Purpose: This paper describes women’s opinions of the attributes of the ideal body image program to inform the design, development, and implementation of future programs for those diagnosed with breast cancer.Methods: Deductive-inductive content analysis of semi-structured interviews with 26 women diagnosed with breast cancer  was performed.Findings: Participants’ opinions regarding the ideal body image program are summarized into five themes, mapping the where , when (across the cancer continuum or at specific points), how , what , and who .Conclusion: This study provides useful data on what women believe are the attributes of the ideal body image program, which can contribute to efforts aimed at developing and delivering body image programs for women diagnosed with breast cancer that prioritize their needs and preferences. Worldwide, there are more than 2.1 million women diagnosed with breast cancer each year, making it the most common type of cancer diagnosed in women . Conventds = 0.15–1.43, g = 0.50, 95% CI , Two recent systematic reviews of studies with women diagnosed with breast cancer showed that body image programs comprised of psychotherapy, psychoeducation, or physical activity can have positive effects on body image , resulting in a variety of modes, formats, and content, and potentially programs that are not likely to match women’s needs and preferences . As suchwhat, when, where, who, and how of the ideal body image program. The aim of the present qualitative study was therefore to explore women’s opinions of the attributes of the ideal body image program. This manuscript describes their opinions and presents implications for future program design, development, and delivery.Public involvement in the development, design, and delivery of programs is highly promoted  and is vThis study used a qualitative interview design and adopted a constructivist paradigm approach wherein knowledge is considered introspective and co-developed based on individuals’ unique perspectives . Ethics Twenty-seven women provided consent prior to completing the interview; one was removed from this study because she did not answer questions pertaining to the current aim due to time constraints. Data from the remaining 26 women  were analyzed; n = 10), by telephone (n = 11), or via videoconferencing (n = 5). The latter two means were done to reduce barriers for participation  and permit interviews with women from a broader geographic area  reflected on their positions throughout the research process. They acknowledge that none had been diagnosed with breast cancer at the time of this study. As such, they were keenly aware of the differences between themselves and the interviewed women, especially in terms of disease history and age. Nonetheless, they had empathy for the interviewed women and the challenges they had faced as a result of having been diagnosed with breast cancer. Moreover, the interviewer felt compelled at times to reassure women and struggled to curb her urge to offer support in a matter that would take her beyond her role. To avoid this and protect against biases, she spent time debriefing with the first author after interviews.where, when, how, what, and who of the ideal body image program. Themes are summarized in Participants clearly had an overwhelming desire to access a body image program. Yet, the data collected in this study suggest they had a range of opinions regarding the attributes of the ideal body image program. Participants’ opinions were summarized into five themes reflecting their preferences for the there’s never going to be a convenient place for everyone at different times.” Of the 26 participants, two expressed interest in a body image program that could be undertaken within the community  for convenience. Four expressed interest in a body image program that could be delivered online to obtain easily accessible information at convenient times at their preferred location. Three mentioned that a body image program could be delivered in-hospital because it “makes more sense” and because they had “become accustomed to medical settings.” However, they did not necessarily feel it was the most appropriate setting. Moreover, five participants voiced concerns about having to go to the hospital for a body image program because they thought of the hospital as “such a sterile place that nobody wants to go to” and added that parking would likely deter participation.This theme refers to participants’ preferred location for a body image program. Based on participants’ collective accounts, body image programs should be delivered across different locations because “several sections. There would be a newly diagnosed section, then there’d be the treatment time, then there’d be the post-treatment time, and then there’d be the rest of your life.” Only one participant was of the opinion that a program offered pre-diagnosis would be of interest because “if you’re coming from a place of negative body image and then you have breast cancer, I mean, you would need a lot of help.” However, similar to two others, she also believed post-surgery would be good timing. Others described a preference for a program at diagnosis or immediately after (n = 4), during treatment (n = 3), or post-treatment (n = 6). Further supporting post-treatment timing, two participants expressed a negative opinion about having a body image program during treatment because they believed women would have other priorities . Thus, overall, participants were of the opinion that women should be able to undertake a body image program when they are comfortable doing so.This theme describes participants’ preferences for the timing of a body image program. Participants had a variety of opinions, highlighting that differential timing may be needed for different women. Only three participants were interested in accessing a body image program across the cancer continuum, suggesting there should be “there’s safety in small groups but sometimes there’s anonymity in big groups. So you might be doing the same things, but have two different kinds of workshops and you give them the option of what ones they would rather [attend].” Another option mentioned was to have a peer-led program (n = 7) or support group for women that facilitates discussion and encourages friendly relationships (n = 8). This was driven by participants’ belief that camaraderie would be built with other women diagnosed with breast cancer and that peer social support would be facilitated. Others, however, were more interested in receiving individual guidance or support such as through one-on-one counseling (n = 1) or by receiving a compilation of resources women can access on their own (n = 2). Whilst divergent opinions were shared by participants, they generally agreed that, regardless of the format, a one-size fits all approach would not work as “you’d probably have to cover quite a wide range because everybody has different problems depending on your age, your situation, and things are different.”This theme refers to the format participants were interested in for a body image program. Ten participants were interested in attending presentations or workshops focused on body image, and an additional five suggested special events. Whilst different terms were used by participants, they described presentations, workshops, and special events as one-time occurrences wherein information pertaining to body image  could be shared with attendees to facilitate knowledge. Yet, it is unclear what the size of the audience should be as “n = 3), encouraging physical activity for improved health and wellbeing (n = 6), giving sleep advice (n = 3), and discussing body size, shape, and weight management (n = 2). Of note, many of these participants expressed interest in multiple aspects of self-care. Within the realm of counseling and education, two key topics prevailed. Twenty-five participants expressed a desire for a program aimed at accepting and embracing their new normal, either by promoting self-compassion (n = 12), focusing on inner strength more so than appearance (n = 5), building resistance to body-based stereotypes/prejudices (n = 4), supporting spiritual growth – which can exist within or independent of religion – to feel more connected with life (n = 3), and building confidence (n = 1). Another 13 mentioned counseling and education related to addressing the side-effects of cancer treatment, including discussing typical physical changes (n = 6), learning to deal with stress and mental health issues (n = 2), and discussing sexuality/sexual health (n = 5). Raising awareness of how body image affects women (n = 3), especially with partners, was also mentioned and thus necessary to “focus on the people around, the support system, and yes, educate men. […] Like have him attend a meeting, educate him, this is how your wife is feeling.” In addition to discussing sexuality/sexual health, three participants mentioned wanting help addressing sexuality/sexual health concerns to help improve their body image. Finally, four participants expressed interest in receiving make-up and clothing tips for coping with body image changes, though two others held negative opinions toward make-up and clothing-oriented topics.This theme describes what topics participants would like to see covered in a body image program. Participants identified 13 topics, none of which were unanimously recommended, highlighting the personal nature of body image and the potential need for tailoring. Despite the diversity in participants’ preferred topics, these could be organized into five areas: self-care, counseling and education for oneself, education for others, support for addressing sexuality/sexual health concerns, and concealing treatment-related changes. Of the 26 participants, 14 mentioned an interest in self-care, including promoting healthy eating (n = 16) preferred that women previously treated for breast cancer be involved in delivering the program because “you need somebody that’s been in the trenches.” Similarly, four said that a promising option could be to have women previously treated for breast cancer partner with a health professional to deliver the program. The rationale provided was that “some people really like having the authority of having a medical degree behind the person speaking. Some people really like that and have difficulty if they don’t have that. Other people prefer a more peer type interaction with the person presenting. So those are dichotomies. How are you going to do that unless you have two people speaking, which obviously might work.” Generally, participants who desired that women diagnosed with breast cancer be involved in delivering the program were driven by the belief that having a shared experience was an essential component of a body image program. Eleven participants expressed an interest in having health professionals such as doctors (n = 6), nurses (n = 5), dieticians/nutritionists (n = 2), psychologists (n = 3), social workers (n = 4), fitness professional or physiotherapists (n = 3), or sex therapists (n = 2) deliver the program. Only one participant mentioned having a make-up artist and/or clothing stylist deliver the program. Given this diversity, delivering a body image program may require a multi-disciplinary team approach, whereby women previously treated for breast cancer partner with health professionals to design and deliver programs to meet an assortment