was 56 years (IQR 51–63), the median dense breast volume was 57.8 cm3 (IQR 42.9–78.9), the percentage breast volume was 6.4% (IQR 4.8–9.8) and the texture score was 0.50 (IQR 0.48–0.53). The median total number of screening examinations  that a woman had was 5 (IQR 2–8) of which the median number of digital examinations was 2 (IQR 1–3). The median follow-up time was 4.2 years (IQ: 2.0–6.2).Characteristics of the study population are presented in Table p < 0.01), percentage dense volume , and texture . Percentage dense volume and texture were strongly positively correlated (0.90 (p < 0.01)). Finally, dense volume was positively correlated with percentage dense volume  and texture .Table p value for trend <0.001). There were comparable results for percentage dense volume . Women with a high texture pattern score had three times higher breast cancer risk than women with a low texture pattern score .High mammographic dense volume, percentage dense breast and texture scores were all associated with a higher breast cancer risk (Table p < 0.001). When both dense volume and texture residuals were included in the model, they were both positively associated with breast cancer risk .When the residuals of texture scores regressed on dense volume were added to the model with only dense volume (model 1a vs 1 in Table p = 0.054). In the model with both percentage dense volume and texture residuals .When the residuals of texture scores regressed on percentage dense volume were added to the model containing only percentage dense volume (model 2a vs 2 in Table The results of the models including continuous breast density measures were in line with those including quartiles of breast measures Table .percentage dense volume regressed on texture scores) are presented in Additional file p = 0.076 and p = 0.760, respectively).The results of model 3a  and model 3b  in relation to breast cancer risk. In a review by Gastounioti et al., studies were described in which computerized approaches with manually designed and selected texture features were used for breast cancer risk assessment on both digitized film screen and digital mammograms . In someThe current texture measure was developed in a relatively large population (394 cases and 1182 controls) compared to other studies developing a texture measure , 24. TheIn our study, we used a cohort design, studying mammograms in a breast-cancer-free cohort and then following up for breast cancer diagnosis, for an average period of 4 years. In the previously discussed studies mammograms of the contralateral breast at the time of breast cancer diagnosis were used –31. For All studies investigating the combination of texture and breast density in relation to breast cancer risk or the ability to separate breast cancer from non-breast cancer cases used area-based percent breast density measures. The most widely used quantitative are-based breast density assessment method is the semi-automatic method, Cumulus . This isA limitation of our study is that as far as potential confounders are concerned, we only had information about age. We made use of anonymized routinely collected screening data and the Dutch screening program, like many other screening programs, does not collect any information on risk factors. In studies where adjustment for breast cancer risk factors, in particular body mass index, was possible, this usually led to slightly higher risk estimates for percent density , 22. TheStrengths of this study are the automatically measured density and texture scores, as they give objective and reproducible results. In addition, these are high-throughput methods which make them suitable for screening practice. Finally, the ability of breast density and texture to discriminate non-breast cancer from breast cancer cases in a population-based breast cancer screening cohort resembles the performance of these measures in real screening practice, probably better than when using a case-control study.Deep-learning-based texture pattern scores measured automatically on digital mammograms were shown to be related to breast cancer risk. Additionally, texture pattern scores statistically significantly improved the discriminatory performance in addition to absolute dense volume. Therefore, texture measures in addition to density may be taken into account in the development of more personalized breast cancer screening.Additional file 1:Table S1. Texture measures in combination with breast density and breast cancer risk. (DOCX 16 kb)Additional file 2:Table S2. The association between breast measures and screen-detected breast cancer risk. (DOCX 17 kb)Additional file 3:Table S3. Texture measures in combination with breast density and screen-detected breast cancer risk. (DOCX 16 kb)Additional file 4:Table S4. The association between breast measures and interval breast cancer risk. (DOCX 17 kb)Additional file 5:Table S5. Texture measures in combination with breast density and interval breast cancer risk. (DOCX 15 kb)


The second is mammographic density, originally developed by Wolfe but now available in both quantitative area based (Cumulus) and volumetric forms (Volpara). The third is a panel of common low penetrance single nucleotide polymorphisms (SNPs). Over 100 of these have now been validated [BRCA1 or BRCA2 and the dozen or so immediate risk genes such as ATM, CHEK2 and PALB2 [Accurate risk assessment for breast cancer is based on information from three domains. The first consists of classical factors which can be determined from interview or questionnaire, and include age, family history of breast or ovarian cancer (with age of onset), menopausal status, body mass index, use of hormone replacement therapy, age of first childbirth and prior proliferative benign breast disease (with or without atypia). These factors can be combined in programmes such as the Tyrer-Cuzick (TC) model, which is freely downloadable .Accurate risk prediction is essential for risk adapted screening algorithms, which are currently being explored in different settings. In the long-term, breast screening programs should be expanded to provide risk assessment and take on a breast cancer prevention activity. This is would be best based on a single risk assessment at the time of the first mammogram around age 40 to 50y, and would include classical risk factors as provided in the TC model, as well as density based on the first mammogram and a SNP score. In addition to identifying high-risk women who might benefit from preventive therapy, this approach could be used to guide the frequency of subsequent screening and determine which women need additional types of screening such as MRI, and which may need little or no screening at all.


