Is gonadotrophin stimulation as part of IVF associated with an increased risk of relapse in breast cancer?Controlled ovarian stimulation (COS) in connection with IVF in women with previous breast cancer was not associated with an increased risk of breast cancer relapse.Breast cancer is the most common malignancy among women worldwide and the leading cause of cancer death among females. The use of COS with gonadotrophins with subsequent cryopreservation of oocytes or embryos in order to enhance the chances of pregnancy after cancer treatment is the current most established fertility preservation method for women with breast cancer. To date, there are only a few small retrospective hospital-based controlled studies evaluating the risk of breast cancer relapse in patients undergoing fertility preservation with or without COS, showing no evident risk of relapse in breast cancer after the use of gonadotoxic agents.This was a retrospective, population-based cohort study comprising 5857 women with previous breast cancer of whom 337 were exposed to COS. Exposure (COS) and outcomes (relapse and death) were identified for all patients from 2005 to 2014 by assessing the National Quality Register for Assisted Reproduction, the Swedish Medical Birth Register, the National Patient Register, the Swedish Prescribed Drug Register, the Swedish Cause of Death Register, the National Breast Cancer Register and the Swedish Cancer Register. Matching according to set criteria was possible for 334 women, who constituted the control group. A total of 274 women had undergone IVF after completing breast cancer treatment and 63 women had undergone COS for fertility preservation at the time of breast cancer diagnosis.Women aged 20–44 years previously diagnosed with breast cancer and exposed to COS were matched for age at breast cancer diagnosis ±5 years, tumour size and lymph node involvement with a non-exposed control group, including women with known T- and N-stages. In a subsequent analysis, the matched cohort was assessed by also including women with unknown T- and N-stages. A secondary analysis comprised the entire non-matched cohort, including all women with known T- and N-stages. Also here, a subsequent analysis included women with missing data for T- and N-stages. The risk of relapse in breast cancer was estimated as crude hazard ratios (HRs) and 95% CI using Cox proportional hazards models in the primary and secondary analyses where T- and N-stages were known: otherwise the risks of relapse were only given descriptively.P = 0.22). In the subsequent analysis, also including women with unknown T- and N-stages, relapse occurred in 27 of 337 (8.0%) women having undergone COS compared with 71/334 (21.3%) among the non-exposed. In the secondary adjusted analysis, relapse occurred in 20 of 126 (15.9%) exposed women and in 918 of 3729 (24.6%) non-exposed women . In the subsequent analysis, including unknown T- and N-stages, relapse occurred in 27 of 337 (8.0%) women in the exposed group and 1176 of 5520 (21.3%) in the non-exposed cohort.In the primary matched analysis, relapse occurred in 20 of 126 women exposed to COS (15.9%) compared with 39 of 126 (31.0%) in the control cohort  is rather short. Finally, we cannot be sure whether the prognostic information from receptor status, showing a lower incidence in the exposed group, is representative. Information on T- and N-stages was missing in more than half of the patients.In this large, retrospective, matched cohort study, we found no increased risk of relapse in breast cancer among women who had been exposed to gonadotrophins as part of IVF. This is reassuring but might be confounded by the selection of a group of women with a more favourable prognosis than those not undergoing IVF. The present study strengthens previous findings by being large, national and register based. Its results are applicable to women undergoing fertility preservation as well as to those undergoing regular IVF treatment.Supported in part by grants from the Swedish state under the agreement between the Swedish government and the county councils the ALF-agreement (ALFGBG-720291), The Assar Gabrielsson Fund (FB 15-20), The Breast Cancer Fund and the Swedish Association of Local authorities and Regions, SKR. There are no conflicts of interest to declare.N/A WHAT DOES THIS MEAN FOR PATIENTS?For women who desire to have a child after breast cancer treatment difficulties in becoming pregnant may occur due to side effects of the chemotherapy used for treatment of the cancer. Some women may then use assisted reproduction techniques. The most common approach to preserving the ability to conceive after breast cancer is by hormonal stimulation of the ovaries with subsequent freezing of eggs (oocytes) or fertilised eggs (embryos) before cancer treatment begins. To date, there is limited knowledge showing no apparent risk for breast cancer relapse after hormonal stimulation of the ovaries. In the present study, women who had breast cancer and were treated with hormonal stimulation as part of IVF were compared with a group of women not exposed to such hormonal stimulation: we found that hormonal stimulation of the ovaries was not associated with an increased risk of relapse in breast cancer. This is a reassuring result. However, although many important risk factors were considered in our study, particularly the T- and N-stages as well as the receptor status, the group of women who underwent hormonal stimulation of the ovaries was probably a group with a better prognosis concerning breast cancer, compared with the women not undergoing ovarian stimulation.Breast cancer is the most common malignancy among women in the world and the leading cause of cancer death among females, accounting for 23% of the total cancer cases and 14% of the cancer deaths . Althougper se using controlled ovarian stimulation (COS) with gonadotrophins  to identify the population, exposure and outcome.in situ (Cis/Tis) and/or T0 according to the Tumour-, Node- and Metastasis staging system (TNM) as no evident risk for cancer relapse can be shown for these early-stage cancer categories , the National Quality Register for Breast Cancer and the Swedish Cancer Register were used to identify all women, aged 20–44 years, who were diagnosed with breast cancer from January 2005 to December 2014. These women formed the study base. The diagnosis malignant tumour in the female breast was coded as C50 in the International Classification of Diseases (ICD)10 (from 1997). We excluded all early-stage breast cancer defined as cancer Exposure to gonadotrophins was defined as either the start of a fresh IVF cycle including stimulation with gonadotrophins, as registered in the National Quality Register for Assisted Reproduction (Q-IVF) or the acquisition of a prescription of gonadotrophins from the Swedish Prescribed Drug Register. Between 2005 and the end of 2006, data were collected from the Swedish Prescribed Drug Register using the unique codes according to the Anatomical Therapeutic Chemical Classification System for all available prescribed gonadotrophins (code G03G). From 2007 and onwards, exposure data were collected from Q-IVF. We further identified the COS exposure subgroups for the purpose of fertility preservation by setting a time limit from breast cancer diagnosis to COS exposure to a maximum of 3 months. The main indication for IVF treatment was subfertility. This variable could not be individually assessed, since it was not registered in Q-IVF. The COS regimens included gonadotrophin stimulation in combination with a GnRH antagonist and in recent years an aromatase inhibitor from the start of the cycle.Patients in the study base without any stimulated IVF-cycles or use of gonadotrophins formed the non-exposed cohort, from which controls were identified.The outcome was relapse in breast cancer. Relapse as a variable in the National Breast Cancer Register was reported only in one region in Sweden; Stockholm-Gotland. For this study, we created a coding template where we defined typical patterns of ICD diagnoses and procedures in the NPR that indicated relapse in breast cancer (Supplementary Table SI). This was carried out independently by two authors whose results were compared. Any disagreement was resolved in consensus with a third author with oncological expertise. The Stockholm-Gotland part of the National Breast Cancer Register was used as the true reference to which the defined relapse cases were compared.The coding template was further elaborated by including and excluding diagnoses and treatments to achieve optimal sensitivity and specificity to correctly identify relapse before using it in the entire cohort.Potential confounding factors affecting the risk of relapse in breast cancer were accounted for through available register data. The Medical Birth Register (MFR) provided data on previous childbirths and BMI at the start of pregnancy, as well as smoking (3 months before pregnancy). From the National Breast Cancer Register, information on tumour stage and hormone receptor status as well as a woman’s age at the time of diagnosis was collected. To define the time from diagnosis to exposure, we