of needs and preferences.This theme refers to the person(s) who participants felt should deliver a body image program to women diagnosed with breast cancer. Because the person(s) delivering the program is central to what and how the content is delivered, most participants , participants expressed a desire for group discussions and interactive activities that facilitate peer engagement, print or digital materials , and one-on-one counseling. In particular, a large proportion of participants expressed a desire to interact with other women diagnosed with breast cancer in order to have opportunities to ask questions and share experiences. Notably, interaction with others has been highlighted as an important component of creating an optimal experience in psychological and behavioral interventions for adults diagnosed with cancer . As reseThe limitations of this study should be acknowledged. It only included English-speaking women living in a high-income country. Relatedly, the high education and lack of racial and ethnic diversity in the sample is another limitation. It is possible that women living in lower income countries, women with lower education, or women from different racial or ethnic groups may have different opinions and preferences. Also, participants were post-treatment. By interviewing women post-treatment, it is possible that certain opinions were influenced by reduced recall. Prospective, longitudinal studies are warranted to understand women’s opinions at different points in time. Similarly, participants had completed treatment within the past 5 years. Accordingly, it will be necessary to determine if the opinions and preferences expressed by participants in this study are echoed by women further away from treatment completion. Additionally, data were analyzed to aid in the development, design, and delivery of future body image programs for women diagnosed with breast cancer that meet their needs and align with their interests and preferences. It remains to be seen if such programs are just as effective as others tested in the literature. However, the strengths of this study were its sample size, inclusion of women with a range of ages and cancer experiences to support a diversity of perspectives, and analysis conducted by two researchers experienced in qualitative methodology.what , when (across the cancer continuum or at specific points), where , who , and how  that should be considered when developing, designing, and delivering body image programs targeting women diagnosed with breast cancer. It is hoped this information will help in the creation of more meaningful, relevant, useful, and usable programs for this growing segment of the population.The objective of this study was to investigate women’s perspectives of the ideal body image program for women diagnosed with breast cancer. Participants’ accounts suggest that a body image program may be an effective way to help them feel better about their bodies in a world that constantly makes them focus on their perceived flaws, personal inadequacies, and failures. Although one specific formula for how to design, develop, and deliver a body image program to women diagnosed with breast cancer may not exist, the findings of this study lay the foundation for the development, design, and delivery of a more diverse and tailored approach to such programs. Specifically, findings provide a starting point for Jennifer.Brunet@uottawa.ca.The datasets presented in this article are not readily available because the data cannot be shared as participants were assured that their data would be kept private and confidential to the extent permitted by law and that only the research team would have access to the data. Requests to access the datasets should be directed to JB, This study involving human participants was reviewed and approved by the Ottawa Health Science Network Research and the University of Ottawa Ethics Boards. Participants provided their written informed consent to participate in this study.JB conceptualized and designed the study, oversaw the acquisition of the data, contributed to the interpretation of the data, and drafted the manuscript. JP led the analysis and interpretation of the data, helped draft sections of the manuscript, and critically revised the manuscript. CH assisted with the conceptualization of the study, participant recruitment, and revised the manuscript. All authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Several statistical models exist to predict a person’s risk of breast cancer. Risk assessment models can guide cancer screening approaches by identifying individuals who would benefit from additional screening. In this study, we compared the performance of four models in predicting the 5-year risk of breast cancer in a cohort of women aged 40–84 years who underwent screening mammography at three large health systems. Models showed comparable discrimination (ability to distinguish between cases and non-cases) and calibration (ability to accurately predict risk) overall, with no difference by race. Model discrimination was poorer for some cancer subtypes, and better for women with high BMI. The combined BRCAPRO+BCRAT model had improved calibration and discrimination among women with a family history of breast cancer. Our results can inform risk-based screening approaches by identifying women at a high risk of breast cancer.(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography. BRCA1/2 mutation and risk of breast cancer. Recently, the BRCAPRO model was combined with the BCRAT model to create the BRCAPRO+BCRAT model [While breast cancer mortality has fallen over the past decade in the U.S., it remains the second leading cause of cancer death among women, with 43,600 breast cancer deaths projected in 2021 ,6. The BAT model  that incOur prior work compared the BCRAT, BCSC, and BRCAPRO models in a cohort of women undergoing mammography screening at a single institution . We founThe purpose of the current study is to compare the performance of breast cancer risk models for use in the setting of mammography screening, and specifically to evaluate model performance among subgroups defined by race/ethnicity, by molecular subtypes, by family history, and by obesity. Additionally, we performed validation of the recently developed BRCAPRO+BCRAT model , utiliziWe assembled three cohorts of women presenting for screening mammography at Massachusetts General Hospital (MGH), Newton-Wellesley Hospital (NWH), and the University of Pennsylvania Health System (UPenn) between 2006–2015, 2006–2015, and 2011–2015, respectively. All mammograms included were digital or digital breast tomosynthesis... The following patient-reported risk factors were collected via questionnaire at the time of the mammogram: age, race/ethnicity, age at menarche, age at first birth, body mass index (BMI), history of breast biopsy, history of atypical hyperplasia or benign breast findings, and family history of breast cancer. Breast density measurements were pulled from radiology reports in electronic medical records (EMR) and were classified based on the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). EMR was also used to supplement missing survey information on the history of breast biopsy, atypical hyperplasia, BMI, and history of prior breast cancer. At MGH, the questionnaire asked whether women had ever had “benign tissue removed from the breast” which was considered as evidence of atypical hyperplasia or benign breast findings. Additionally, at MGH and NWH, we additionally included data from pathology reports on diagnoses of atypical hyperplasia, lobular carcinoma in situ (LCIS), or lobular neoplasia. At UPenn, women were asked whether they had previously been diagnosed with atypical hyperplasia. Additional detail about benign breast conditions was not available for UPenn. Missing BMI specifically was supplemented with the closest measurement from EMR within 1 year prior to or 6 months after the mammogram. The presence of BRCA1 and BRCA2 pathogenic genetic mutations was also collected from linkage with genetic counseling records. We included the first mammogram visit over the time period for each woman, using covariates collected at the time of the first mammogram visit in the cohort for analysis .Patients with breast cancer prior to screening were excluded, as were patients diagnosed with cancer within 6 months of screening . For MGHBreast cancer cases were determined through to 31 December 2017, using a combination of hospital cancer registries and state health department cancer registries. At MGH and NWH, breast cancer diagnosis information was obtained from the Massachusetts Cancer Registry, while at UPenn, breast cancer diagnosis information was obtained from Pennsylvania, New Jersey, and Delaware state cancer registries. Invasive breast cancers were categorized into molecular subtypes based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Borderline ER and PR results were considered positive, while borderline HER2 results were considered unknown . Since thttps://dceg.cancer.gov/tools/risk-assessment/bcra, accessed on 23 November 2021); the BayesMendel R package (v2.1-7) for BRCAPRO and BRCAPRO+BCRAT ; and the BCSC SAS program (v2.0) for BCSC .All patients were followed from the date of first screening mammogram visit until breast cancer diagnosis, death, or administrative censoring on 31 December 2017. We estimated the 5-year absolute risk of breast cancer using the BCRAT, BCSC, BRCAPRO, and BRCAPRO+BCRAT models and compared the results with observed breast cancer outcomes. Death was treated as a competing risk. We used the BCRA R package (v2.1) for BCRAT  and that those with a missing number of biopsy examinations had no such examinations . Regarding missing menopause status, any person over 55 or anyone who had stopped menstruating was categorized as postmenopausal . Anyone 2 or ≥30 kg/m2. We performed sensitivity analyses evaluating model performance by site and comparing performance for Black and White women only at UPenn since the majority of Black women were from that site.We validated and compared the performance of BCRAT, BCSC, BRCAPRO, and BRCAPRO+BCRAT with respect to the absolute risk of invasive breast cancer. We also compared these models in subgroups defined by race/ethnicity, invasive molecular subtypes , a number of affected first- or second-degree relatives, age <50 or ≥50 years, and BMI <30 kg/mCalibration of risk prediction models was evaluated by the observed to the expected ratio (O/E), where the numerator is the observed count of invasive breast cancer cases that arose within 5 years of the initial mammogram, and the denominator is the expected number of cases predicted by the model, obtained by the sum of the predicted 5-year absolute risk estimates. An O/E ratio of 1 indicates perfect calibration, an O/E ratio >1 indicates that the model under-predicts the true number of cases, and an O/E ratio <1 indicates that the model over-predicts the true number of cases. Calibration curves were plotted as the observed proportion of breast cancer cases versus the predicted proportion of breast cancer cases in each decile of predicted absolute risk. Discrimination was assessed by area under the receiver operating characteristic curve (AUC). AUC measures the probability that a prediction model provides higher absolute risk scores for cases than for non-cases. An AUC of 0.5 indicates that the model