Breast Cancer Research, Shawon and colleagues provide more evidence of consistency for the inverse associations between childhood and adolescent body size and breast cancer risk [In epidemiology, inconsistencies are quite common, consistency less so. In this issue of cer risk . Consistcer risk –4), theycer risk  and mammcer risk .Shawon and colleagues  use pictEpidemiologic inference, particularly with observational studies, can be enhanced by repeated measures. Shawon and colleagues observed a 10% reduction in breast cancer risk for those who reduced a major body size category from 7 to 18 years , but wheBRCA1 [BRCA1 to function as a tumor suppressor [The consistent inverse association between early-life body size and breast cancer risk is matched by a similarly consistent association between larger adult body size and increased breast cancer risk. Given the positive association between large body size in adulthood and breast cancer as well as many other cancers and chronic diseases, and the fact that adult body size is shaped by growth trajectories much earlier in life , the conBRCA1 . RecentlBRCA1 . Interesppressor , pre-pubppressor . Thus ovppressor . Much stppressor .In addition to measuring childhood and adolescent growth prospectively and using pre-pubertal biospecimens to measure whether biomarkers of the pre-pubertal environment help to explain differences in breast tissue characteristics based on body size, there remains another piece of the puzzle that needs to be completed. Specifically, it is unclear whether the breast cancer risk in girls that are larger in childhood and adolescence and are large at birth and infancy differs from the risk in girls who start small and grow rapidly. The inverse association between adolescent body size and breast cancer risk may not be apparent for the former . Epidemi


In spite of the increasing incidence of in situ breast cancer, the information about the risk factors of in situ breast cancer (DCIS) is scarce as compared to the information available for invasive ductal breast cancer (IDC), with inconsistent findings regarding the difference in risk factors between DCIS and IDC.We enrolled 472 women with IDC and 90 women with DCIS and 1088 controls matching for age and menopausal status. Information on risk factors was collected through self-administered questionnaire. Percent mammographic dense area (PDA), absolute mammographic dense area (ADA), and nondense area were assessed using a computer-assisted thresholding technique. Odds ratio (OR) and 95% confidence intervals (CI) were estimated by conditional logistic regression model with adjustment for covariates.P > 0.1).Later age at menarche and regular physical exercise were associated with decreased risk of IDC, whereas alcohol consumption, previous benign breast disease, and family history of breast cancer were associated with increased risk of IDC. For DCIS, previous benign breast disease and alcohol consumption were associated with the increased risk, and regular physical exercise was associated with decreased risk. Increase of ADA by 1-quartile level and PDA increase by 10% were associated with 1.10  and 1.10  times greater risk of IDC, respectively. The increase of ADA by 1-quartile level and PDA increase by 10% were associated with 1.17  times and 1.11  times greater risk of DCIS, respectively, but the associations were not statistically significant. There was no significant difference in the association with risk factors and mammographic density measures between IDC and DCIS  contains supplementary material, which is available to authorized users. Over the past several decades, the incidence rate of in situ breast cancer has increased worldwide, probably due to the widespread use of mammograms for breast cancer screening –3.Ductal carcinoma in situ (DCIS), the most common type of in situ breast cancer, is the proliferation of presumably malignant epithelial cells confined to the mammary ducts and lobules without evident stromal invasion through the basement membrane . DCIS isHowever, findings regarding the difference in risk factors between DCIS and IDC were inconsistent, and the information about the risk factors of DCIS was less available than for IDC, especially for Asian women. Some studies reported similar associations with risk factors such as family history of breast cancer, previous breast biopsy, or parity between DCIS and IDC –14, wherWe therefore conducted a case-control study in Korean women to evaluate the associations of breast cancer with risk factors including MD, separately for DCIS and IDC. Considering that most of the previous studies have been conducted on Western population and information on the risk factors of breast cancer by invasiveness for Asian women population was scarce, it is hypothesized that the findings from this study on the extent to which DCIS and IDC share the risk factors may provide awareness regarding the natural history of breast cancer in Asian women.We included a total of 562 breast cancer patients (472 IDC and 90 DCIS), who received curative surgery at Samsung Medical Center between February 2006 and August 2013 and had available data for MD and pathologic status. Of the 562 cases, 186 cases were recruited retrospectively from the Health Promotion Center of the Samsung Medical Center, while 376 cases were prospectively recruited from the department of surgery. Controls were randomly selected from 6863 women who had repeatedly (at least three times) received a periodic health checkup at the Health Promotion Center in the Samsung Medical Center and had no evidence of malignant breast disease for at least 1 year after the time at which mammogram for the present study was taken. We selected