used the dates from the National Breast Cancer Register, the Swedish Cancer Register, the MFR, the Q-IVF and the Swedish Prescribed Drug Register. Q-IVF was established in 2007. The register covers ∼20 000 COS cycles annually, with treatment results and potential medical risks for both IVF children and their parents.The MFR, which started in 1973 and covers almost all births in Sweden, has high validity  starting from 2008 (coded as C50) and categorised by gender, age, tumour stage (according to the TNM system) and procedures.TheThe Swedish Cancer Register was founded in 1958 and covers the entire population. Approximately 60 000 malignant cases of cancer are registered every year in Sweden.Descriptive statistics  are presented with number and percentages for categorical variables and with mean, SD, median, minimum and maximum for continuous variables.The primary analysis was conducted on the matched cohort in which each exposed woman was matched to one control and included women with known T- and N-stages. Matching variables were age at breast cancer diagnosis (±5 years), tumour size , T2 (<5 cm), T3 (>5 cm)) and lymph node involvement (N-stage 0 or 1). In a subsequent analysis, patients with unknown T- and N-stages were included.A secondary analysis was conducted on the entire study base cohort, including patients with known T- and N-stages and with adjustment for these stages. A subsequent analysis with inclusion of patients without recorded T- and N-stages was also performed.The risk of relapse in breast cancer was estimated as hazard ratios (HRs) using Cox proportional hazards models. We included each woman’s time at risk computed from the date of breast cancer until whichever event occurred first; relapse in breast cancer, death or end of the follow-up period. COS was included as a time-dependent variable, i.e. the time before ovarian stimulation was assigned as non-exposure and the time after COS as exposure. We estimated crude and adjusted HRs and 95% CIs. Significance level was set to 5%. In the analyses where T- and N-stages were unknown, the risks of relapse were only given descriptively.The study has been approved by the Regional Ethical Committee at the University of Gothenburg (number 240-15). Informed consent was not required.A total of 5857 women were included in the study base, of whom 337 were exposed to COS. Matching according to set criteria was possible for 334 women, who constituted the control group. A total of 274 women had undergone IVF after completed breast cancer treatment and 63 women (18.7%) had undergone COS for fertility preservation at the time of breast cancer diagnosis. The median time from the date of breast cancer diagnosis to the first COS exposure for those who underwent IVF after breast cancer treatment (n = 274) was 3.38 years ranging from 3 months to 12 years. Number of cycles per patient varied from one (30%) to nine, but only three patients underwent more than five cycles. Stimulation for fertility preservation was started within mean 49 days (range 6–89) from breast cancer diagnosis.P = 0.22). Mean time from breast cancer diagnosis to relapse was 3.87 years (range 10 months to 10 years) in women exposed to COS and 3.50 years (6 months to 10 years) in non-exposed women.Background variables for women being exposed or not being exposed to gonadotrophins in the matched cohort with known T- and N-stages are presented in In the subsequent analysis, also including women with unknown T- and N-stages, relapse occurred in 27 of 337 (8.0%) women having undergone COS and in 71 of 334 (21.3%) among the non-exposed (Supplementary Table SII shows patient characteristics). Nine of 27 women with relapse of breast cancer died, corresponding to 2.7% among women exposed to COS. In the non-exposed group, 39 breast cancer-related deaths occurred (11.7%).P = 0.70). This analysis was adjusted for T- and N-stages  exposed women and in 918 of 3729 (24.6%) non-exposed women . In this analysis, relapse occurred in 27 of 337 (8.0%) women in the exposed group and in 1176 of 5520 (21.3%) in the non-exposed cohort.The coding template for relapse reached a 90% concordance with the Stockholm-Gotland part of the National Breast Cancer Register serving as the reference. The ability of the coding template to correctly identify relapses among ‘true’ relapses was 611/646 (0.95) and to correctly identify individuals without relapse was 1034/1183 (0.87), resulting in a sensitivity of 95% and specificity of 87%.In this large, retrospective, matched cohort study, we evaluated the risk of relapse in breast cancer among women who had been exposed to gonadotrophins either at the time of breast cancer diagnosis or later, after completing breast cancer treatment, in comparison with those without any gonadotrophin exposure. By including both those who underwent fertility preservation and those who underwent a complete IVF treatment, we created a national complete cohort of women exposed to COS and increased the sample size, although the latter group had already been shown to have a reasonable prognosis by the time interval between breast cancer diagnosis and start of COS. Ovarian stimulation with gonadotrophins was not associated with an elevated risk for breast cancer relapse, neither in the primary matched analysis nor in the secondary analysis adjusted for T- and N-stages. These are reassuring results but are probably confounded by selection of a group of women with a more favourable prognosis than those who were not undergoing IVF. Comparing the non-exposed group with the COS exposed group, we noticed a 4-fold increase in breast cancer-related death (11.7% versus 2.7%), which may suggest a selection bias.We tried to counteract selection bias by matching controls for age at breast cancer diagnosis as well as tumour size and lymph node involvement. From baseline data in the entire cohort, it can be assumed that women in the control group had a more advanced cancer with worse prognosis compared with those who have undergone COS, based on the more advanced T-stage and hormonal receptor sensitivity. Thus, only descriptive statistics are presented for comparisons including the entire cohort and no test for significance was conducted.The evaluation of receptor data  was hampered by the poor reporting to the register, since these variables were not included from the start of the register: neither was the breast cancer gene (BRCA)-status part of the register. We cannot be sure whether the prognostic information from receptor status, showing a lower incidence in the exposed group, is representative. In addition, information on T- and N-stages was missing in more than half of the patients, since the register was not established until 2008.There are few earlier reports studying the risk of breast cancer relapse after COS and overall, and there seems to be no support for a negative impact on the oncological prognosis. In an observational study from the USA, the relapse risk in 215 women exposed to COS combined with letrozole was compared with 136 women not undergoing COS. Although no difference in risk of recurrence was demonstrated  ), the study was hampered by a small population  for relapse was 0.59 (95% CI 0.34–1.04) .The strengths of the present study are that data were collected on a national basis as opposed to previous hospital-based cohort studies, resulting in a large cohort, and that data from several registries were combined to control potential confounding. Using national registries gives a low risk of selection bias. By including patients from the time of breast cancer diagnosis until the completion of breast cancer treatment, the results are applicable to women undergoing fertility preservation as well as to those undergoing regular IVF treatment.There are several limitations in this study. One is the substantial degree of missing data on several important prognostic variables, which may be explained by the recent start of both the Swedish Prescribed Drug Register in 2005 and the National Breast Cancer Register in 2008. Data on potential confounding factors, such as BMI and smoking, were not completely covered in the registries. Although demographic factors, such as high BMI and low parity, have been associated with an increased risk of breast cancer and the effect of smoking is unclear, less is known about their effect on relapse.Another limitation is the lack of documented relapses. The coding template resulted in a sensitivity of 95% and a specificity of 87%, considered to be fairly good. It is, however, a limitation that a pre-specified variable for relapse was not in use in the register at that time. Furthermore, the follow-up time from available register data (2005–2014) is rather short.COS as a fertility preservation method or IVF treatment seems to be safe in women with previous breast cancer as no increased risk of relapse in breast cancer was observed in a matched cohort or when adjusted for N- and T-stages. This is reassuring but might be confounded by selection of a group of women with a more favourable prognosis than those not undergoing IVF.Human Reproduction Open online.hoaa061_Supplementary_DataClick here for additional data file.