performs no better than chance, while an AUC of 1 indicates perfect discrimination. The clinically recognized threshold for elevated 5-year risk of breast cancer for chemoprevention is 1.67% ,15. Thisp-values for the difference in AUC between pairwise comparisons of models and strata using the Chi-square distribution with 1 degree of freedom. All statistical tests were two-sided using an alpha of 0.05. In addition, we adjusted for multiple comparisons  by the Bonferroni method, with corrected p-values less than 0.00147 considered statistically significant (p = 0.05/34 comparisons).To obtain 95% confidence intervals (CI) for the performance metrics we generated bootstrap samples and calculated metrics for each model in each bootstrapped sample . To testwww.R-project.org, accessed on 23 November 2021).We also compared the proportion of patients categorized as high vs. low absolute risk, comparing two models at a time and using the cutoff of 1.67% 5 years risk. All analyses were performed using R statistical software (p = 0.010), BCRAT and BRCAPRO (p = 0.007), BCRAT and BRCAPRO+BCRAT (p = 0.040), BCSC and BRCAPRO (p < 0.001), and BRCAPRO and BRCAPRO+BCRAT (p < 0.001). The differences in AUC remained statistically significant after Bonferroni correction for the comparisons of BRCAPRO and BCSC (p < 1 × 10−8) and BRCAPRO and BRCAPRO+BCRAT. O/E ratios ranged from 1.036 for BCRAT to 1.185 for the BCSC model, and the O/E for the BCSC model was further away from 1. The true positive rate ranged from 30.7% for the BCSC model to 37.8% for the BCRAT model, whereas the false positive rate ranged from 18.4% for BCSC to 24.3% for BRCAPRO. The proportion of patients identified as having high 5-year risk differed across models, with 18.6% of patients identified as high risk based on the BCSC model compared with 24.5% based on the BRCAPRO model. Comparable model performance was observed across the three institutions  and BRCAPRO (p = 0.049) had significantly higher AUC among Black compared with White women, though the O/E ratio for both Black (O/E = 1.40) and White (O/E = 1.41) women at UPenn indicated under-prediction of cancer cases for BCSC , but differences did not reach statistical significance for ER/PR+HER2− compared with ER/PR/HER2− . After Bonferroni correction, the difference in AUC for BRCAPRO between ER/PR+HER2− and HER2+ disease remained statistically significant (p < 1 × 10−5).p = 0.019) and BRCAPRO+BCRAT (p = 0.016) models. These differences were no longer statistically significant after the Bonferroni correction. O/E ratios for all models among patients with a family history were further from 1 than those without a family history, and confidence intervals did not overlap, suggesting cases were underpredicted for patients with a family history . Both the BCSC model and the BRCAPRO model had significantly higher AUC among obese women compared with non-obese women after adjusting for multiple comparisons . However, the under-prediction was more severe for BCSC and BRCAPRO among women with high BMI compared with lower BMI. history . For womOur study is one of the largest to examine the validity of multiple breast cancer risk prediction models simultaneously in a large and diverse sample of women undergoing mammography screening, allowing comparisons of model performance by race/ethnicity, tumor molecular subtypes, family history, and obesity. We found that model performance was comparable across the four models examined, with moderate discriminatory accuracy, and generally good calibration, consistent with previous estimates ,28,29,30The measures of calibration and discrimination of the models observed in our study are generally consistent with existing literature ,28,29,30p-values for differences in AUCs were not significant, and confidence intervals for O/E ratios for Black or African American and White women were overlapping. Despite our large population, there were only 209 breast cancers diagnosed among Black or African American women, which may limit our power to detect differences. The original BCRAT model was found to underestimate risk among Black or African American women and was therefore updated to better predict breast cancer risk for Black or African American women based on the results of the Women’s Contraceptive and Reproductive Experiences (CARE) study [Our study found no significant difference in model performance between Black or African American and White women. To our knowledge, ours is the first study to compare the performance of multiple breast cancer risk prediction models simultaneously for White and Black women. AUCs tended to be higher for Black or African American than White women, and calibration tended to be poorer for Black or African American than White women, though E) study . HoweverE) study . ValidatE) study . The AUCE) study . Future Our results further suggest that risk models do not identify the risk of triple-negative or HER2+ tumors as well as hormone receptor-positive HER2− tumors. Our results are consistent with an analysis of the Women’s Health Initiative, which showed that the BCRAT model predicted ER+ breast cancers, but not ER− breast cancers  and withBRCA1/2 who may benefit from genetic testing.BRCAPRO and BRCAPRO+BCRAT use more extensive family history data than the BCSC and BCRAT models. In this screening cohort, approximately 20% of patients had any family history of breast cancer, including first or second-degree relatives. We found that BRCAPRO+BCRAT had significantly better AUC than BRCAPRO and calibration was very similar for both models overall, and among the subset of women with family history. This highlights the need to balance between a potentially small increased accuracy of risk assessment including extended family history, and the additional time and computational burden of collecting and analyzing extensive family history data. It is important to note that detailed family history assessment has the added benefit of identifying patients who may be at high risk for high and moderate penetrance mutations such as We found significantly higher AUC across models among obese women compared with non-obese women, though calibration was poorer for obese women, particularly for the BCSC model. While none of the existing models include BMI as a predictor, the BCSC model does incorporate breast density, which is highly correlated with BMI, with women with higher BMI having lower breast density on average. Therefore, the BCSC model likely downgrades risk for many obese women given their lower breast density, leading to under-prediction of risk. Breast density as coded by the radiologist has been shown to have poor reproducibility . Novel mTo our knowledge, this is one of the largest studies to compare multiple breast cancer risk models among women undergoing mammography screening in the U.S. The large and diverse cohort enabled comparisons of model performance by race/ethnicity, molecular subtypes, family history, age, and BMI. Additionally, we were able to evaluate a novel combination of the BRCAPRO and BCRAT model, BRCAPRO+BCRAT, that may be useful for patients with an extensive family history of breast and ovarian cancers. Our findings provide directions for future research by identifying subsets of the population for which existing models may have suboptimal performance.BRCA1/2 carriers because the BCRAT and BCSC risk models are specifically designed to predict risk in non-carriers. There were fewer BRCA1/2 carriers at NWH than MGH and UPenn, which is partly expected, as NWH is a community hospital, whereas MGH and UPenn have large cancer genetics clinics. It is also possible that BRCA1/2 mutation status was less well annotated in health records at NWH, and we may have included some BRCA1/2 mutation carriers in the analysis, which may bias results in unknown ways. However, given the small prevalence of BRCA1/2 mutations in the general population, we expect the magnitude of this bias to be small. Finally, we lacked genetic data to incorporate polygenic risk scores into risk models, which has been shown to improve predictive accuracy. A recent study showed that adding the 313 SNP polygenic risk score to classical breast cancer risk factors improved the AUC from 56% to 64% among women younger than 50 and from 57% to 64% among women aged 50 and older [p-values and found several associations that remained statistically significant after Bonferroni correction.Several limitations should be noted when interpreting the findings. We were unable to include the IBIS/Tyrer-Cuzick model because we lacked sufficient data on the use of menopausal hormone therapy for input to this model. Due to the small number of Hispanic and Asian/Pacific Islander patients, there were too few cancer outcomes to make meaningful inferences on the performance of the risk models in these racial/ethnic groups. There were 209 breast cancers among Black or African American women, which is also a relatively small sample. Most of the Black or African American patients were screened at one of the three sites, and therefore results for Black or African American patients may be confounded by site-specific measurement biases. In addition, the data on prior biopsy from MGH was incomplete. However, we believe the data on atypical hyperplasia to be reasonably accurate since it incorporated both self-reported data and information extracted from biopsy reports. However, we lacked data on other benign breast conditions, such as non-proliferative benign breast diseases and proliferative benign breast diseases without atypia. While the BCRAT uses atypical hyperplasia, the BCSC model uses benign breast disease, and therefore we may be underestimating risk among patients since we do not have full data on benign breast diseases. This highlights the broader concern, that there is variation in data quality across risk factors and across sites. However, this is the reality when data collected for clinical purposes is utilized for risk assessment. The inclusion of three cohorts with slightly different risk collection instruments may help in reducing the effect of misclassification of risk factors at any one site on the results. We treated patients diagnosed with DCIS as non-cases since the risk models considered focus on the risk of invasive breast cancer. However, since risk factors for DCIS and invasive cancer share common risk factors, this may have led to the poorer observed performance of our models. Additionally, the purpose of this study was to evaluate risk models that would be feasible to integrate into clinical care in mammography to support decision-making in the general population. We excluded known nd older . PolygenIn summary, our research provides important data on the performance of breast cancer risk assessment models among women undergoing mammography screening in the U.S. and provides important data on how models perform in different subsets of the population, including Black or African American women and women with a family history of breast cancer, and how well the models predict breast cancer subtypes. We validate the BRCAPRO+BCRAT model which may be beneficial for use among women with a family history of breast and ovarian cancer. These findings are useful to develop risk assessment strategies but ultimately point to the need to further improve risk prediction by incorporating additional risk factors, such as quantitative measures of breast density and genetic markers.