two controls for each breast cancer case through individual matching for menopausal status and age (within one year) at which mammogram was taken, except for 36 cases for whom only one control could be selected because of the limited control pool within matching strata. Thus, 1088 controls (912 for IDC cases and 176 for DCIS cases) were included in the final analysis. This study was approved by the Institutional Review Board of Samsung Medical Center (SMC 2011–06–052-022). The Board waived informed consent for the retrospectively recruited subjects, and all prospectively recruited subjects provided written informed consent.2) and area of mammographically dense tissue  directly from the cranio-caudal view using the computer-assisted thresholding technique . Subsequently, we calculated nondense area (cm2) of breast by subtracting ADA from total breast area and percentage dense area (PDA) as ADA divided by total breast area. MD measurement by Cumulus was reported to be highly reproducible [Mammograms were taken at the same institution using a full-field digital mammography system such as Senograph 2000D/DMR/DS  or Selenia . For breast cancer cases, MD of the breast contralateral to the breast involved in the cancer diagnosis was measured in the mammograms taken 1.0 (standard deviation: 2.1) months prior to the diagnosis. For controls, MD of the right breast was measured. Single observer who was blinded to all identifying information completed the measurement of total breast area , PgR by IA6 , and HER2 by CB11 . ER and PgR positivity was defined as an Allred score of 3 to 8. Allred scoring semi-quantitatively measures the proportion of positive cells on 0 to 5 scales and staining intensity on a 0 to 3 scale. Positivity for HER2 overexpression was defined as a score of 3+  on immunohistochemistry or as a gene amplification ratio ≥ 2.0 by fluorescence in situ hybridization using PathVysion HER2 DNA Probe kits .2) was calculated using measured height (cm) and weight (kg).Family history of breast cancer among first-degree relatives , previous benign breast disease; menstrual and reproductive history ; and health-related behaviors  were collected using a self-administered questionnaire. All control subjects and retrospectively recruited 186 cases completed the questionnaire on the same day when they received a mammogram, and prospectively recruited cases completed the questionnaire when they were admitted to the hospital for surgical treatment. We defined a woman postmenopausal if she had no menstrual period for at least one year, has ever received hormone replacement therapy, or aged over 55 years. Study participants were divided into two or three categories for each of the following variables: alcohol consumption , smoking , frequency of regular physical exercise , and use of hormone replacement therapy . Body mass index , we imputed data using the average value for the variable among controls in the same age group. We compared demographic and clinical characteristics between cases and controls by paired t test and Cochran-Mantel-Haenszel chi-square test. We also compared demographic and clinical characteristics between IDC and DCIS cases by t test and chi-square test.We estimated odds ratio (OR) with 95% confidence intervals (95% CI) for DCIS and IDC associated with MD and clinical risk factors by fitting a conditional logistic regression model for matched case-control study data. For estimating the association between MD and breast cancer, we adjusted covariates including age, menopausal status, height, BMI, age at menarche, number of live birth, smoking status, alcohol consumption, regular physical exercise, family history of breast cancer, previous benign breast disease, and use of estrogen replacement therapy. Furthermore, to reduce he probable confounding by the different recruitment method, we adjusted the recruitment method in addition.We evaluated whether the association of breast cancer and clinical risk factors with MD differs between IDC and DCIS by adding interaction terms (invasiveness x each variable) to the analytic model.In addition, we did stratified analysis according to the method of case recruitment and checked whether there is influence of recruitment method by examining interactions between the recruitment center and the variables on the breast cancer risk, separately for DCIS and IDC. We also did stratified analysis to examine whether the association of BMI with DCIS and IDC differed according to the menopausal status, and checked interaction between menopausal status and BMI, separately for DCIS and IDC.P = 0.05.All statistical analyses used the SAS statistical package  with the level of statistical significance set as DCIS cases occupied 16.0% (90 cases) of all the breast cancer cases. Clinical and lifestyle characteristics and mammographic measures were compared between cases and controls, and between IDC and DCIS (Table P > 0.1).Table We checked the influence by the method of case recruitment  as compared with the lowest , the ORs were 3.58  and 2.92  for IDC, and 2.86  and 2.59  for DCIS [Mammographic density is a well-established strong risk factor for invasive breast cancer . Epidemifor DCIS  without for DCIS . A largefor DCIS . A Britifor DCIS . In our for DCIS .Interestingly, the estimates (OR (95% CI) for the association between MD and breast cancer in our study tend to be weaker than the magnitude of association observed in the above mentioned studies in Western population: the risk associated PDA ≥ 40% was 1.54 1.02, 2.31) for IDC and 1.90  for DCIS in our study. Different strength of association between MD and breast cancer has been frequently reported across different ethnic groups , 30, and for IDC The