To analyze the diagnostic accuracy of predictive models of breast cancer risk for the Brazilian population. A cross-sectional, study was conducted in a sample of 382 women aged 35-69 years who were users of the Unified Health System (SUS) residing in a municipality in southern Brazil. The results showed that the Tyrer-Cuzick model had the highest mean risk values and estimates (proportion) for predicting the 5-year risk of breast cancer, reaching a maximum risk of ±1.63% in the 60-64 year age group. For the 90-year risk, a maximum risk of ±12.8% was predicted for the 50-54 year age group using this model. The 5-year risk calculated by the three tools increased progressively with increasing age, where the mean risk was ±0.8% in women aged 35-39 and reached ±1.50% in women aged 65-69. The 90-year risk declined with increasing age only in the Tyrer-Cuzick model, from ±10.8% to ±9%. The BRCAPRO model presented a greater sensitivity compared to the Gail and Tyrer-Cuzick models. And, the model that presented greater specificity was Gail. The Tyrer-Cuzick model presented the highest risk estimates for 5 years and 90 years in the studied population, however, this data is not enough to validate this tool, since when analyzing the sensitivity and specificity the BRCAPRO and Gail have the highest values respectively. Breast cancer is the most common type of cancer in women, accounting for more than 508,000 deaths worldwide . Globally, a woman is diagnosed with breast cancer every 3 minutes, for a total of one million new cases per year . The incidence varies by region, with 27 cases per 100,000 women in Central and East Asia and 96 cases per 100,000 women in Western Europe .In Europe, 28% of new cancer cases are breast cancer. In Brazil, the estimate for the 2018 – 2019 biennium is 59,700 new cases of breast cancer, with an estimated risk of 56.33 cases per 100,000 women. In the state of Paraná, Brazil, the estimated rate for 2018 was 64.70 cases per 100,000 women .Although breast cancer is considered to have a good prognosis, there has been an increase in both its incidence and mortality, which may be associated with diagnoses performed at advanced stages .Screening is an important method of testing or examining an asymptomatic, apparently healthy population to identify lesions suggestive of cancer. It is considered a primary care technology and its indication may vary according to the target population, screening periodicity and method . Studies have shown that in countries with organized screening programmes, the mortality from breast cancer has been reduced . Thus, to organize the screening methods, several risk calculation models have been developed to predict a woman’s risk of developing breast cancer over the lifetime. Among these models, the most used are the Gail, BRCAPRO and Tyrer-Cuzick models .The developed risk calculation tools for rapid screening are easy to use and effective for breast cancer screening . The predictions derived from the models are being used more frequently in the development of guidelines and recommendations for clinical care, in which women with a higher predicted risk are being advised to initiate mammography screening earlier and to consider magnetic resonance imaging (MRI) screening, genetic testing and chemoprevention .The American Cancer Society (ACS) and the National Comprehensive Cancer Network (NCCN) recommend MRI screening in women with a lifetime risk of breast cancer ≥20% as estimated by the Tyrer-Cuzick model, genetic testing in women with a risk for BRCA gene mutation of ≥10% calculated by the BRCAPRO model, and chemoprevention in women with a 5-year risk of breast cancer ≥1.67% calculated by the Gail model .The tools for calculating risk as a screening method are important to classify the risk of the population so that screening methods specific to the population classified as high risk can be used for monitoring them, thus ensuring the early detection of breast cancer and therefore improving the disease prognosis. In this scenario, despite the validation of these screening models in several countries, their applicability differs in specific populations. The Tyrer-Cuzick and BRCAPRO models have not yet been validated in the Brazilian population; that is, there are still no studies evaluating their applicability in this specific population. Furthermore, studies on the Gail model only applied this model to populations who already had breast cancer, and thus cohort studies in the asymptomatic population evaluating the applicability of this model are lacking.In view of the above, the objective of this study was to analyse the agreement between the Gail, Tyrer-Cuzick and BRCAPRO models regarding the obtained risks for breast cancer and to identify the best model for screening in the Brazilian population. Cross-sectional study of a sample of 382 women aged 35 to 69 years. The female population in this age group residing in Paranavaí - Pr, according to IBGE data (2010), is 17,672. After the sample calculation based on the total number, with 5% error and 95% confidence level, a sample is 243 women plus 10% for possible tests, with a final sample of 267 women, who are users of the Unified System. (SUS) in the 13 primary care units of the municipality. Women who previously had or still had breast cancer were excluded.Data were collected between March and September 2018 through a semi-structured instrument that was designed to explore the study objectives. The questionnaire contained 3 sections covering the main topics: (a) Characteristics of the study population such as socioeconomic and demographic factors, including age, educational level, city of residence, self-reported race or ethnicity, family income, and civil status; (b) Behavioural data, including weight, height, smoking, and alcohol consumption; and (c) Reproductive history and family history, including family history of breast cancer, age at menarche, number of children, breastfeeding, onset of menopause, and hormone replacement. The interviews were conducted by the researcher and by individuals trained by him. The data were tabulated in Microsoft Office Excel® 2010 spreadsheets, and the Rx64 software version 3.5.1 was used for statistical analysis. The risk calculations were performed with the CRA Health system , which uses the Gail, Tyrer-Cuzick and BRCAPRO models to calculate the 5-year and lifetime risks. The variables used in the calculation were the same in all tools. Women were considered to have a high lifetime risk for breast cancer if the predicted risk was ≥20% and were considered to have a high 5-year risk if the predicted risk was ≥1.67%.The women were grouped by age into 5-year increments to compare the risks between the tools. The kappa coefficient was calculated to evaluate the level of agreement and reproducibility between the BRCAPRO and Gail, Tyrer-Cuzick and Gail, and BRCAPRO and Tyrer-Cuzick tools for the 5-year and lifetime risk. Analysis of variance (ANOVA) with a post hoc test was used test to the equality between the means of the tools, based on the analysis of the sample variances.The analysis of sensitivity, specificity, positive predictive value and negative predictive value was performed per variable to detect which of the three tools is most appropriate for the study population. The sensitivity test has the diagnostic / screening ability to detect true positives, and the specificity, the diagnostic / screening ability to detect true negatives. While the sensitivity and specificity of a test are inherent properties of the test and do not vary except by technical error, positive predictive and negative predictive values depend on the prevalence of “disease” in the study population. Positive predictive value increases with disease prevalence while negative predictive value decreases . For this analysis was used the software R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: F Foundation for Statistical Computing; 2014.To establish the cut-off point for the studied variables, used a variation of the ROC curve denominated TG-ROC (two-graph receiver operator characteristic), that is an alternative way of express graphically the relationship between sensitivity and specificity to help identify the best cut-off point. . The sensitivity and specificity calculation was performed at the 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 percentiles to adopt the cutoff point.The ethical aspects of the present study are based on the Guidelines and Norms Regulating Research involving Human Subjects , according to Resolution 510/2016 of the National Health Council of Brazil . The study was approved under opinion number 2,251,844 of the Research Ethics Committee of the State University of Maringá .Regarding the risk factors assessed by the three models in this study, which are age, age at menarche, age at first birth, history of cancer in first-degree relatives and race, the age ranged from 35 to 69 years, with an average age of 50 years. The age at menarche ranged from 7 to 22 years, with a mean of 12.8 years, and the age at first birth ranged from 11 to 42 years, with a mean of 22 years. Overall, 15.27% of the women were nulliparous, and 19.4% had at least one first-degree relative  with breast cancer. Regarding race, 40.15% of the women were of mixed race, 44.62% were white, 12.34% were black, 2.62% were Asian and 0.26% were indigenous.The mean 5-year risk for the development of breast cancer  was equaAccording to According to the kappa analysis, the agreement between the tools was significant, with greater agreement between the Tyrer-Cuzick and Gail tools for both the 5-year and 90-year risk. The difference between the means was significant (p≤0.05) between the Tyrer-Cuzick and Gail models and between the BRCAPRO and Tyrer-Cuzick models for the 90-year risk .Regarding the 5-year risk, 37 cases of high risk were predicted with the Tyrer-Cuzick model, but these cases were predicted as low risk with the Gail model; the opposite occurred in 7 observations, where the Gail model resulted in a high risk and the Tyrer-Cuzick model in a low risk. Therefore, the Tyrer-Cuzick model presented greater sensitivity compared to the other tools.When analysing the age-based risk proportion, the 5-year risk of breast cancer increased with increasing age group in all three tools . The TyrThe 90-year risk of breast cancer decreased with increasing age according to the Tyrer-Cuzick model, but the same did not occur with the Gail model, as the risk remained high. The BRCAPRO model did not estimate any women as having high risk .The The Tyrer-Cuzick tool obtained higher Positive Predictive Value (PPV) for the group of women who never breastfed (34.1%) and also had the highest overall PPV, compared to the other models. The negative predictive value (NPV) was higher in the group of women who underwent screening