The purpose of this study was to determine the feasibility and preliminary efficacy of a cardiac rehabilitation (CR) intervention in the breast cancer population. t-tests.  This single-arm feasibility study evaluated a 14-week CR intervention program in breast cancer survivors. Feasibility was defined as completion of at least 30/36 sessions of the program without serious adverse events (SAE) in 80% of patients. Secondary endpoints included the change in VO2 max, cardiovascular disease (CVD) risk factors, Duke Activity Secondary Index (DASI), Brief Fatigue Inventory (BFI), and QLQ-C30. All outcomes were reported as mean change and compared using paired p=0.6). The scores for DASI, BFI, and QLQ-C30 improved from baseline to posttreatment.  A total of 25 patients were enrolled in the study. 18 patients of the 25 enrolled (72%) completed the 14 weeks program without SAE. The overall adherence to the study protocol was 60%. Of the 18 participants who did not withdraw from the program, 15 (83%) adhered to the study protocol and completed 30 or more sessions. There was a nonsignificant improvement in VO2 max , without significant improvement in CVD risk factors. Implications for cancer patients are that early implementation of a CR program should be considered by practitioners as it improves QoL and exercise tolerance in breast cancer survivors. Cardiovascular disease (CVD) is the leading cause of death among women in the general population and among breast cancer survivors specifically , 2. IncrEvidence suggests that exercise also decreases long-term side effects from cancer treatments among breast cancer survivors and may provide additional physiological and psychological benefits , 8. A prA CR program is a unique model for providing an exercise intervention to breast cancer survivors. As per the statement from the American Heart Association for the need for effective and viable strategies to mitigate cardiovascular disease risk in cancer population, cardiac rehabilitation can play a role for potential solution. AHA statement provides an overview of the existing knowledge and rationale for the use of cardiac rehabilitation to provide structured exercise and ancillary services to cancer patients and survivors .The CR program is defined as “the provision of comprehensive long-term services involving medical evaluation, prescriptive exercise, cardiac risk factor modification, and education, counseling, and behavioral interventions” . CR is cAlthough CVD and breast cancer share many important risk factors, this current study is the first to study the implementation and feasibility of CR among breast cancer survivors posttreatment to improve cardiorespiratory fitness, reduce CVD risk, and improve quality of life (QoL). Earlier existing studies report increased CVD risk among breast cancer survivors  but haveThe purpose of this study was to assess the feasibility of a CR program in breast cancer survivors and to evaluate cardiorespiratory fitness, CVD risk factors, and patient reported outcome measures before and after the intervention.This was a single-arm prospective feasibility study of a 36-session (14-week) CR intervention in female breast cancer survivors. A sample size of 20 participants was initially planned to evaluate the feasibility of the CR program. An additional five participants were accrued to account for attrition.Participants' recruitment, eligibility screening, and consent occurred at the time of their appointment of their final breast cancer treatment and/or subsequent follow-up appointments. Women between the ages of 30 and 75 with stages 0–III breast cancer who were within 18 months of treatment including surgery, radiation, and/or chemotherapy, regardless of type and duration were eligible for the study. Patients could be receiving ongoing endocrine therapy or trastuzumab. Individuals with existing CVD, contraindications to exercise, or cardiac stress testing, metastatic breast cancer, other concurrent malignancies except skin cancer, active infection, psychiatric illness/social situation that would limit compliance with study requirements, or who were pregnant or breastfeeding were excluded from the study. An exercise prescription, approved per protocol by the CR program director and cardiac rehabilitation staff, was developed for each participant in the intervention group to guide the CR sessions.This study was approved by the Ohio State University Medical Center Cancer Institutional Review Board and followed IRB guidelines.Demographics, clinical data, and inclusion/exclusion criteria were obtained and recorded for all patients. Variables of interest were participants' age, self-reported race/ethnicity, cancer treatment details, and medical history. Breast cancer patients typically progress through treatment in the following order: surgery, chemotherapy, and radiation therapy, though not all patients progress through each step. We recruited patients from their treatment close-out appointment to participate in the study, regardless of type or duration of therapy received. The rationale for the proposed research is that cardiac rehabilitation programs have substantial extant infrastructure and may improve morbidity and mortality from both CVD and cancer among breast cancer patients posttreatment.The CR program consisted of one-hour sessions, three times per week for a maximum of 14 weeks and for a minimum total of 36 sessions. The program took place at an outpatient CR center at the Ohio State University Medical Center that was designated for cardiac patients. An exercise prescription, approved per protocol by the CR program director and cardiac rehabilitation staff, was developed for each participant to guide the CR sessions according to the cardiorespiratory fitness level of the participant. During each session, participants started the intervention at a workload of 60–85% of their VO2 max as determined in advance by a graded exercise stress test using the Bruce protocol . The goaA cardiac stress test was performed at baseline and week 14. The participants were instructed on the use of the Borg Perceived Exertion Scale for reporting their subjective level of exertion during the test . The strPatient questionnaires were administered at baseline, week 8, and week 14. Systolic and diastolic blood pressure (BP), a fasting lipid panel, fasting glucose, height and weight, and body mass index (BMI) of participants were checked at baseline and at the end of the 14-week intervention period.During the 14-week intervention period, the CR staff contacted participants by phone to remind them of their upcoming CR appointments for the week. The participants received a $20 per week stipend for the 14-week intervention period to help offset travel costs to and from the CR facility. Upon completion of the 14-week assessment, all participants received an additional $50 stipend.Cardiorespiratory fitness was measured by the participants VO2 max in mL/kg/min at baseline and at the end of the 14-week intervention period. The VO2 max was directly obtained from all study participants using a face mask that measured the concentration of inhaled and exhaled gasses during a graded exercise stress test using the Bruce protocol . During 2) was calculated from these measurements.CVD risk factors including BP, heart rate (HR), BMI, fasting total low-density lipoprotein (LDL), total cholesterol, and fasting glucose were measured at baseline and at the end of the 14-week intervention period. The systolic and diastolic BP (mm/Hg) of each participant was assessed three times at one-minute intervals after the participant was seated with legs uncrossed for 5 minutes. The last two readings were averaged for the BP measurement. Height (cm) and weight (kg) were measured without shoes, and BMI (kg/mThe Duke Activity Status Index (DASI) questionnaire was used to assess self-reported physical activity among all participants. The DASI is a 12-item questionnaire that assesses both activities of daily living and leisure-time physical activity. The summed score correlates with peak oxygen consumption . Higher The European Organization for Research and Treatment of Cancer QoL core questionnaire (EORTC QLQ-C30) and its associated breast cancer module (EORTC QLQ-BR23) were used to measure QoL and were scored according to the EORTC scoring manual . The EORThe Brief Fatigue Inventory (BFI) questionnaire was used to measure self-reported fatigue. BFI is an instrument for evaluation of fatigue and its impact on daily life in patients with cancer. The BFI is a short, validated measure of fatigue severity which complements findings from the EORTC questionnaires .Participants were asked to rate their degree of satisfaction with the exercise portion of the program, the education portion of the program, and the overall cardiac rehabilitation experience on a scale of 1–10, with 1 being extremely dissatisfied and 10 being extremely satisfied.Adverse events were reported using the Common Terminology Criteria for Adverse Events v4.0 (CTCAE) of the National Cancer Institute. Grade 3, grade 4, and grade 5 toxicities were reported as adverse events.The primary endpoint of the study was to assess the feasibility of conducting a 14-week CR program in women with breast cancer after completion of therapy. Feasibility was defined as completion of at least 30 sessions of the program without serious adverse events (SAE) in 80% of patients and was reported as a percent with 95% confidence interval.Secondary endpoints of the study included the change in cardiorespiratory fitness and DASI. Tertiary endpoints included CVD risk factors, QoL, and patient satisfaction at baseline and 14 weeks.t-test or sign-rank test. All data analyses were performed using SAS 9.4 .Summary statistics for patient demographics and disease characteristics were calculated. The proportion of participants who completed the CR program was calculated, along with the 95% confidence interval (CI). The change in each endpoint from baseline to 14 weeks was calculated and summarized by mean, standard deviation, and 95% CI. The difference in these outcomes from baseline to 14 weeks was compared using either a two-sample n = 13, 54%), stage I  or II  breast cancer.Twenty-five participants were consented and enrolled in the study . Particin = 18) of those participants who enrolled completed the CR program . Seven participants withdrew from the study. One patient could not tolerate the schedule, one did not want to start the program, two developed an arrhythmia during the program and were unable to return, two withdrew consent, and one did not return for the program for undocumented reasons. 60% of those enrolled in the study  completed 30 or more sessions and adhered to the study protocol. Of the 18 participants who did not withdraw from the CR program, 83% (n = 15) adhered to the study protocol and completed 30 or more sessions without SAEs.Seventy-two percent (p=0.587)  .p=0.595), systolic BP , and diastolic BP  between baseline and 14-week follow-up. Improvements were also found in BMI , fasting blood glucose , total cholesterol , and LDL cholesterol , mean BFI score , and QLQ-C30  scores during the treatment period. p=0.007) and QLQ-C30 (p=0.008) as early as at the 8-week study period. On the BR23 scales, participants reported improvement in body image, sexual functioning, breast symptoms, and systemic therapy side effects over the 14-week period.There was improvement in the patient reported physical activity according to the DASI  reporting extreme satisfaction, with the exercise portion of the program. Most of the participants  were also very to extremely satisfied with the education component of the program, and 15 participants (83.3%) were very to extremely satisfied with the overall CR experience.Of those 18 participants who remained in the CR program until the end of the 14 weeks, 15 participants (83.3%) rated they were very to extremely satisfied, with the majority (Two patients withdrew from the study due to serious adverse events related to cardiac arrhythmias. One patient experienced ventricular tachycardia (grade 4) and required cardiac catheterization during the study period and could not continue with the study protocol past week 10. Another patient experienced supraventricular tachycardia (SVT), and the patient could not return to the study protocol following treatment for the SVT (grade 3). These events did not occur during the CR sessions, but are possibly related to the study intervention. Moreover, one patient experienced a headache during the study intervention (grade 1).The breast cancer survivors who successfully completed the 14-week CR intervention have shown significant improvement in patient reported physical activity, fatigue, and quality of life (QoL), but without significant improvement in CVD risk factors. Our long-term goal is to decrease the burden of CVD among breast cancer survivors. Moreover, with our central hypothesis, the cardiac rehabilitation intervention will improve cardiorespiratory fitness, CVD risk factors, and QoL among survivors. To mitigate CVD risk in cancer patients, there is a need of effective strategies, and the use of a cardiac rehabilitation (CR) programs has been significantly helpful in selected patients. The cardiooncology rehabilitation (CORE) model has been used to identify patients at high risk of CVD including cardiotoxicity due to cancer therapies . Recent It is estimated that nearly 90% of women diagnosed with breast cancer will be alive in 5 years, which has important implications for CVD morbidity and mortality . UnfortuA recent report indicates that older female breast cancer survivors were more likely to die of CVD than breast cancer. Moreover, those patients that had comorbid conditions, such as CVD, had similar or worse survival compared to survivors with a higher stage of breast cancer without these comorbid conditions . Early rThe primary objective of this study was to assess the feasibility of a CR program in the breast cancer population and is one of the first studies to prospectively assess the feasibility of a CR regimen in this population. Although only 60% of those enrolled on the study adhered to the protocol, 83% of those who completed the CR program were able to adhere to the program and complete 30 or more sessions. This rate is similar to an average completion rate of 60% for the general CR population . The finThere have been conflicting results on changes in cardiac markers from a CR program. One study did not demonstrate an improvement in heart rate and blood pressure from baseline to postintervention , while aA CR platform is known to improve the quality of life for patients that complete the program, including improvement in fatigue , 47. SevThe strength of this study was that it used an existing outpatient CR program, including its convenience to hospital-based services and treatments, which have been shown to be efficient and cost effective from a health services perspective . Most prThe limitations of the study include the follow-up at only 8 and 14 weeks from the last treatment. Further investigations with longer-term follow-up are needed to assess duration of the positive effects of the program and to further evaluate changes in cardiac risk factors. Another limitation of the study includes the overall general health of the participants and lack of significant comorbidities in the CR program. Unfortunately, many patients in the oncology population have other comorbidities that would modify their CR program prescription. Moreover, the satisfaction results with the CR intervention are limited due missing data from the people who did not complete the program. Additional important limitations of our study include the lack of a placebo comparison arm, lack of blinding, and no treatment randomization. Therefore, the next step is to complete a double-blind, randomized controlled trial with significantly larger study population, comparing VO2 max in patients undergoing CR program to current standard of care follow-up, which does not include an organized exercise program.Taken together, our study suggests that CR program in breast cancer survivors is feasible but does not meet is predefined criterion of feasibility. Adherence to the CR program among breast cancer survivors was similar to the general population. While a CR intervention in breast cancer survivors showed a modest improvement in VO2 max and CVD risk factors, it did demonstrate an improvement in patient reported outcomes of increased physical activity, fatigue, and QOL, in a significantly limited period of time suggesting that a larger randomized clinical trial would enroll much higher patient number that should be undertaken.