association between age at menarche and in situ cancer was controversial with either null , 13, 16 In the present study, we found no significant association of BMI with IDC as well as DCIS. In studies that did not differentiate premenopausal and postmenopausal breast cancer, no association between BMI and in situ cancer and a positive association between BMI and invasive cancer have been reported. , 16 MostIt has been suggested that the risk factors operating early in life such as family history might be involved in the initial stages of carcinogenesis, resulting in in situ cancer, and other factors needed to continue promoting the tumor to invasive cancer . A studyThe risks of IDC and DCIS have been consistently found to increase in women with a history of benign breast disease , 16, 33.P = 0.105). Trentham-Dietz et al. also reported that ORs (95% CI) for ≥183 g/week of alcohol intake were 2.34 for DCIS and 1.76 for IDC but without statistical difference between them [Regular physical activity has been proposed as an independent protective factor of breast cancer , 41, whi41.32, 4. for DCISThe present study had some limitations. First, our study may have weakness with respect to representativeness that is commonly innate in a hospital-based case-control study. As controls were recruited from participants in a health checkup program, selection bias may exist. We tried to overcome this bias by considering a wide range of covariates including health behaviors such as physical exercise, alcohol consumption, and smoking habit. Second, we recruited cases in two ways (retrospectively or prospectively), and this might have incurred bias in the study findings due to the health behavior modification or recall bias. To reduce the probable confounding by the heterogeneous recruitment method, we adjusted for the recruitment method. However, the significant interaction by the recruitment method on the association of IDC with physical exercise and ever-use of estrogen replacement with different direction of the estimates for the association between the retrospectively (positive) recruited subjects and prospectively (inverse) recruited subjects suggests that careful interpretation is necessary, especially for the association of IDC and those two factors. Third, we could not consider the mode of breast cancer detection and could not examine the association of age at menopause, age at first birth, lactation, and oral pill with IDC and DCIS because of the lack of information. Fourth, because of the low proportion of DCIS among breast cancer, we may have included relatively small number of DCIS cases in this study and, thus study power could have been inadequate. Finally, given that ‘benign breast disease’ constitutes a heterogeneous group of breast lesions, evaluation of breast cancer risk associated with the benign breast disease could have been too vague to give useful clinical information.On other hand, our study has some strength. First, the influence of age and menopausal status was strictly controlled through individual case-control matching and statistical adjustment. Second, a wide range of covariates was considered: BMI, age at menarche, number of children, use of estrogen replacement therapy, lifestyle factors, previous benign breast disease, and family history of breast cancer among first-degree relatives. Third, we measured MD quantitatively using a computer-assisted thresholding technique.In conclusion, differential associations of DCIS with mammographic density and risk factors as compared with the associations of IDC were not evident. This finding suggests that IDC and DCIS develop through the shared causal pathways.


The relationship between vitamin D and breast cancer is still controversial. The present meta-analysis examines the effects of the 25(OH)D, 1,25(OH)2D and vitamin D intake on breast cancer risk. For this purpose, a PubMed, Scopus and Web of Science-databases search was conducted including all papers published with the keywords “breast cancer” and “vitamin D” with at least one reported relative risk (RR) or odds ratio (OR). In total sixty eight studies published between 1998 and 2018 were analyzed. Information about type of study, hormonal receptors and menopausal status was retrieved. Pooled OR or RR were estimated by weighting individual OR/RR by the inverse of their variance Our study showed a protective effect between 25 (OH) D and breast cancer in both cohort studies  and case-control studies . However, analyzing by menopausal status, the protective vitamin D – breast cancer association persisted only in the premenopausal group  when restricting the analysis to nested case-control studies. No significant association was found for vitamin D intake or 1,25(OH)2D. Conclusion: This systematic review suggests a protective relationship between circulating vitamin D (measured as 25(OH) D) and breast cancer development in premenopausal women. The incidence is decreasing every year, which is partly due to early detection programs2.Breast cancer is an important public health problem in developed countries as it is one of the most common cancers, being the most if only the female population is consideredin vitro experiments and in vivo models have evaluated the role of vitamin D in the development of breast cancer, finding a protective anticancer role of 1,25(OH)D33. It has been demonstrated that treating breast cancer cells with 1,25(OH)D3 induces two beneficial effects: an anti-proliferative effect4 and a pro-apoptotic effect6. The former is linked to the suppression of growth stimulatory signals and the potentiation of growth inhibitory signals, whilst the second one is explained by the bcl-2 family proteins. The interaction