exams for breast cancer (87.5%) and was calculated by the BRCAPRO tool, and the group of women who did not breastfeed (87.5%) was calculates by Gail tool. When analyzing the NPV in general, the Gail model presented a higher mean compared to the other models.The cut-off point of variables analyzed as the woman’s age, body mass index and the woman’s age at the birth of the first child are presented in Tools for assessing risk as a screening method are important for classifying the population risk for breast cancer and for monitoring the population classified as high risk through other screening methods. Thus, models should be used that ensure the early detection of breast cancer in its initial stage, thereby improving the prognosis of the disease and the quality of life of women. Although validation of these screening models has been conducted in several countries, their applicability differs in specific populations. To date, despite the high rates of mortality due to breast cancer in the Brazilian population, there are still no correlation studies of these models and their effectiveness in screening for breast cancer. The Tyrer-Cuzick and BRCAPRO models have not yet been validated in the Brazilian population; there are no studies evaluating their applicability in this specific population. Studies on the Gail model have applied it only to populations already with the disease, and there are no cohort studies in an asymptomatic population evaluating the applicability of this model.The estimated risk of breast cancer varied between the risk models. This is because each model uses a different statistical calculation for the same analysed variables. Additionally, each model has different risk factor variables for the statistical calculation. Thus, identifying the best risk model for the Brazilian population is important to provide specific recommendations according to the risk found and the age of the patient. According to the NCCN guidelines (2016), a risk equal to or greater than 20% calculated by a validated risk tool is an indication for MRI breast cancer screening. A risk equal to or greater than 1.67% is a criterion for the indication of chemoprevention.The BRCAPRO model obtained the highest mean risk (24.84%) for calculating breast cancer risk in 5 years. For the 90-year risk calculation, the Tyrer-Cuzick tool had a higher mean risk (6.7%). In the study by Coopey et al., (2018), the Tyrer-Cuzick tool presented the highest estimates of risk for breast cancer throughout life in all age groups. In a study realized out by Weisstock et al. (2013), about risk assessment used the model of Gail and Tyrer-Cuzick, the mean overall risk was higher for the Gail tool (2.86%) than for Tyrer-Cuzick (2.63%).When analyzing risk estimates by age group, the Tyrer-Cuzick model calculated the highest risk estimates for breast cancer in 5 years (≥ 1.67%), and for the risk in up to 90 years (≥ 20%) in most of the age groups and also presented the highest number of observations, according with analysis of kappa. Brentnall et al. (2015) also evaluated these models in the United Kingdom with a cohort study and observed a higher diagnostic value of the Tyrer-Cuzick model in comparison to the Gail model, reinforcing the results of the present study. Ewans et al., (2016) compared the Tyrer-Cuzick, Gail and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models and found greater risk accuracy for the Tyrer-Cuzick model in a cohort of 6,268 women in the United Kingdom.The 5-year risk calculated for this population by the three tools increased progressively with the age of the women, and the 90-year risk decreased with age when using the Tyrer-Cuzick tool. Using these models, Coopey et al., (2018) found a similar result in the American population, where the calculated lifetime risk of breast cancer decreased with patient age.This study also showed that the BRCAPRO tool presented a sensitivity in the studied sample with a mean of 46.4% among all variables analyzed. The specificity, positive predictive value and negative predictive value were higher in the Gail tool with the respective values 58.9%, 23.4% and 84.1%. These results diverge from the study by Zhang et al. (201.8), which showed that the Tyrer-Cuzick model presented higher sensitivity (66%), specificity (86.92%) and positive predictive value (85.34%) for breast cancer risks, compared to the Gail model with 53.33%, 77.69% and 73.39 % respectively. In another study, conducted in India by Challa et al., (2013) showed a sensitivity and specificity of the Gail model closer to the result of the present study, in which the sensitivity was 51.9% and the specificity 64%, however, concluded that from these data it is still not possible to validate this tool in the Indian population, thus requiring more studies with a larger population.The Gail model is the most validated model worldwide, but in Brazil its applicability remains questionable, since the most recent studies of this tool did not use a retrospective cohort to evaluate its performance. In the study by Lopes et al., (2014), which evaluated 105 women with breast cancer in Minas Gerais, the 5-year risk of breast cancer was underestimated. In a case-control study with women from the Brazilian state of Bahia, the 5-year risk was underestimated in the control group compared to the case group . However, the Gail model is considered to predict risk in asymptomatic women; thus, the applicability of the Gail model in the Brazilian population is questionable, since no cohort study has evaluated risk in this population after years of monitoring.A limitation of this study is that it was performed in a small sample of the Brazilian population, and the variables used by the three tools were the same, thus reducing the accuracy of the risk prediction by tools that have more specific risk variables for the statistical calculation. However, when analysing the same variables, the tools performed differently. The BRCAPRO model demonstrated a greater sensitivity for the prediction of breast cancer risk in Brazilian women compared to the Gail and Tyrer-Cuzick models.In Brazil, the use of these risk prediction tools remains limited due to the lack of knowledge of the existence of these tools by some professionals, the culture of professionals in regard to resisting new technologies and especially the reliability of the results obtained, since there is still no model that evaluates the specific risk factors of this population, and the applicability of the existing models has not yet been verified through cohort studies.In conclusion, the Tyrer-Cuzick model presented the highest risk proportion for both 5 years and 90 years in the studied population. However, this data is not enough to validate this tool, since that when analized the sensitivity, specificity, PPV and NPV, the BRCAPRO and Gail models present the highest values, respectively. It was also concluded that when the diagnostic accuracy of these three tests were determined, they were not sensitive enough to detect risk when there was actually risk, but they showed a greater capacity to determine absence of risk when the result was negative. Therefore, future studies are necessary to evaluate the applicability of these models in this population and to determine which tool is most appropriate considering the risk factors of Brazilian women, or even the development of specific models for this population.


The association between obesity and lung cancer (LC) remains poorly understood. However, other indices of obesity on the basis of body shape instead of body size have not been examined yet. The aim of this study was to evaluate the association between different indices of body size and body shape and the risk of LC. In particular, this study examined the association between A Body Shape Index, a more precise indicator of abdominal fat than traditional anthropometric measures, and the risk of LC.In the prospective cohort the Rotterdam Study, we analysed data of 9,689 participants. LC diagnoses were based on medical records and anthropometric measurements were assessed at baseline. Cox-regression analyses with corresponding Hazard Ratios were used to examine the association between the anthropometric measurements and the risk of LC with adjustment for potential confounders. Potential non-linear associations were explored with cubic splines using the Likelihood ratio (LR) test.During follow-up, 319 participants developed LC. Body mass Index (BMI) was inversely associated with the risk of lung cancer  and persisted after excluding lung cancer cases during the first 10 years of follow-up. There was evidence for a non-linear association between BMI and the risk of lung cancer , which indicated that the inverse association between BMI and lung cancer was mainly present in non-obese participants. Waist circumference (WC) (HR 1.03 95% CI: 1.01–1.05), Waist-to-Hip Ratio (WHR) (HR 1.23 95% CI: 1.09–1.38) and ABSI (A Body Shape Index) (HR 1.17 95% CI: 1.05–1.30) were positively and linearly associated with the risk of lung cancer.Body shape rather than body size may be an important risk indicator of LC. Future research should focus on the role of visceral fat and the risk of LC as well as the underlying mechanisms. Lung cancer is currently the main cause of cancer mortality worldwide . In 2018Several indices of nutritional status exist. The most commonly used method is Body Mass Index (BMI) due to its simplicity of measuring and low cost . SeveralIn contrast to overall obesity and body size, as measured by BMI, specifically abdominal obesity may have a more important role in lung cancer development , 21–23. A newly developed anthropometric measurement is A Body Shape Index (ABSI), combining WC to BMI and weight into one formula . StudiesThe objective of this study was to evaluate the different indices of body size (i.e. BMI) and body shape  and the risk of lung cancer in a population-based prospective cohort study.Data for this prospective cohort study was derived from three sub cohorts from the ongoing prospective Rotterdam Study . The RotThe population in this study consisted of participants from the first three cohorts that started in 1990 (RS-I), 2000 (RS-II), and in 2006 (RS-III). By 2008, the total number of participants in the Rotterdam Study at baseline consisted of 14,926 participants aged 45 or over. In this study we excluded participants without all investigated anthropometric or lifestyle measurements at baseline from the initial cohort . Therefore, the number of participants at baseline was equal to 9,689. All participating women were postmenopausal.The primary outcome of interest was incident lung cancer. Two research physicians independently assessed the diagnosis of cancer based on medical records obtained through general practitioners and hospital discharge letters. Additional information