Breast cancer incidence is increasing in Asia. However, few women in Singapore attend routine mammography screening. We aim to identify women at high risk of breast cancer who will benefit most from regular screening using the Gail model and information from their first screen .In 24,431 Asian women (50–69 years) who attended screening between 1994 and 1997, 117 developed breast cancer within 5 years of screening. Cox proportional hazard models were used to study the associations between risk classifiers , and breast cancer occurrence. The efficacy of risk stratification was evaluated by considering sensitivity, specificity, and the proportion of cancers identified.Adjusting for information from first screen attenuated the hazard ratios (HR) associated with 5‐year absolute risk , but improved the discriminatory ability of the model . The sensitivity and specificity of the adjusted model were 0.709 and 0.622, respectively. Thirty‐eight percent of all breast cancers were detected in 12% of the study population considered high risk .The Gail model is able to stratify women based on their individual breast cancer risk in this population. Including information from the first screen can improve prediction in the 5 years after screening. Risk stratification has the potential to pick up more cancers. Readily available information from the first mammography screening should be used to help women make informed choices on attending regular screening. Having a false‐positive result or dense breast should not deter from mammography screening; conversely, they should consciously attend mammography screening. Early detection of less‐advanced breast cancers requires less aggressive treatment and confers a survival benefit.Mammography screening is not perfect. Despite advances in mammography techniques, it is estimated that 10%–29% of breast cancers are missed.Traditional breast cancer risk factors considered for risk‐based screening include family history of the disease, ethnicity, age, breast biopsy results, and breast density.The campaign for nation‐wide breast cancer screening in Singapore started in 2002. Under the program, biennial mammography screening is heavily subsidized for citizens and permanent residents above the age of 50 years.A local focus group study identified concerns such as painful and uncomfortable experience, low perceived self‐risk of breast cancer, need for timely reminders, and misinformation as barriers to routine screening.In this population‐based study comprising 24,431 women attending mammography screening in Singapore, we attempt to identify women who would benefit most from regular screening, using the Gail model and additional information on false‐positive results and breast density from the first screen.22.1The Singapore Breast Screening Project (SBSP) was designed as a population‐based randomized trial on mammography screening of women aged 50–64 years, with 69,473 (41.7%) women randomly selected and invited for this single free screening.Breast cancer cases were identified via linkage with the Singapore Cancer Registry with the latest date of occurrence set at December 2007. We excluded 98 women whose breast cancer was detected during screening, 18 whose self‐reported age at first birth was before their age of menarche, three with invalid age and two with unknown date of diagnosis. A total of 24,431 women were included. In this dataset, 117 breast cancers were diagnosed within 5 years after screening; 21 were diagnosed between 12 and 24 months from screening This study was approved by the SingHealth Centralised Institutional Review Board (IRB: 2009/985/B).2, BMI) was retrieved from the questionnaire, and missing values were replaced by the mean. Missing values for the Gail model variables were coded as the reference level.Details on self‐reported breast cancer risk factors included in the Gail model—age at menarche, age at first live birth, prior biopsy, and family history of breast cancer—were obtained from the questionnaire administered at recruitment. Women who had a biopsy done during screening were coded as 1 for the risk factor biopsy and the rest were coded as 0 (no biopsy done in the past year). Family history of breast cancer was ascertained as no/yes for mother, sister(s), and daughter(s). Number of first‐degree relatives with breast cancer was coded as 0 (no family history), 1 , or 2 (any two first‐degree relatives). Information on recall for additional follow‐up due to suspicious findings on the mammogram  and percent mammographic density was obtained from the first screening result mediolateral oblique views of both breasts were used in measuring mammographic density, detailed process is in Lee et al..2.2Breast cancer risk factors, recall status, and mammographic density were compared between 117 women who develop breast cancer within 5 years from screening date and 24,314 women who did not .To obtain the 5‐year absolute risk of each individual, we first estimated the Gail model relative risk score using Asian weights and attributable risks from the BCRA package in R.The association of the Gail model 5‐year absolute risk, recall status, and mammographic density with breast cancer development within 5 years from screening date was studied using the Cox proportional hazards model. In addition, we adjusted for body mass index, which is a well‐known confounder of the mammographic density and breast cancer risk association.Breast cancer case/non‐case classification was done using logistic models with one or more factors . The discriminatory ability of the logistic model was measured using the area under the receiver operating characteristic curve (AUC). Sensitivity and specificity reported were based on cut‐off points identified by Youden's J statistic.The performances of the Gail model 5‐year absolute risk, recall status, and mammographic density as risk stratification tools were evaluated by considering sensitivity, specificity and the proportion of all breast cancers identified.All analyses were performed in R version 4.0.2.3p < 0.001), to have a family history of breast cancer (p < 0.001), to be recalled for additional follow‐up (p < 0.001), or to have higher breast density (p < 0.001) as compared with women who did not.A total of 24,431 women were included, with 117 women developing breast cancer within 5 years of their screening date. Age at study entry was not significantly different between women who developed breast cancer and those who did not  for relative risk and a median of 0.9% (range: 0.5%–6.3%) for absolute risk Figure . Women a3.2p < 0.001) (Table p < 0.001). Having a higher mammographic density was significantly associated with breast cancer occurrence vs. <15% [95% CI]: 2.6 [1.5–4.3], p < 0.001), and HR[≥50%]) vs. <15% [95% CI]: 2.4 [0.6–10.3], p = 0.234), adjusted for the Gail model 5‐year absolute risk and recall status  Table . This ined HR15%–5% vs. <13.3n = 28) developed breast cancer in the next 5 years; these breast cancer cases formed 24%  of all breast cancer occurrence in that period. Twenty‐five percent (n = 30 of 117) of all breast cancer occurrence were from women who were recalled at first screen, and 2% (n = 2 of 117) had mammographic density above 50%. Forty‐four percent of all breast cancer occurrence can be identified when we defined high risk as a combination of having any one of the following, (i) the Gail model 5‐year absolute risk of 1.3% and above, (ii) positive recall status, or (iii) mammographic density of 50% and above. This was observed due to the low concordance between the three criteria  Table . An impr3.5Based on a binary division of breast cancer risk (high or low) by the different scoring systems , screening only the high‐risk group out‐performs screening a random sample of a similar size in terms of the number of breast cancers detected Figure . Thirty‐4As the most common cancer diagnosed among women with tangible benefits from early detection, breast cancer is an ideal disease for an organized population cancer screening program.Our results showed that risk stratification of screening participants using breast cancer risk factors included in the Gail model picked up more cancers than a random sample among those in the highest risk group. In addition, information from a single mammogram, such as mammographic density and recall status, helped to identify women at higher risk of developing breast cancer. Each risk model has its own limits and flaws in terms of sensitivity and specificity. The best performing discriminatory ability (AUC = 70%) was observed when the Gail model 5‐year absolute risk, mammographic density, and recall status were considered together in our study, making it possible to generate individual breast cancer risk profiles at first screen. The tailored information may be communicated to the screening participant to raise breast cancer awareness and education. Women at high risk may be particularly targeted to attend regular screening.For a mammography screening program to be effective in catching breast cancers early, the screening interval (i.e. time between two screening mammograms) must be shorter than the time the tumor takes to grow larger than the preclinical screen‐detectable phase, otherwise known as “sojourn time”.At the individual level, the start age and screening interval may be earlier and shorter for women at higher risk of breast cancer.Our results show that high‐risk individuals, especially those with a high Gail model 5‐year absolute risk or who were recalled previously, are more likely to benefit from adherence to routine mammography. The accuracy of mammography is lower in women with dense breasts. While the sensitivity of the method is approximately 77%–95% across all ages, it can be as low as 63% in women with high mammographic density.It should be noted that the incidence and mortality rates used in the calculation of the Gail model in this study are from more recent years as compared to the period of data collection. Sensitivity analysis using incidence rates (period: 1993–1997) and mortality rates (year 1997) closer to the recruitment period showed a shift in distribution of 5‐year absolute risk to that of lower risk , as expected of a period of lower breast cancer incidence. Nevertheless, the result from the sensitivity analysis shows that the majority of breast cancer cases that occurred within 5 years of screening can be identified in the top five percentile of the 5‐year absolute risk and/or positive mammogram recall status group . In addition, the relative associations between the 5‐year absolute risk and breast cancer occurrence remain (adjusted HR in the period 1993–1997 [95% CI]: 2.8 [1.7–4.5]); vs. period 2013–2017: 1.9 [1.4–2.6]).The strength of our study is that it is a nation‐wide screening program with over 24,000 participants from an unselected population of women aged 50–64 years.BRCA1/2 genes or polygenic risk scores, will help further identify high‐risk individuals. The Women Informed to Screen Depending on Measures of Risk (WISDOM) trial, for example, incorporated genetic risk  in their assessments.Several limitations apply to our study. SBSP was established over two decades ago as a pilot study to support a nation‐wide mammography screening service in Singapore.5The Gail model is able to stratify women based on their individual breast cancer risk. Information from the first screen improved the prediction for cancers developing in the next 5 years after screening. However, challenges in communicating individual breast cancer risk and other barriers to routine mammography screening  will need to be overcome to increase screening uptake and adherence.None declared.Study design: PJH, JL, and FYW; Provision of data: FYW, WYC, and EHL; Statistical analysis: PJH and JL; Writing of the first draft: PJH and JL; Clinical expertise: FYW, WYC, EHL, and MH; Epidemiological expertise: MH and CKS. All authors read and approved the final version of the paper.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.