between vitamin D and its receptors induces an increase in the expression of pro-apoptotic family member (bax and bak protein) and simultaneously a decrease of anti-apoptotic (bcl-2/bcl-XL)6. In addition, the breast tissue contains the 1-α-hydroxylase, allowing for the generation of the active vitamin D metabolite  from the circulating precursor (25 hydroxyvitamin D). As vitamin D receptors are found in the breast6, an autocrine role of vitamin D has been suggested7.In the last decades, cellular 16  or odds ratio (OR) with confidence interval at 95%. (95% CI)65 and seventeen cohort studies81. Tables We began our search in Pub-Med, Scopus and Web of Science database using “breast cancer” and “vitamin D” as keywords, finding 2313 articles. After having read the title and abstract, 2123 articles that did not meet the above criteria were eliminated. Next, we carried out a more exhaustive and complete reading, which allowed us to reject another additional 69 articles D, dietary intake of vitamin D, 1,25(OH)2D and vitamin D supplements.The ways that doses or levels of vitamin D were reported in each individual article were not standardized across studies , making it difficult to extract them in an analyzable form. Therefore, in order to provide a consistent criterion of comparability, we selected the OR or RR reported for the highest category compared to the lowest one.2 statistics83. A fixed-effect model was preferred if the Q statistic was higher than 0.1 or I2 lower than 25%, indicating no relevant heterogeneity; a random-effect model was otherwise chosen84. The presence of small-study bias was explored with Rosenthal model and with Egger test85; due to the low sensitivity of Egger test, the cut-off was set at p = 0.1. Funnel plots86 were applied to detect publication bias.Regarding the type of breast cancer, we analyzed all invasive breast cancers together, and breast cancer stratified according to the cancer estrogen receptor status and woman’s menopausal status. Pooled OR or RR were estimated by weighting individual OR/RR by the inverse of their variance. OR or RR heterogeneity was measured using Q and IAn analysis of influence was performed via the re-estimation of pooled OR/RR by removing one study at a time. Studies that, when removed, strongly changed the OR/RR would be considered as highly influential. Results are displayed as forest plots showing OR/RR and their 95% confidence intervals for each individual study and for the pooled result. Cumulative meta-analyses were carried out to deem the stability of the OR/RR estimates. In order to do that, all studies considered were arranged from oldest to neweest. Then an OR/RR estimate was obtained for the two eldest studies; another for the three eldest, and so on, adding a study each time. Results are reported as forest plots.All the statistical analyses were carried out with the package Stata 14/SE .63 obtaining a pooled OR of 0.65 (95%CI: 0.56–0.76)  of the fixed-effect. Although Egger test cannot rule out a small-study effect (p = 0.001), no study shows a relevant influence. The funnel plot shows asymmetry   D and breast cancer76) Fig. , Table 401) Fig.  with I2 80 provided results on 25(OH)D and breast cancer relationship, from which we obtained a pooled RR of 0.85 (95% CI:0.74–0.98).Four cohort studies80 and five case-control studies45. In both cases (ER+ and ER− tumors) statistical significance was not reached. With respect to menopausal status (Table 81 with a pooled OR of 0.74 (95%CI: 0.59–0.93), and nine focused on premenopausal60 obtaining a pooled OR of 0.63 (95%CI: 0.49–0.80) ).We also analyzed the relationship between 25(OH) D and breast cancer, stratifying results by hormonal receptors (ER+/ER−) and menopausal status . Regarding hormonal receptors Table , we have80) Fig. . When th39 examined the relationship between circulating 1,25(OH)2D and breast cancer; significant association was not found either in the whole analysis (pooled OR = 0.61 (0.33–1.16)) or in postmenopausal women (combined OR = 1.28 IC 95%: 0.98–1.67)37.Three case-control studies64 on the relationship between dietary vitamin D and breast cancer with a pooled OR of 0.91 (95%CI: 0.72–1.17) (Table 72 we obtained a RR of 1.00 (95% CI 0.93–1.07) (Table We found eight case-control studies7) Table . In addi7) Table .64 and five cohort studies77 assessed the risk of breast cancer in postmenopausal women. The pooled OR for case-control studies was 0.78 (95%CI: 0.68–0.90) and the pooled RR for cohort studies was 0.95 (95%CI: 0.83–1.09)  for case-control studies and the RR for cohort studies was 1.01 (95% CI: 0.86–1.18)  and 1.06(95% IC: 0.90–1.25) respectively .In addition, we found two case-control studies54 focused on premenopausal women obtaining a weak protection (pooled OR 0.89 95%CI (0.84–0.95)).We identified five case-control studiesly Table . Regardi71 and four case control studies64 on vitamin D intake (dietary plus supplements) and breast cancer risk, providing no separate results on dietary/supplemented vitamin D origin. We obtained a combined RR = 0.93 (95% CI: 0.86–1.02) for cohort studies, and a combined OR = 0.84 (95% CI: 0.68–1.05) for case-control studies. Five cohort studies77 provided results on postmenopausal women (RR = 0.94 95% CI: 0.87–1.00) and three cohort studies77 on about premenopausal women (RR = 0.90 95% CI: 0.72–1.12) D levels were associated with smaller risk of breast cancer in both case-control and cohort studies; these results were consistent on premenopausal women for case-control studies but could not be analyzed for cohort studies. Results for the relationships between breast cancer and dietary vitamin D or between breast cancer and vitamin D supplements, however, showed a protective