was collected through linkage with the Dutch Hospital Data, National Cancer Registry, and Dutch pathology database (PALGA). Only cases confirmed by pathology were used. Cases of mesothelioma were not included in the definition of cancer for these analyses. Lung cancer was classified according to the International Classification of Diseases tenth edition . In case of discrepancy, consensus was obtained through consultation with a cancer epidemiologist. Follow-up of cancer registration was completed up to January 1, 2014.The anthropometric measurements Body Mass Index (BMI), Waist circumference (WC), Waist-to-Hip Ratio (WHR), and A Body Shape Index (ABSI) were measured in the research centre by trained staff during regular on site visits every 3–4 years.2) was calculated by dividing the weight (kg) by the squared height (m2). Height and weight were measured without shoes and heavy outerwear, using calibrated SECA-measurement scales. BMI was categorised as normal, overweight, and obese with the corresponding cut offs <25, 25–30, and >30 kg/m², respectively (BMI (kg/mectively . Since tWC (cm) was measured with a flexible measuring tape at the level halfway between the lower rib margin and the iliac crest in standing position, without heavy outerwear, empty pockets, and calm exhaling. According to recommendations of the WHO , WC was categorised as normal, increased, and substantially increased with the corresponding cut offs <80, 80–88, and >88 cm, respectively, in females and <94, 94–102, and >102 cm, respectively, in males . WHR was defined as the WC (in cm) divided by the Hip Circumference (HC). HC (in cm) was measured as the maximum circumference over the nates. According to recommendations of the WHO, WHR was categorised as normal, increased, and substantially increased with the corresponding cut offs <0.80, 0.80–0.85, and >0.85 respectively, in females and <0.95, 0.95–1.00 and >1.00, respectively, in males . We stan2/3×height1/2) (ABSI (A Body Shape Index) was calculated as WC/(BMIight1/2) . To compCovariates of interest were derived from the existing literature and measured at baseline. We considered the following covariates as important due to their relation with the anthropometric measurements and the risk of lung cancer: age; employment; highest educational level; marital status; sex; ever diagnosed for cancer; smoking status; pack years; physical activity; WCRF/AICR (World Cancer research Fund/American Institute for Cancer Research) adherence score; alcohol consumption.Paid employment (yes/no), educational level , marital status, (partner/no partner) and sex (men/women) ever diagnosed for cancer (yes/no) and smoking status , together with the corresponding cigarettes in pack years (continuously), were self-reported during the home interviews. Physical activity , 31 was We used Cox proportional hazards regression to analyse the association between BMI, WC, WHR, and ABSI values with the risk of lung cancer. Both continuous and categorical analyses were performed. For all categorical variables, we used the lowest category as reference category. Results of the Cox-regression analyses were presented as hazard ratios (HRs) with the corresponding 95% confidence intervals (CI). The underlying time axis of the Cox-regression model was the follow-up time, starting at baseline until the first diagnosis of lung cancer, death, or end of the study period, whichever came first. Results of the Cox-regression analysis were presented in a crude model (model 1) which included the association between the main determinant and the outcome variable, adjusted for sex and age, as well as a multivariable model, adjusting the association for all  confounders at baseline (model 2). Furthermore, we used the Wald-test to calculate the overall p-for trend for the categorical variables. We added an interaction term between the covariates and the underlying follow-up time to test for the proportional hazard assumption. In addition to the baseline measurements, we analysed the mean differences between baseline and follow-up of BMI, WC, WHR, and ABSI in relation to the risk of lung cancer to examine whether the association might change over time. Within these associations, the baseline measurement of each anthropometric measurement was added to account for potential regression to the mean . These mWe tested for effect modification by sex (men/women), smoking status , and pack years (continuous). We added an interaction term between the main determinant(s) and sex (determinant*sex) in an unadjusted Cox-regression model. Whenever the interaction term was significant, the results were presented categorized by sex, smoking status or stratified by the median of pack years.In this study, we performed two sensitivity analyses. First, we systemically excluded lung cancer cases within the first 5, 7.5, and 10 years of follow-up to examine whether reverse causation could influence the association between BMI at baseline and the risk of lung cancer. Second, to assess the role of residual confounding by smoking, the association between BMI and the risk of lung cancer was stratified for never, ever and, current smokers.The possibility of non-linear associations between the anthropometric measurements BMI, WC, WHR and, ABSI with the risk of lung cancer were assessed by using data driven natural cubic splines as suggested by Durrleman et al. . DegreesAll missing values, except for the outcome variable, were imputed by using the Markov chain Monte Carlo (MCMC) method. After the imputation procedure, the resulting ten datasets were pooled and an average of the imputed data was calculated. The pooled data was used to conduct all analyses in this study. In this study, p-values less than 0.05 (two-sided) were considered as statistically significant. For the non-linear Likelihood-ratio (LR) test we used R-statistics version 4.4.3. In all further statistical analyses we used Statistical Product and Service Solutions (SPSS) version 24.0.2 increase of BMI was associated with a reduced risk of lung cancer, after adjustment for sex and age  and after full adjustment for sex, age, cohort, physical activity, alcohol intake, pack-years, education, WCRF adherence score, and diabetes mellitus . This corresponding inverse trend was also observed for the normal BMI group  compared with the high and obese group, after adjustment for sex and age (High: HR 0.76 95% CI: 0.60–0.96) (Obese: HR 0.68 95% CI: 0.47–0.99) and in the fully adjusted model (High: HR 0.72 95% CI: 0.56–0.91) (Obese: HR 0.61 95% CI: 0.41–0.89). Furthermore, 1 cm of WC (HR 1.03 95% CI: 1.01–1.06) and one standard deviation increase of WHR (HR 1.23 95% CI: 1.09–1.38) and ABSI (HR 1.17 95% CI: 1.05–1.30) were positively associated with a 3%, 23%, and 17% higher risk of lung cancer respectively, after full adjustment for confounders. Similar results were observed when WHR  and ABSI  were analyzed categorically.As shown in 2, 95% CI: -0.62 – -0.53), WC , and WHR (-0.016 95% CI: -0.019 – -0.014) decreased, whereas the ABSI mean (0.0350 95% CI: 0.0348 – 0.0352) increased. Within a mean follow-up of 11.6 years from baseline until the next follow up measurement, in which 173 lung cancer cases were identified, the mean BMI (-0.58 kg/m(data not shown). shows the relation between 1-unit increase of BMI, WC, WHR, and ABSI with the risk of lung cancer for 9689 participants, including 319 lung cancer cases. None of the Likelihood-ratio (LR) tests between WC , WHR  or ABSI  and the risk of lung cancer suggested a non-linear relationship. In contrast, the LR test between BMI  and the risk of lung cancer did suggest a non-linear relationship. More specifically, the inverse association between BMI and lung cancer risk was only linear in non-obese participants whereas the curve flattened in obese participants.In all Cox regression models, there was no significant interaction between follow-up time and BMI, WC, WHR, and ABSI. Therefore, the proportional hazard assumption was met for all associations. No significant interactions were observed between BMI, WC, WHR, and ABSI with smoking status and sex (data not shown). Furthermore, no significant interactions were found for BMI, WC, WHR, and ABSI with pack years of smoking In this prospective cohort study, we found that BMI was inversely associated with the risk of lung cancer. In contrast to BMI, an increased WC, WHR, and ABSI were associated with a higher risk of lung cancer. Additionally, we found evidence regarding a non-linear association between BMI and the risk of lung cancer, which indicated that the inverse association between BMI and lung cancer risk was only present in non-obese participants, whereas an increase in WC, WHR, and, ABSI showed a linearly higher risk of lung cancer.Our results showed that a higher BMI was associated with a reduced risk of lung cancer which is in line with two meta-analyses , 37 and An explanation for the inverse association between BMI and the risk of lung cancer in those without obesity may come from residual confounding by smoking. In the current study we found no association between BMI and the risk of lung cancer among never, ever, or current smokers. Several other studies which stratified the association for smoking found similar results –19. HoweAn alternative biological explanation for the inverse association between BMI and the risk of lung cancer can be the storage of common environmental genotoxicants in fat tissue. Lipophilic genotoxicants such as polycyclic aromatic hydrocarbons (PAHs) that are derived from smoking or occupational exposure influence the development of lung cancer through DNA damage . PreviouOur study found that WC and WHR were positively associated with the risk of lung cancer, consistent with other studies , 21. HowThe findings is this study showed that ABSI was positively associated with the risk of lung cancer. Although this is the first study that examined the association between ABSI and the risk of lung cancer, another study among 200 adults with overweight found an inverse association between ABSI and FFMI (Fat Free Mass Index) , indicat2/year) of BMI, was associated with a reduced risk of lung cancer in women .The datasets presented in this article are not readily available because sharing of individual participant data was not included in the informed consent of the study, and there is a potential risk of revealing participants' identities. Requests to access the datasets should be directed to data manager Frank J.A. van Rooij ; and the Municipality of Rotterdam. The funders played no role in the study design or in data collection and analysis. The authors had final responsibility for design and conduct of the study, collection, management, analysis, and interpretation of the data, and preparation, review or approval of the manuscript.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.