A history of breast cancer in the participant’s mother resulted in an odds ratio of 3.12 (1.75–5.59), and a history of breast cancer in the participant’s sibling resulted in an odds ratio of 2.63 (1.85–3.74). There was no interaction between the history of maternal breast cancer and the history of sibling breast cancer. Based on the subgroup analysis, family history was a stronger factor in premenopausal women than in menopausal and postmenopausal women. Conclusions: A family history of breast cancer is a significant risk factor for breast cancer in Korea. Premenopausal women with a maternal history of breast cancer are of particular concern. Intensive screening and risk-reducing strategies should be considered for this vulnerable subpopulation.Background: Breast cancer is the most common cancer among women. The Korean Genome and Epidemiology Study (KoGES) is a large cohort study that is available to the public. Using this large cohort study, we aimed to unravel the relationship between breast cancer development and a family history of breast cancer in Korea. Methods: This cohort study relied on data from the KoGES from 2001 through 2013. A total of 211,725 participants were screened. Of these, 129,374 women were evaluated. They were divided into two groups, including participants with and without breast cancer. A logistic regression model was used to retrospectively analyze the odds ratio of breast cancer history in families of women with and without breast cancer. Results: Of 129,374 women, 981 had breast cancer. The breast cancer group had more mothers and siblings with histories of breast cancer ( Breast cancer is the most common cancer among women ,2. The iThe well-known risk factors for breast cancer include environmental exposure, factors related to reproduction or pregnancy, and lifestyle factors such as diet, smoking, or drinking ,7. ApproFamilial breast cancer accounts for approximately 20–30% of breast cancer . Family The risk of breast cancer is increased up to 5.7 times in individuals with first-degree relatives who have a history of breast cancer and approximately two times in individuals with any first-degree or second-degree relatives with a history of breast cancer ,20,21,22Researchers reported a 6–9% incidence of familial breast cancer in a case-control study with approximately 3000 pairs . HoweverIn this study, we aimed to determine the relationship between breast cancer and family history in Korea using data from the KoGES. We could perform in-depth analyses because KoGES, as an umbrella trial, has six cohorts. To the best of our knowledge, this is the first investigation of a large cohort of participants with a family history of breast cancer in Korea.This cohort study relied on data from the KoGES from 2001 through 2013. A detailed description of these data is provided in a previous study [2), using height and weight as continuous variables. Smoking duration was calculated as pack-years and the consumption of alcohol was measured as the mean consumption (g/day), using the frequency and the type of alcohol.Trained interviewers asked participants about their disease history of breast cancer and their age at the time of diagnosis. Participants were also asked about their family history of breast cancer and family members’ ages at the time of diagnosis. Anthropometric and clinical measurements were obtained from the KoGES consortium. In the present study, we categorized family histories of breast cancer into groups of mothers and siblings (sisters or brothers). Monthly household incomes were categorized into four groups, including no information, lowest (less than $1500), middle ($1500–$3000), and highest (more than $3000). Each participant had descriptive records of menopause status, pregnancy, and other disease history such as hypertension, diabetes mellitus, and hyperlipidemia. Obesity was measured by BMI .The chi-square test was used to compare the rates of sex, income, other disease history, menopause, pregnancy, and family history of breast cancer between the breast cancer group and the control group. Independent p < 0.001).From a total of 129,374 women, 981 women had experienced breast cancer. They were categorized as the breast cancer group. Therefore, 128,393 women without breast cancer were categorized as the control group. The average age in the breast cancer and control groups was 54.2 ± 7.5 and 53.4 ± 8.5 years, respectively . There wIn model 1, a family history of a mother with breast cancer resulted in an odds ratio of 3.21 (1.80–5.74), and a family history of a sibling with breast cancer resulted in an odds ratio of 2.66 (1.87–3.78). In model 2, a family history of a mother with breast cancer resulted in an odds ratio of 2.78 (1.56–4.94), and a family history of a sibling with breast cancer resulted in an odds ratio of 2.86 (2.02–4.07). In model 3, a family history of a mother with breast cancer resulted in an odds ratio of 3.12 (1.75–5.59), and a family history of a sibling with breast cancer resulted in an odds ratio of 2.63 (1.85–3.74) . There wIn the subgroup analysis, participants were divided into groups of 80,583 women who had experienced menopause and 48,791 women who had not experienced menopause . In womeOf 981 women, nine participants were excluded from the subgroup analysis due to missing values for age. Thus, in 972 women with breast cancer, family history and age were re-evaluated with a cut-off of 50 years . A greatIn the present study, we performed a risk calculation of family history based on a Korean nationwide public registry database. By analyzing this public database, meaningful family histories were identified in patients with breast cancer. The odds ratios were 3.12 for family history of a mother with breast cancer and 2.63 for family history of a sibling with breast cancer. There was no interaction between history of maternal breast cancer and history of sibling breast cancer. Furthermore, these family histories were more influential in premenopausal women. Only among women with breast cancer were more family histories observed in the group with onset ages less than 50 years, although the results were not statistically significant.To the best of our knowledge, this is the first investigation of a large cohort of participants with a family history of breast cancer in Korea. After adjustment of multiple environmental risk factors, our study showed that family history was a strong risk factor for breast cancer in a large cohort of Asian women. Particularly, premenopausal women with a maternal history of breast cancer were the most vulnerable group, of which the odds ratio was almost five times higher than the control group. Age, BMI, pregnancy, menopause, other disease, smoking, alcohol consumption, or income could be related to the risk of breast cancer, according to previous studies ,26,27. HInherited breast cancer is different from familial breast cancer, which is driven not only by genes but also by interactions with the environment. However, there is a strong possibility of inherited breast cancer among familial breast cancers ,30. BRCAThe records regarding a family history of cancer have not been standardized in a structured manner . HoweverThe present study has some limitations. First, this study was based on the limited data provided, which is a general limitation of public data analysis. Therefore, our study could not consider covariates such as age at menarche, age at first birth, previous biopsies, or occupational exposures, which might be related to breast cancer. Moreover, we could not conduct a comprehensive investigation of first- and second-degree family histories. Second, there could be recall bias given the use of a survey to collect data, which could lead to less reliable results. Last, the present study has the usual limitations of observational studies such as detection bias and selection bias.In conclusion, a family history of breast cancer is a significant risk factor for breast cancer in Korea. The identification of a family history of breast cancer can contribute to early detection in populations at risk. In particular, premenopausal women with a maternal history of breast cancer are at the highest risk. Intensive screening and risk-reducing strategies should be considered for this vulnerable subpopulation.