association only in case-control studies.87. This discrepancy might be the result of several factors: Firstly, it is well known that prospective studies are less prone to be affected by both information and reverse-causation bias. Secondly, several authors highlight the season when the vitamin D measurement was made as a potential limitation of case-control studies. Eliassen et al.33 in a nested case-control study found an inverse association between serum 25(OH) D levels and breast cancer limited only to summer measures. It can be assumed that people with low vitamin D levels in summer would also have low levels year-round; therefore, vitamin D levels in summer would be more adequate for analyzing vitamin D – breast cancer relationship than vitamin D levels in any other moment of the year.In relation to the influence of vitamin D on breast cancer development prospective (cohort and nested case-control) and case control studies tend to show discrepant results: case-control studies usually show a protective effect while prospective studies rarely find itneoplastic transformation89. The active vitamin D metabolite is able to block the mitogenic effects of IGF-I, leading to a decrease in proliferation and an increase in apoptosis90. As there is a physiological decline of the IGF with aging91, the interaction between IGF pathways and vitamin D is likely to be stronger for premenopausal than for postmenopausal women, leading to greater risk reduction in premenopausal breast cancer92. Finally, high levels of vitamin D may reduce progesterone and estradiol, providing a potential mechanism for reducing breast cancer risk in young women93.When stratifying by menopausal status, our meta-analysis shows a consistent protective effect of 25(OH) D in both case-control and nested case-control studies, but only in premenopausal women. There are different explanations for the influence of menopausal status in the relationship between vitamin D and breast cancer. One of them may be related to the joint relationship between vitamin D and insulin-like growth factors (IGFs). IGF-I is a mitogenic and antiapoptotic peptide that can stimulate the proliferation of breast epithelial cells, increasing the risk of et al.13 D) found a slightly stronger inverse association among premenopausal than among postmenopausal women but without significant differences, whereas in the meta-analysis of Bauer et al.8 (nine studies included) the inverse association was only observed in postmenopausal women. In our meta-analysis, new prospective studies94 not included in previous reviews, were added and this fact may explain the differences in the results.Previous meta-analyses of prospective studies showed contradictory results. Kim 95. It has been demonstrated that treating breast cancer cells ER+ with 1,25(OH)D3 induces a cell cycle shutdown in GO/G196. On the other hand, two-thirds of triple negative tumors express VDR97 and it has been demonstrated that VDR expression is inversely associated with more aggressive breast cancer98. In consonance with previous epidemiological studies45, our study does not reach significant differences when the analysis was performed separately in ER+ or ER− subgroups. However, other studies found a decreased risk of ER− breast cancer regarding the serum levels of 25 (OH) D60.Concerning hormonal receptors (ER+/ER−), the relationship with breast cancer remains controversial. On the one hand, a decreased risk in ER+ would be expected, since it seems that sensitivity to 1,25(OH)2D is generally reported as being higher in breast cancer cells that express the estrogen receptor than in those that do not9, while Janowsky et al.39 found an inverse association. Several authors consider that 1,25(OH)2D is not a good indicator of vitamin D status: First, 1,25(OH)2D’s half-life is only 4–6 h, whereas 25(OH)D’s half-life is 3 weeks; second, 1,25(OH)2D is influenced by many factors10, for instance, it can be elevated in patients with vitamin D deficiency as a result of hyperparathyroidism99; finally, as 1,25(OH)2D is metabolized by 1-α -hydroxylase in breast tissue, plasma levels may not adequately represent breast tissue levels100.No relationship is found between the level of circulating 1,25(OH)2D and breast cancer. This result is consistent with previous studiesWe do not find a relationship between vitamin D intake and breast cancer in the overall analysis. In contrast, when stratifying by menopausal status, a protective effect is observed in case-control studies in both premenopausal and postmenopausal women, whereas this association is not present in cohort studies. On the other hand, when analyzing the influence of vitamin D supplements on breast cancer risk, we find a borderline protective effect.53 found a protective association. In a previous meta-analysis13, this association was not significant for either vitamin D intake or supplements.In the relationship between vitamin D intake (dietary and/or supplements) and breast cancer, most observational studies showed non-significant differences; only two articlesA probable explanation for the lack of association observed in the analysis of dietary intake or supplements compared to the 25(OH)D levels may be that the main source of vitamin D is sunlight rather than food or supplements.12 reported that high vitamin D intake is associated with lower breast cancer risk in regions with high ultraviolet solar radiance. These results suggested that the total amount of vitamin D needed to reach a protective effect on breast cancer is too high to be achieved in regions with low ultraviolet radiance. Under these circumstances, as the vitamin D intake has to be higher than the usually recommended, it could eventually