Accordingly, this study aimed to assess the frequency of miR-146a G/C (rs2910164) polymorphism and its association with susceptibility to breast cancer in Iranian women. MicroRNAs (miRNAs), short regulatory RNAs, function as negative regulators able to modulate gene expression. Just as other genetic variant, single nucleotide polymorphisms (SNPs) in We conducted a case-control study using Tetra ARMS polymerase chain reaction (Tetra ARMS PCR) method in 100 Iranian female participants (50 breast cancer patients and 50 controls). Besides, a number of sequenced samples were chosen to confirm the accuracy of genotyping by Tetra ARMA PCR. SPSS software was utilized for all statistical analyses. The odds ratios (ORs) and 95% confidence intervals (95% CIs) were applied to analyze the association between the SNP frequency and breast cancer. miR-146a G/C (rs2910164) polymorphism and breast cancer, either at genotype or allele levels (P>0.05). F-SNP-based in silico analysis indicated possible modifications in transcriptional regulations induced by miR-146a G/C (rs2910164) variations. The frequency of genotypes for G/G, G/C, and C/C were 23 (46%), 26 (52%), and 1 (2%) among cases and 15 (30%), 33 (66%), and 2(4%) among controls, respectively. The results generated by the groups did not show any significant correlation between Overall, our results indicated no correlation between miR-146a G/C (rs2910164) polymorphism and genetic susceptibility to breast cancer in Iranian female populations. However, these findings need to be further confirmed by analyses of a larger number of cases. BRCA1/2 genes are responsible for an average risk of 16–20% for hereditary breast cancer. Recent research, however, has produced evidences to support the role microRNAs (miRNAs) play in development and progression of breast cancer . The human genome encodes more than 1,000 miRNAs . They are noncoding, short , and evolutionarily conserved, RNA molecules that exert post-transcriptional regulation as they bind to complementary sequences in 3′-untranslated region (3′ UTR) or 5′-untranslated region (5′UTR) of target messenger RNAs (mRNAs) . It is estimated that around 30% of human genes are regulated by miRNAs . The genetic variants of a miRNA could affect its biogenesis including its maturation, and is likely to have a role in the pathogenesis of many diseases, such as cancer . As almost 50% of genes encoding miRNA happen to be in cancer-related chromosomal regions, the latest studies have investigated their potential use as cancer susceptibility and prognosis markers . For instance, the result of a study  showed that there were 39 interactions between BRCA1 mRNA and microRNA-146a, introducing this miRNA as a negative regulator of BRCA1. Among different variations in human genome, single-nucleotide polymorphisms (SNPs) are the most prevalent . In the case of miRNAs, such SNPs might change their expression, lead to maturation of aberrant miRNA, and affect the target binding affinity and specificity of transcription . Therefore, they can be potentially linked to human malignancies. As breast cancer is the most reported one among females, it has become a major area of concern for studies linking miRNAs and human cancers . In this regard, numerous epidemiological studies evaluated the association between cancer susceptibility and miRNA SNPs . Furthermore, several meta-analyses case-control studies carried out on the association focused on breast cancer in different populations . For example, the human miR-146a gene at locus 5q34 was linked with BRCA1/BRCA2 activity. The SNP rs2910164:G>C, located in the middle of the miRNA stem hairpin, was reported to cause an alteration in the expression of this miRNA, since it replaces a G:U pair with a C:U mismatch in the stem of the miRNA precursor . This SNP was shown to increase the risk of some human cancers including carcinoma of the breast, urinary bladder, cervical, head and neck, gastric, and lung . Specifically, several molecular studies were conducted on the link between the miR-146a rs2910164 polymorphism and the increased/decreased risk of breast cancer . Here, we analyzed the association between miR-146a rs2910164 G>C polymorphism and breast cancer susceptibility in Iranian females population by means of Tetra ARMS PCR. Breast cancer is the most prevalent cancer among women worldwide and the leading cause of cancer-related death in less-developed countries . Along with the environmental factors and lifestyle that potentially increase the risk of breast cancer, there are genetic factors implicated in the pathogenesis of the disease. The recent discovery of genetic alterations involved in the disease has attracted undivided attention in breast cancer research. Thus far, many of such variants have been identified with potential associations to the risk of breast cancer . By way of illustration, the mutations in theParticipantsIn this case-control study, we recruited 50 patients with histologically confirmed breast cancer and 50 healthy controls. The controls were frequency-matched to patient cases in terms of age, gender, as well as ethnicity. We used medical records and cancer registry data to confirm self-reported cases of breast cancer, and to gain information on tumor characteristics. The cases and the controls were unrelated, yet of similar ethnicity in Iran. The study was approved by Islamic Azad University Ethics Committee (Cod No. 15730503961004) and the authors followed the norms of the World’s Association Declaration of Helsinki. The study methods were carried out according to approved guidelines. Informed consent was obtained from all the participants. The sample size of the case and control groups was calculated with standardized Cochrane formula (as below): n=sample sizeN=population sizeP=proportion of populationq=1-pz=1.96d=margin of error (0.05)DNA Extraction and GenotypingTo meet the sampling requirements, about 3 mL of venous blood was collected from each participant. The blood samples were collected in tubes containing ethylene diamine tetra acetic acid (EDTA). Subsequently, the samples were centrifuged at 8,000g for 180s at room temperature. After centrifugation, the plasmas were stored at 80°C. Genomic DNA from each sample was isolated from the leucocytes of the peripheral blood using a DNA Purification Kit  according to the manufacturer’s instructions. Genotyping of miR-146a rs2910164 G/C polymorphism was performed through Tetra ARMS PCR. As presented in SequencingIn order to confirm the PCR genotyping results, some samples were randomly chosen and sequenced by Bioneer Corporation (Korea).Statistical and Bioinformatics AnalysisStatistical analysis was performed by SPSS version 24. Descriptive statistics of patients and controls were presented as the mean and standard deviations for continuous measures, and frequencies and percentages for categorical measures. A chi-square test was run to calculate the additive combined effects of the risk alleles G and C. The odd ratios (Ors) with their corresponding 95% CIs  were calculated to analyze the associations of polymorphism rs2910164 in miR-146 with breast cancer susceptibility and testing the Hardy-Weinberg Equilibrium (HWE). The cut-off p-value for the association was <0.05. Bioinformatics analysis was performed by bioinformatics tools FAST-SNP (http://fastsnp.ibms.sinica.edu.tw) and F-SNP (http://compbio.cs.queensu.ca/F-SNP/).Characteristic Profile of the ParticipantsThe average age for case (50 breast cancer patients) and control (50 controls) groups were 49.09±11.02 and 48.80±8.28 years, respectively . We founTetra ARMS PCR Genotyping ResultsAll products of Tetra ARMS PCR were analyzed using agarose gel electrophoresis (1.5%), stained by ethidium bromide and visualized on UV transilluminatore .Between Group Comparison of the Association of miR-146a G/C (rs2910164) Polymorphism χ2 test indicated the lack of any significant difference between genotype frequencies of the two settings (patients vs. control) (P>0.05). A logistic regression test was used in order to examine odds ratio for different genotypes. In addition, different patterns of inheritance  were analyzed as well. Dominant model was the only pattern for which the odds ratio was significant (CI95%) ( (CI95%) . Patient (CI95%) .Co-relation of microRNA Polymorphism miR-146a (rs2910164) G>C with Clinical Characteristics (tumor stage) in Breast Cancer PatientsWhen segregated in accordance with the tumor stage, breast cancer patients did not show any significant association of all selected variants of miR-146a (rs2910164) G>C (P>0.05).Sequencing Result miR-146a gene rs2910164 SNP are shown in Sequencing results were analyzed by the sequencer v5.4.6 and then were blasted using National Center for Biotechnology Information (NCBI) BLAST tool. Genotyping results by sequencing were identical to the Tetra ARMS PCR results. Sequencing result for mutant genotype ofBioinformatic AnalysisThe Silico analysis by Golden Plath prediction Tool (http://genome.ucsc.edu/) (F-SNP and FAST-SNP) indicated the possible effect of miR-146a G/C (rs2910164) variation on transcriptional regulation  .MiRNAs, a class of endogenous, are non-coding, single-stranded RNAs involved in many biological processes, such as development, apoptosis, differentiation, proliferation, immune response and regulation of gene expression in post translation levels . Many miRNAs have been implicated in various human diseases, and it has been suggested that miRNAs are aberrantly expressed or mutated in many types of cancers . The most frequent variations in human genome; i.e. polymorphisms in miRNA, can be altred to different miRNA biology pathways including expression, processing, and maturation, as well as attraction and specificity for target binding . Therefore, many epidemiological studies have focused on the correlation between miRNA polymorphisms and susceptibility to the most common tumors in women, i.e. breast cancer . Accordingly, there has been an association documented between rs2910164 polymorphism in miRNA146a and the increased risk of several types of human cancers including papillary thyroid cancer, hepatocellular cancer, esophageal, squamous cell cancer, gastric cancer, prostate cancer, and glioma . As demonstrated by some studies, this SNP could be related with breast cancer susceptibility . This SNP is present on the 3′ UTR strand of the miRNA and triggers a G to C change, leading to an alteration from a G: U pair to a C: U mismatch, which is located on the stem structure of the miRNA precursor. This encourages an alteration in miR-146a expression and thus could influence the risk of cancer . In this study, we investigated miR-146a rs2910164 SNP in 50 cases and 50 controls. In our study, we found that the Iranian population data is different with other populations. As with other research projects, the results of our study found ethnicity as a source of discrepancy between the data on the Iranian and other populations . We did not find any relation between this polymorphism and risk of breast cancer in Iranian women. Our findings were in agreement with those of some other studies . However, a study by Lian et al., (2012) explained that CC genotype of this SNP was able to increase the risk of breast cancer in European populations. Shen et al., (2008) reported the first case of correlation between this SNP and breast cancer, in terms of a correlation between rs2910164 and the patients with familial breast/ovarian cancer. In addition, Pastrello et al., (2010) reported rs2910164 to have a potential impact on the breast cancer in an Italian population. Bansal et al., (2014) found that the rs2910164 G/G polymorphism was associated with reduced genetic susceptibility to breast cancer in a population from North India. There was also a group of postmenopausal breast cancer patients reported without any familial history of the disease, where some genotypes of rs2910164 in miR-146a were correlated to an increased risk of breast cancer in a Chinese population . Between 2011 and 2017, several meta-analyses trading were published with rs2910164 G>C polymorphism were published . Among them, two studies reported rs2910164 SNP is related to breast cancer susceptibility