Dietary acid load (DAL) may be associated with all-cause mortality (ACM) and breast cancer-specific mortality (BCM), and these associations may be modified by serum polychlorinated biphenyl (PCB) levels. Participants included 519 women diagnosed with first primary in situ or invasive breast cancer in 1996/1997 with available lipid-corrected PCB data. After a median of 17 years, there were 217 deaths (73 BCM). Potential renal acid load (PRAL) and net endogenous acid production (NEAP) scores calculated from a baseline food frequency questionnaire estimated DAL. Cox regression estimated covariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between PRAL and NEAP with mortality. We evaluated effect measure modification by total serum PCB levels (>median vs. ≤median). PRAL quartile 4 versus quartile 1 was associated with an ACM HR of 1.31 (95%CI = 0.90–1.92). In the upper median of PCBs, ACM HRs were 1.43 (95%CI = 0.96–2.11) and 1.40 (95%CI = 0.94–2.07) for PRAL and NEAP upper medians, respectively. In the lower median of PCBs, the upper median of NEAP was inversely associated with BCM . DAL may be associated with increased risk of all-cause mortality following breast cancer among women with high total serum PCB levels, but inversely associated with breast cancer mortality among women with low PCB levels. In 2012, the American Cancer Society (ACS) published its nutrition and physical activity guidelines, which included recommendations of balancing caloric intake with physical activity, eating five or more vegetable and fruit servings each day, and limiting the consumption of processed and red meat . These dThe normal function of the body’s physiological processes is tied to proper acid–base balance . As suchUsing resources from the Long Island Breast Cancer Study Project (LIBCSP), a population-based study of breast cancer, we examined whether DAL was associated with increased risk of long-term mortality following breast cancer. We also examined whether this association was modified by total levels of serum polychlorinated biphenyls (PCBs), synthetic, organic and toxic compounds given that the kidneys serve the important role of maintaining acid–base balance of the body ,18 and PThe LIBCSP is a population-based study of breast cancer initiated as a case–control study with subsequent follow-up of participants to identify breast cancer prognostic factors ,23. BreaThe 591 women with breast cancer included in this study were followed from enrollment in 1996/1997 through 31 December 2014. Date and causes of death were determined by linkage to the National Death Index. Breast cancer-related deaths were identified using the International Statistical Classification of Diseases codes 174.9 and C-50.9 listed on the death certificate. For analyses of all-cause mortality (ACM), women alive at the end of follow-up were censored. For analyses of breast cancer-specific mortality (BCM), we also censored women who died from other causes at the time of death. After a median follow-up of 17 years (range = 0.41–18.41 years), we identified 217 deaths, 73 of which were due to breast cancer.All 1508 LIBCSP women with breast cancer completed a comprehensive interviewer-administered questionnaire on known and suspected breast cancer risk factors. Of these 1481 also completed a modified Block FFQ that captured intake, usual frequency, and portion sizes of ~100 foods and beverages in the 12 months before diagnosis . The FFQPRAL measures the renal net acid excretion by measuring the mean intestinal absorption rates for individual nutrients and post-absorptive metabolism of sulfur-containing amino acids . NEAP fan = 646), as previously reported  or invasive). We also included adjustment for PCBs (≤median [ref] vs. >median), as appropriate.Covariates were identified a priori based on previous studies of DAL and breast cancer incidence/mortality ,16,32, aPTrend was the p-value of the continuous PRAL or NEAP (per SD) variables in age-adjusted and fully adjusted cox regression models. The proportional hazards assumption was assessed using Schoenfeld residuals, which were independent of time . In sensitivity analyses, we restricted the follow-up to seven years to examine whether DAL (using median splits of PRAL and NEAP) was associated with short-term mortality following breast cancer.We used Cox regression modeling to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of the associations between PRAL and NEAP with ACM and BCSM from diagnosis in 1996/1996 to 31 December 2014. We examined quartiles of PRAL and NEAP scores (reference = Quartile 1) as well as the continuous scores per standard deviation (SD) increases. The To examine whether total serum PCB levels modified the associations between PRAL and NEAP with ACM and BCSM, effect measure modification on the multiplicative scale was evaluated by conducting stratified analyses  and by including median split DAL-by-dichotomous PCB level interactions in the Cox regression models. These associations were visualized using forest plots for the fully adjusted models. Interaction on the additive scale was evaluated by examining joint indicators of PRAL or NEAP and PCB levels ; high/low; low/high; high/high) in the fully adjusted model, which were used to compute interaction contrast ratios (ICRs) and 95% CIs . The abs2  between NEAP/PCBs and PRAL/PCBs in fully-adjusted models were 0.02 and 0.06, respectively. For BCSM, all NEAP or PRAL-by-PCB PInteraction’s were ≥0.73. In the upper median of PCBs, mortality rates were elevated for ACM for the upper (vs. lower) median of PRAL  and NEAP  in Model 2. In the lower median of PCBs for Model 2, the upper (vs. lower) median of NEAP was inversely associated with BCSM ; this association was null in the upper median of PCBs   . In sens57–2.36) . There w57–2.36) . The p-vIn this study of mortality following breast cancer, DAL at diagnosis was not associated with increased risk of ACM or BCSM after a median 17 years of follow-up among all women. However, PCB exposure modified the relationship between DAL and ACM; we identified interactions between PCBs and DAL on the additive and multiplicative scales. In the lower median of PCB levels, higher NEAP scores were inversely associated with mortality, while in the upper median of PCB levels, higher DAL was associated with an increased risk of ACM.To date, the relationship between DAL and mortality among women with breast cancer has only been examined in one study . In the Several hypotheses underlying the association between DAL and breast cancer mortality have been proposed. Cancer patients and survivors have been reported to have reduced kidney function, which can lead to increased susceptibility to higher acidogenic diets ,36,37,38The mechanism of action by which DAL impacts overall mortality may also be due to its association with other comorbidities. The association between PRAL and ACM may be related to the impact of diet’s acidity on the cardiovascular system, as excess diet alkalinity and acidity was associated with higher mortality from cardiovascular conditions in Swedish and Japanese adults ,45. High2 respiration) and non-volatile (H+ excretion) acid from consumed food are excreted by this organ [The additive effects of PCB exposure and acidogenic diets on mortality of cancer survivors has not been well established, but may be due to the negative effects of PCBs on kidney function. The kidneys are crucial to maintaining acid–base balance, as the metabolic process which generates both volatile , which may have higher measurement errors in assessing DAL than, for example, repeated 24 h recalls and we did not use a biomarker of DAL to confirm pH levels estimated using PRAL and NEAP values. Third, we did not collect information on metastasis in the LIBCSP; however, breast cancer stage at diagnosis was included in the models to help control for confounding by severity of breast cancer. Last, the LIBCSP was comprised primarily of White women and therefore the findings might not be generalizable to the all women with breast cancer. Future studies should consider collecting multiple biomarker-based measurements of DAL over time in a diverse sample of women with breast cancer, to better understand the relationship between DAL and mortality.Although we did not find a strong relationship between baseline DAL and mortality following breast cancer, there was a modest increase in risk of ACM among women with higher PCB levels and higher DAL. Women with breast cancer with diets high in animal fats, thus, may be at increased risk of mortality due to the resulting acid–base imbalance and exposure to PCBs. Although exposure to PCBs has declined since their use and production have been banned in the US, some PCB congeners are still detectable at high levels in US children and adults . This is


As part of the bioinformatics studies, we utilized National Cancer Institute (NCI)’s Breast Cancer Risk Assessment Tool to estimate the five-year period and lifetime risk of breast cancer development among Iraqi risky women. BRCA1 or BRCA2 genes were included. Totally, 110 risky women aged 21-67 (mean=36±7.4) years were interviewed by a series of questions regarding the risk of breast cancer development. Moreover, 100 cases with mutation in the Our results demonstrated that the patient’s estimated risk of breast cancer development during the next five years and lifetime (until the age 90 years) included 0.96% (p=0.211) and 9.97% (p=0.002), respectively being relatively low. Accordingly, the lifetime risk for the breast cancer development was significantly higher (10.38%) than that of 5-year. However, the age of patients was not significantly associated to the breast cancer development as there was no significant difference among various age groups.  