lead to side effects such as hypercalcemia, constipation or muscle weakness.In addition, the French E3N Cohort Study75. Thirdly, case-control studies are more prone to methodological issues, such as recall and selection biases, which limits the strength and quality of evidence. However, about half of the case-control studies included in our meta-analysis are nested in cohort studies, which minimizes the possibility of introducing biases. Finally, breast cancer is a heterogeneous disease and it is possible that vitamin D only affects certain breast cancer subtypes. However, this aspect has been scarcely studied in primary articles, so we have not been able to analyze it in the present meta-analysis.Our study has some limitations; firstly each article uses different cutoff points according to serum levels of vitamin D. To analyze it we restricted our analysis to the comparison between the highest vs. lowest category of exposure. This analysis strategy does not allow for a dose-response analysis. Moreover, we carried out a sensitivity analysis excluding one study at a time, showing that no single study substantially affected the pooled RR/OR. Secondly, there is huge variability in the literature on the type of vitamin D studied, which makes it difficult to perform the analysis. In addition, levels of vitamin D depend on the season, so it would be advisable to take all samples at the same time, or at least refer to when they were collected99 or vitamin D intake12. This strategy allows us to obtain a more detailed analysis of the relationship between vitamin D and breast cancer.Despite these limitations, our study also has several strengths; first, we have gathered all the observational studies published in the last twenty years. In addition, we have focused the analysis on different types of vitamin D exposure  D and 1,25(OH)2D) whereas other meta-analyses are only focused on 25(OH)D levelsIn conclusion, our meta-analysis supports the hypothesis that high serum levels of 25(OH) vitamin D has a protective effect on breast cancer risk in premenopausal women; we cannot draw the same conclusion regarding vitamin D intake or supplements of vitamin D since the number of studies are still limited and publication biases cannot be excluded.Supplementary material


Studies on the relationship between vegetarian diet and breast cancer in Asian populations are limited. This study aimed to investigate the relationship between vegetarian diet, dietary patterns, and breast cancer in Taiwanese women.This case-control study compared the dietary patterns of 233 breast cancer patients and 236 age-matched controls. A questionnaire about vegetarian diets and 28 frequently-consumed food items was administered to these 469 patients in the surgical department of Taipei Tzu Chi Hospital. Serum biochemical status was also examined.P < 0.05). Two food items (shellfish and seafood) were highly correlated (correlation coefficient = 0.77), so shellfish was excluded to avoid multicollinearity. A factor analysis of 27 food items produced five dietary patterns: meat, processed meat, fruit/vegetable/soybean, dessert/sugar, and fermented food. Multivariate logistic regression showed that meat/fat and processed meat dietary patterns were associated with breast cancer risk (odds ratio (OR): 2.22, 95% CI 1.67–2.94, P < 0.001; OR: 1.49, 95% CI 1.09–2.04, P = 0.013, respectively). Vegetarian diet, high isoflavone intake, and high albumin levels were inversely associated with breast cancer risk (P < 0.05). Vegetarians had a higher daily soy isoflavone intake than non-vegetarians .There were no significant differences between the two groups for age, education, family history, oral contraceptive usage, or regular exercise. However, the cancer group presented with both a higher body mass index and an older age of primiparity  contains supplementary material, which is available to authorized users. In Taiwan, breast cancer is the most common type of cancer in females and the fourth leading cause of female cancer deaths since 2003 . The incThe role that nutrients and food components play in the development of breast cancer has been comprehensively examined. Caffeinated coffee intake has been shown to likely be associated with a lower risk of postmenopausal breast cancer . High diThis case-control study was conducted at the Taipei Tzu Chi Hospital from April, 2010 to December, 2013. The admission of patients into the study followed a protocol similar to that used in the preliminary report of this study . The resInclusion criterion for the cancer group was female patients (≤85 years of age) who were diagnosed with primary breast cancer. Research team members contacted potential participants to introduce the study and obtained their signed informed consent. Among 250 eligible women, 17 patients (6.8%) declined, and 233 patients (93.2%) were interviewed. Recruitment of the control group was based on individuals who underwent health examinations for breast lesions during the same time period at the same hospital. Inclusion criteria for the control group consisted of the following: no history of breast cancer, residence in the same neighborhood, and age within a 5-year margin of the cancer group. Individuals who were on controlled diets for diabetes or other diseases were excluded. As performed in the cancer group, research team members contacted potential control group candidates and scheduled interviews for those who agreed to participate. Among women eligible to participate in the control group (500 contacted), 274 declined to answer questionnaires (mostly due to time limitations or factors unrelated to dietary pattern), and 236 (47.2%) agreed and completed questionnaires. Questionnaires