in a Caucasian population . The other five studies showed that rs2910164 was not correlated to breast cancer susceptibility in both Caucasian and Asian populations . In another meta-analysis, Lian et al., (2012) initiated a significant correlation between the rs2910164 CC genotype and the risk of breast cancer in European populations. In contrast, a research by Hu et al., (2009) did not indicate an association between this SNP and the risk of breast cancer in Chinese women. More recently, two studies were dedicated to the relationship of this polymorphism and the breast cancer among different ethnicities of the Iranian population . The former analyzed the association of miRNAs 146aG/C (rs2910164) polymorphism with the risk of breast cancer between case and control groups of Azeri ethnicity women in Iran. Their analysis, similar to our research, showed no significant relationship between miRNAs 146aG/C (rs2910164) polymorphism and breast cancer susceptibility in Azeri ethnicity in Iran . The latter, however, indicated that the CC homozygous genotype of miR-146a (rs2910164) was seen in 45 (12.7%) patients with breast cancer and could be related to breast cancer in Rasht ethnicity in Iran . What mentioned above simply indicates that the genetic background could have a deep impact upon oncogenic mechanisms across populations. Therefore, further studies in larger populations including different ethnicities, and other genetic and environmental factors, such as genetic markers, assortment and socioeconomic sections, are required to achieve a definitive conclusion concerning the probable relationship of this polymorphism with breast cancer in any populations including the Iranian one. Nevertheless, what may merit attention to review the limitations and strengths of our study. The very first point is that while genetic associated studies concerning common diseases normally enjoy large datasets, small p values and independent replication for more reliable results the study involved a very small number of cases and controls. When sufficiently large (n>=30), the sample statistically benefits from a normally distributed sample mean that allows a z test to be run for a single mean. Yet, a small sample size (n<30) calls for running a t-test to compensate for the absence of a normal distribution.The second area of concern with association studies is if the design allows the researchers to replicate the tests. This ultimately ensures the accuracy of the results and enhances the degree of significance. In order for the results to be reliable, it is essential to pay meticulous attention to factors like confounding population structure, misclassification of outcome, allelic heterogeneity and gene-disease associations that might in theory limit the application of this approach. Another serious problem with association studies is that population structure can lead to false associations between a candidate marker and a phenotype. Although for a study of associations between genotype and the risk of a disease, the case-controls are created to the cohort the design, cohort studies have been recommended because they can be used to study gene-environment interactions. Despite the pertinent scientific relevance of its statistical interaction, there is a very considerable disadvantage in using the case-control design susceptibility to bias when estimating effects of exposures that are measured retrospectively. The pitfall is that such a process does not necessarily apply to the cases where the investigation targets the statistical interaction between genotype and environmental exposure. According to the latest designs run for genetic association studies, it is highly recommended to rely on randomized sampling in order to compare the relation between genotypes (heterozygote and homozygotes) and environmental risk factors. To rely on random selection or to choose a cohort design are other measures to increase the reliability of the results in genetic experiments with a small sample size. The reason is that such measures could substantially control both the impact of environmental factors on the disease related genes and the sensitivity of other disruptive factors. These, in return, minimize the risk of error and significantly compare the interactions between genotypes and environmental risk factors. In brief, randomized sampling (of both the cases and controls) will assist to achieve adequate statistical power  required to detect the factors that might have an impact on the above mentioned relation, particularly when the risk factors are unknown. The randomized sampling is as valuable as the cohort design. Subsequently, this study enjoyed a randomized sampling method and a cohort design. Next comes the discussion concerning the significance of the p value for Hardy Weinberg equilibrium test. The results of our study demonstrated that both control and case groups were within Hardy-Weinberg equilibrium (P<0.05); this also could be explained by the small sample size. In this regard, it has been suggested that the gene associated with human disease show enormous variation  in their allelic spectra. For some genes, there are a few predominant disease alleles, whereas others have a wide range of disease alleles, each relatively rare. The allelic spectrum is important, disease genes with only a few deleterious alleles can be more readily identified and are more amenable to clinical testing. It has become increasingly apparent that the identification of true genetic associations in common multifactorial disease calls for studies comprising thousands rather than the hundreds of individuals employed so thus. When the sample size is small, it can be difficult to see if there is any difference between the sample mean and the population mean, because the amounting degrees of sampling variability act as a source of confusion. Thus, it is highly recommended to resolve this matter before the standard error could be calculated, and the standard deviation could be divided by the sample size. Besides, when the sample size is small, it is desirable to consider the probabilities of both type I and type II errors as this will allow to use a larger alpha for the purpose of justification. With a small sample, the probability of a type II error with the standard alpha of 0.05 may be too high, and might act in a way appropriate to an occasion when in the face of a p value greater than 0.05, the null hypothesis is false. However, when the p value is greater than 0.05, there is less evidence for the false null hypothesis than the time there is a smaller p value with a larger sample. Here, the rationale is that a p value greater than 0.05 with a small sample is just strong evidence against the null hypothesis as a smaller p value with a big sample. Moreover, when there is only a minimum sample size, regardless of total number of population, one can consider using t statistic with the appropriate degrees of freedom as reliable means of performing advanced statistical tests. This approach is efficient even at presence of a tiny sample size . In addition, restricting the statistical test results to 95% confidence interval enables researchers to generalize their results to the complete population with a similar range to that of the total population. Furthermore, mathematical likelihood , shows that the p value substantially overstates the evidence against the null hypothesis. The likelihood depicts the distinction between error rates and inferential evidence more clearly and as a quantitative tool for expressing the evidential strength, it outperforms the p value for the purposes of epidemiological purposes.Finally, as breast cancer is a heterogeneous disease, we had to thoroughly investigate any potential gene-gene interaction and gene-environment interactions. As indicated by a large number of researchers in the area of breast cancer, variation in the frequency of allelic variants of human genes, can affect not only the risk itself, but the assessment of the associated risk scales as well. Similarly, some studies focused on genotype prevalence and gene disease associations, where these focuses of research was on sample selection, analytic validity of genotyping, population stratification, and statistical issues. The growing body of research on genetic markers as well as the development of more sophisticated tools for genotyping have recently made a considerable contribution to proliferation of association studies. Therefore, the time has come to critically reconsider the design of such studies, in order to avoid the mistakes of the past and maximize their potential to identify new components of diseases. Metabolic and signaling pathways that regulate hormone action are two potential aspects of the breast cancer, demanding undivided attention. When perturbed, they simply alter their outputs of matter and energy which, depending on the environmental context, can produce either a pathological or a normal phenotype. To investigate the dynamics of these pathways through approaches like metabolic control analysis may provide new insights into the pathogenesis and treatment of complex diseases. One of this control mechanisms, is microRNAs. Variations and changing expressions of microRNAs (miRNAs), could bring about multiple changes in the expression of tumorigenesis promoting genes that ultimately increase the risk of breast cancers. Recent data on breast cancer-specific miRNA polymorphisms and expression profiles and their participation in regulating invasive processes, illustrated that they were associated with changes in cytoskeletal structure, cell-cell adhesion junctions, cancer cell-extracellular matrix interactions, tumor microenvironments, epithelial-to-mesenchymal transitions and cancer cell stem abilities. Besides, breast cancer invasiveness are shown to be strongly triggered by epigenetic regulation of individual miRNAs and their modified interactions with other regulatory genes, and with the functions of miRNA isoforms and exosome-mediated miRNA. Research into miRNA’s function in breast cancer has just begun and there is still a long way to go. This has roots in two important issues: First, most genotypes that contribute to the constellation of necessary genes are uncommon and hard to recognize. Second, although more common genotypes may confer susceptibility, they are weak predictors of particular diseases such as breast cancer. Consequently, more attention must be inevitably paid to environmental risk factors. For this reason, more important than conducting a genetic search, is acquiring the know ledge about the relations among environmental / genetic factors and other interactions. For example, obesity–insulin connections have been considered as potential risk factors for postmenopausal breast cancer, and the association between insulin resistances (IR) genotypes and phenotypes can be modified by obesity-lifestyle factors, affecting breast cancer risk. Recently, more and more studies, have confirmed accurate predictions of breast cancer and indicated potential intervention strategies for women with specific genetic and lifestyle factors  to correlate them with breast cancer risk. In this study, the results indicated that there was relation between older age and breast cancer risk in Iranian female. Finally, multiple comparisons are commonly made in genetic association research, yet how to appropriately change for multiple comparisons remains a controversial issue. However, that large increases in the number of comparisons has a limited effect on the sample size required to maintain an experiment level.In summary, our results suggested that the rs2910164 (G > C) polymorphism in miR-146a may not be associated with increased susceptibility to breast cancer and could not be a biomarker to predict the breast cancer risk and potential therapeutic targets in the studied population. However, the mechanisms essential for this association need to be further discovered. Further studies need to focus on the role of genetic variants of miRNA in breast cancer and inclusion of diverse ethnic groups and larger sample sizes in order to confirm the role of rs2910164 polymorphism in miR-146a with breast cancer pathobiology.