It was concluded that long-term or lifetime period plays as a significant risk factor for developing breast cancer among female patients who had had a screening episode in Iraq. Cancer is accompanied by as a set of pathways that promote the cells multiplication without natural control. Cancer can be considered as the major or second leading cause of death before age of 70 years in 91 countries, and as third or fourth most reason in 22 additional countries . Breast cancer contains various subtypes in terms of morphologic structures with various risk factors, responding differently to the anticancer therapy. Around two million women have been recently recognized with breast cancer in 2018, worldwide, accounting for ¼ of total cancer cases among women. Mutations in the BRCA1 or BRCA2 genes induce the progress of breast cancer. Breast cancer is the major cancer in most of countries and is considered as the main cause of deaths due to cancer in more than 100 countries . Breast cancer-mediated death cases has increased to be involve 2,173, or 1.23% of all deaths in Iraq, according to the WHO data published in 2017. Cancer is one of complicated and multi-stage cell processes during which the cells lose control of normal proliferation . The cancer is likely to be classified as the major reason of death and the most important obstacle to longevity predictable in any area worldwide in the 21Characteristic molecular subtypes of breast cancer were identified using gene expression, a process which is expensive and complex . Numerous risk factors such as inherent cases being family history, age, late menopause, early menarche, and sex have been stated. Other epigenetic factors are associated with an increased risk of breast cancer including alcohol consumption, menopausal obesity, the use of combined progestin and estrogen after menopause and smoking. Many risk factors affect breast tissue exposition to hormones for life. Concerning reproductive hormones, it is likely that they affect the risk of breast cancer through promotion of cancer cell growth, proliferation and increase of the likelihood of DNA damage . Bioinformatics simply means applying computational techniques for understanding and organizing information related to biological macromolecules. This combination of the two subjects is largely attributed to the fact that biology itself is information technology. Simultaneously, there has been significant progress in technologies that provide raw data. According to studies by Kerlavage, the experimental lab can easily produce more than 100 GB of data per day . Bioinformatics make it accountable for analysis, distribution, and the storage of biological information. It sometimes refers to the formation and development of algorithms, theory to solve formal, analysis of biological data, statistical techniques, and practical problems . The aim of this study was assessment of the risk of breast cancer development by application of NCI tool among Iraqi women patients who had had a screening episode (reference screening). Patients’ demographic dataHerein, 110 female patients with risk of breast cancer development were included. The age of them included 21-67 (mean=36±7.4) years. They had no other complications such as diabetes, immune-compromising conditions, infections such as human immunodeficiency virus (HIV), corten-therapy and pregnancy. Moreover, all were inside the country and had same race and had no prior history of ductal or lobular carcinoma  or invasive carcinoma before the screening. Additionally, the familial information was not included in the Gail analyses. Questionnaire of interview and provided data The questionnaire for risk factors assessment was filled through appointment letters for women (lifepool participants) who admitted for screening. Variables included age, race/ethnicity, a breast biopsy, age at the first live birth, and the number of first-degree relatives with breast cancer. The patients had the consent of participation in the study and the data was collected using both interview with them and their medical records and analyzed at further stage as a lifepool cohort study. The data was provided from baseline questionnaire for all participants who filled it in the time spanning of study. Risk assessment using NCI’s Breast Cancer Risk Assessment ToolWe estimated lifetime and five - year risk of developing breast cancer using NCI’s Breast Cancer Risk Assessment Tool (or Gail model). This model allows researchers to rate a woman’s risk of invasive breast cancer within the next five years and the age of 90 (lifelong risk). The tool evaluates/calculates collected data from interviews by patients including woman’s personal medical or familial history/first degree relatives  to estimate risk factors and possibility of developing breast cancer among them. This tool cannot accurately estimate the breast cancer risk for a range of conditions including patients with a previous history of invasive carcinoma, LCIS or DCIS and patients with a breast cancer-promoting mutation in BRCA1 or BRCA2. We used this tool to analyze the collected lifestyle, socio-demographic and health-related data. The Gail scores were used for evaluating their association with the future invasive breast cancer through Gail input variables . Statistical MethodsAs a personalized screening protocol, the NCI tool (or Gail model) calculated the risk percentage of developing breast cancer in 5 years and lifetime periods. The Gail risk scores were assigned using codes as described before to generate the risk probability during a specified year. Intended for intergroup evaluation, the typically distributed variables were compared using independent t-test in the SPSS software version 20. The significant statistical level was considered as p<0.05.The studied population included 110 female patients with risk of breast cancer development. Their age ranged 21-67 (mean=36±7.4) years . Depending on the obtained results, the mean patients’ evaluated risk of invasive breast cancer over the next five years included 0.96% which was not significantly correlated with the breast cancer development 0.211, .Furthermore, there was a significant higher relation (p<0.005) between the mean patients’ risk of lifetime (to age of 90) and the breast cancer development in the carcinoma group compared to that of five year risk, though being relatively low .In addition, as revealed in the BRCA1 and BRCA2 breast cancer allergy genes. This type of mutation is found in approximately less than 1% of the general population but mostly occurs in specific ethnic groups such as Eastern Europe . Results of studies about breast cancer risk in women with these mutations by age 70 have been in range of 44 -78% of women with BRCA1 mutations and 31 -56 % with BRCA2 mutations with increased breast cancer . Approximately 15-20% of familial breast cancer cases are attributed to the BRCA1 or BRCA2 gene mutations . It is predestined that 5- 10 % of breast cancer cases are caused by inherited mutations such as those in the DCIS is a group of unusual breast changes begin in the cells lining the breast canals. DCIS is a non-enlarged form of breast cancer because of lack of increase in nontypical cells size in the cell layer in which they develop. It was considered to be the most prevalent type of breast cancer on-site accounting nearby 83% of cases diagnosed during 2006-2010. Indeed, several of these cancers grow very tardily not influencing women’s health even if left untreated. Studies have outlined that ≥one- third of DCIS cases will develop into invasive cancer if neglected or left untreated . LCIS is cancer-like tumor and an indicator of developing the risk of invasive cancer. During 2006-2010 LCIS prevalence was diagnosed and found to be significantly less prevalent than DCIS, where it accounted for approximately 12% of breast cancers on site . Alhough rarely developing into invasive cancer, patients with LCIS are predisposed 7 -12 times higher to progress invasive cancer in each breast than those with the absence of LCIS . LCIS usually does not appear using a mammogram and is usually detected through a biopsy taken for other causes. Since both of these stages are pre-cancerous lesions of the breast and require cancer-oriented treatment, it is necessary to distinguish between pure LCIS from DCIS and pleomorphic LCIS . In previous studies, the NCI Breast Cancer Risk Assessment validity has been confirmed in multiple settings, including, among women at high risk, representing accurate results regarding the number of breast cancer cases observed at different time intervals . This site does not integrate with second-degree relatives, including the age of onset of breast cancer in relatives or paternal kin, so the ACS guidelines did not advise it to evaluate the individual patient’s life during the annual MRI examination, BRCAPRO or other models that are greatly influenced by family history . In the Claus et al., (1993) Boadicea  and the Tyrer-Cuzick (2012) models, the calibration was not tested in the general population. In another study, Amir et al., (2003) provided overall review of these models. A small study in a group of 64 high-risk clinics only found that the expected number of cases corresponded to the cases observed for the Tyrer-Cuzick model, but these expectations were somewhat low for the NCI breast cancer risk tool being Brcapro and Claus model. A group of studies that compared several scenarios, including families with affected relatives, outlined that the Tyrer-Cuzick model and breast cancer risk assessment tool commonly accord the highest risks in families with at least one affected relative, and Bracapro gave the lowest risk  and another research comparing Claus Model and the NCI breast cancer risk assessment tool in 491 women with a family history of breast cancer, found that the average lifetime risk was higher in the breast cancer risk assessment tool (13.2%) compared to the Claus Model (11.2%). Previous studies have illustrated that Brcapro and the Claus Model usually has lower risks than the NCI breast cancer risk assessment tool in a high-risk clinic population . In a previous study, atypical hyperplasia has not been reported in the NHIS , which can cause to decrease the risk of breast cancer. For these reasons, the study has shown that the breast cancer risk assessment tool is beneficial for estimating the convergent number of women patients with a lifetime risk of ≥ 20% in the general people, though their calculations can reduce the real number . In our study, according to NCI’s Breast Cancer Risk Assessment Tool calculations, there was no significant association between patients and the average risk of developing breast cancer for five years. This model offered that the average risk for women was lower than that of same age and race/ethnicity for patients in the general U.S. population in 5 years. However, the average lifetime risk of developing breast cancer was significantly associated with patients’ conditions. BRCA1 and BRCA2 genes, there was no significant association regarding neither 5-year nor the lifetime risk with the breast cancer development. Bioinformatics is critical in the field of genetic pharmacology and necessary to improve precise tools for active treatment based on the tumor biology of all patients . Using appropriate bioinformatics tools, these findings predict risk factors, demonstrate drug resistance, provide deeper knowledge and understanding of treatments and predict risk factors, open new fields and goals and vital and disease-related signs to those beneficial drugs. Noticeably, the impact of epigenetic factors on the etiology of breast cancer is inevitable. Although a set of research related to DNA methylation, those revealed various patterns including oncogenes and tumor suppressor genes, a small portion of them link genome data with transcriptome . Huge developments that have occurred both in bioinformatics and their application are largely probable by multidisciplinary teams seeking depth and focused study. The combination of tools, methodologies, specificity, databases, and sensitivity must be assessed in a perfect matter. Furthermore, the results confirmed many molecular techniques before translating into clinical practice . In our study we observed that the age of women patients was significantly correlated to the average, patients’ 5 – year and Lifetime risk as shown in In conclusion, the NCI site was suitable for cases of breast cancer except those related to . The results illustrated that the patients risk was relatively low. Lifetime but not 5 years risk was significantly associated with developing breast cancer. The authors confirm contribution to the paper as follows: study conception and design: Dhafer A. F. Al-Koofee; data collection: Zainab Abbas Al Talebi; analysis and interpretation of results: Seenaa kadhum Ali, Zinah Kadhim Kareem; draft manuscript preparation: Dhafer A.F. Al-Koofee; All authors reviewed the results and approved the final version of the manuscript.