were administered at the study hospital.The first section of the questionnaire contained demographic information including body mass index (BMI). The second section contained a Food-Frequency Questionnaire (FFQ) with 28 separate food items. Daily dietary intake of each participant was assessed by the FFQ, which was developed and used for the third Taiwan National Nutrition and Health Survey conducted from 1993 and 1996 , 27. ForMean values (with standard deviations) and frequencies were reported for the demographic and dietary characteristics of each group. Independent-sample t tests were used to compare continuous variables. Chi-square tests were used to compare categorical variables. Bivariate correlation analyses across food items were conducted to avoid multicollinearity. We used factor analysis according to monthly FFQ scores to derive dietary patterns for subsequent inferential statistics. Researchers frequently use factor analysis to derive dietary patterns –32. TheoP < 0.05 was considered statistically significant.Relative risks computed from logistic regression were reported as odds ratios (ORs) and 95% confidence intervals (CIs). All analyses were conducted using SPSS . Statistical tests were two-sided. In all tests, a P < 0.001), higher percentage of primiparity >30 years (P = 0.011), and lower exercise frequency (P = 0.009) than the control group. The cancer group also had a lower percentage of women following vegetarian diets  and lower daily soy isoflavone intake  than the control group. Additionally, serological tests showed that participants in the cancer group had lower serum albumin levels . There was no difference in serum estradiol levels between the breast cancer and control groups (P = 0.205). Monthly food frequency was the highest for tea  between seafood  and shellfish . Therefore, shellfish was excluded from subsequent analyses, which computerized only twenty-seven food items. Figure P < 0.001), BMI > 23 kg/m2 , primiparity >30 years , serum albumin > 4 g/dl , and daily soy isoflavone intake > 22 mg . Serum triglyceride and estradiol levels were not associated with breast cancer risk (P > 0.05). Vegetarian and non-vegetarian participants had similar BMI . Vegetarian participants consumed higher daily quantities of soy isoflavones than non-vegetarian participants  derived from factor analysis were significantly associated with breast cancer risk using both univariate and multivariate analyses. Other dietary patterns  were not associated with breast cancer risk. Our results are partially consistent with a prior study in Taiwanese women, which showed a harmful effect of dietary fat on the risk of breast cancer. However, that same study did not observe the protective effect of soy dietary patterns found in this study . In studHowever, there are some disagreements between our results and those from other studies. Zhang et al. reported a diet characterized by the high consumption of vegetables, fruit, soy, milk, poultry and fish was associated with a lower risk of breast cancer in Chinese women, while refined grains, red meat and pickled foods were associated with higher risk of breast cancer . A studyThe term “vegetarian diet” refers to a special dietary pattern that precludes meat-fat and processed meat dietary patterns. Several recent studies have reported that plant-based dietary patterns are associated with a reduced breast cancer risk , 39. Is The reasons explaining the reduction in breast cancer risk for vegetarians remain unclear. Reasonable explanations might be that vegetarian and vegan diets increase the consumption of beneficial plant foods and plant constituents, eliminate the intake of red and processed meat, and aid in achieving and maintaining an ideal weight . BecauseIsoflavones, a class of phytoestrogens, are found predominantly in soybeans and soy products . The majResearchers have investigated the association between dietary patterns and breast cancer using various definitions. In Chinese women, Cui et al. observed that a meat/sweet pattern was associated with breast cancer risk, while a vegetable/soy pattern was not . Wu et aThere are several limitations of this study. First, the study did not use a longitudinal study design. Therefore, the results cannot be used to infer a causal relationship between diet and breast cancer risk. Second, the number of participants is relatively small, which might limit our ability to differentiate the risks of women who were life-long vegetarians from those who became vegetarian only in later life. The extrapolation of results to the general population should be performed cautiously. Future studies may require more participants and a longer follow-up period. Finally, an inherent limitation originates from the factor analysis, as it captures the correlated profiles of variables that may or may not have any association with the disease. The dietary patterns derived from factor analysis reflect existing eating patterns of participants and may not necessarily be those patterns that are optimal for cancer prevention.Our findings demonstrate a significant positive correlation between breast cancer incidence and the meat and processed meat dietary patterns. They also demonstrate that vegetarian diet is associated with a reduced incidence of breast cancer. Higher BMI and older age of primiparity are also risk factors of breast cancer. In contrast, higher isoflavone intake and serum albumin levels are associated with a lower incidence of breast cancer. Vegetarian diets can increase isoflavone intake, which may partially contribute to the protective role of vegetarian diet against developing breast cancer. Large study samples are required to validate this association.