Early diagnosis of breast cancer is essential for mitigating its related morbidity and mortality. Therefore, high awareness is required.We aimed to evaluate the knowledge, awareness and perceived barriers among females in Bangladesh regarding breast cancer.A hospital-based survey was performed from April 2019 to June 2019. A total of 500 females aged >18 years were recruited to the study. The participants were selected by trained personnel and physicians via simple random sampling.The mean participant age was 37.13 ± 12.66 years. Among all the participants, 79% were married, 4% were single, 3% were divorced and 14% were widowed. We observed that 80.6% of respondents were housewives, 5% were students and 14.4% were working women. The participants had a severe lack of knowledge and awareness, and perceived barriers regarding breast cancer screening. Breast cancer was more linked to personal history, occupation and, marital status. Shyness, fear, lack of knowledge and deficient awareness programs were the major perceived barriers.Educational interventions and proper, appropriate and socially acceptable awareness programs will help to ameliorate knowledge and awareness by addressing barriers regarding breast cancer among the females in Bangladesh. Cancer Research; Epidemiology; Health Sciences; Public Health; Clinical Research; Education; breast cancer; awareness; Knowledge; barriers; females; Bangladesh. It is tCountries in South Asia are facing a secret epidemic of breast cancer. There is a worrying rise in the incidence of breast cancer. Around 588 million women aged >15 years are facing an increasing epidemic of breast cancer. Throughout South Asia, knowledge of epidemiology, genetic and various environmental contexts of breast cancer is scarce. There are no central cancer registries that can deliver complete nationwide data . In PakiBangladesh is a small country with the highest population density in the world. The increasing population and scarcity of proper knowledge may lead to an increased number of patients with cancer, which is inevitable. Breast cancer is also increasing at an alarming rate. A recent report suggested that the breast cancer incidence rate in Bangladesh was 22.5 per 100,000 women . MoreoveHowever, Bangladesh has paid little attention to the knowledge and awareness of breast cancer in females. There is a lack of trained oncologists to serve the huge number of patients with breast cancer. Besides, there are only a few studies on breast cancer in the Bangladeshi population, but the disease burden has remained immense, as cases are identified late. Moreover, information from previous studies does not reflect the complete scenario of Bangladeshi communities and has many limitations. Thus, there is a compelling need for a more comprehensive study on this issue. Therefore, considering the socio-cultural norms and other facts, we designed a hospital-based survey to explore the complete scenario of knowledge, awareness and perceived barriers of breast cancer in Bangladesh among females.22.1The National Institute of Cancer Research & Hospital (NICRH), a tertiary care hospital, was selected for this hospital-based survey. The hospital is in Dhaka, the capital city of Bangladesh, where people of all ethnicities come to seek medical treatment for cancer . All the2.2The questionnaire was constructed combining previously published articles , 17, 18.2.3Data collection was assisted by four dedicated volunteers who were pharmacy graduates and physicians. They had completed a 1-month training course on breast cancer screening and risk assessment and were trained on the research questionnaire. They had also attended the seminars on breast cancer awareness and perspectives in Bangladesh arranged by different medical institutions. The data collection comprised four steps. In the first step, the participants were required to fill out the questionnaire, including questions related to socio-demographic and anthropometric information. The questionnaire was distributed to the participants, to whom we explained the research purpose and that their confidentiality would be maintained. The authors and trained volunteers were present to clarify any doubts or questions the participants might have had. The questionnaires were completed and collected immediately thereafter.2.4All collected data were entered into Microsoft Excel 2013 and transferred to SPSS software version 22.0 for Windows. The data were analysed using descriptive statistics to summarize the demographic data and responses of the participants.3Overall, 500 female patients with breast cancer participated in the study, yielding a response rate of 94.55%. When evaluating general knowledge regarding breast cancer, 64.4% of participants reported that they had some knowledge of breast cancer. Of the total participants, 17.2% reported that they did not know about the BSE and how it was done. About 91% of participants had never heard of any breast cancer screening programs, and 9% of participants considered breast cancer an infrequent disease. Finally, it was important to determine the possible barriers to breast screening. Many participants felt embarrassed about telling others about breast cancer (64.6%) and had no idea what other people, i.e. those in society, thought about it (62.2%). Of these respondents, 53% were concerned about stigma following their breast cancer diagnosis, 53.6% felt shy about uncovering their breasts, 41.8% were afraid of hospitals and health facilities, 56.6% worried about what the doctor might find during screening and 52.2% had difficulty talking with the doctor. Most of the participants had a lack of knowledge (58.6%) and were afraid of undergoing a mammography (60.8%), an important technique for diagnosing breast cancer. Moreover, 38.6% of the participants were busy or had no time for screening, and 53.4% reported deficient awareness programs .Table 3B4This hospital-based survey was carried out to determine the knowledge, awareness and perceived barriers of breast cancer among females in Bangladesh. We identified key factors that may shape comprehensive studies on breast cancer awareness. Despite the participants having some level of awareness regarding breast cancer, most of the women lacked diagnostic intervention for socio-cultural reasons. We found that the mean age of patients with breast cancer in the Bangladeshi population is comparatively lower than that of India, Pakistan and the western countries. This early age has great implications regarding the lack of knowledge and awareness of breast cancer among females in this country. Our findings are consistent with that of studies in Iran and Pakistan , 19, 20.Most of the participants tended to avoid breastfeeding, used contraceptive pills and had late menopause, which is considered risk factors of breast cancer. In addition, breast cancer awareness was comparatively higher with age and increased level of education. This could be due to their level of education and experience-sharing with other females such as friends, relatives, and colleagues. Moreover, single female had comparatively lower breast cancer awareness than the other participants. This could be due to their younger age or less interest in sharing personal experiences with other females, as it is not a common social norm in Bangladesh. Pakistan has a similar scenario .As breast cancer can be diagnosed early by breast screening programs, it was important to determine the possible barriers to screening. The results indicate that embarrassment was the leading barrier to breast screening programs. From the results, it was also evident that most of the participants were worried, lacked knowledge and were less confident about sharing their problems with healthcare providers and people in society, which is consistent with some previous studies , 18. ThiThe findings from the present study show that a lack of knowledge of performing BSE and socio-cultural factors against breast screening might be the causes of the unsatisfactory level of breast cancer awareness in Bangladesh. More awareness programs and proper knowledge delivery may change this scenario, although breast cancer prevalence is on the rise. Healthcare professionals and the government can come forward together to formulate an executable policy that can diagnose and increase awareness among the females as well as in society. According to the American Cancer Society (ACS), BSE is helpful in early diagnosis but can increase stress and anxiety in females and decrease survival . Again, One of the main limitations of the present study is that it is not representative of the whole Bangladeshi population or the participants are comparatively more health-aware. As the socio-cultural situation is not conducive to covering a wide variety of demographic classes, the study was conducted in a cancer hospital located in the nation's capital. Also, the research was conducted with no external funding. So, we were limited to collecting the sample from other regions of Bangladesh. Further research can be performed on a wider scale, covering more respondents from different demographic classes and geographic locations to picture the total scenario that may lead to the design of intervention programs on breast cancer awareness.5The majority of female respondents were generally unaware, lacked knowledge and awareness, and perceived barriers regarding breast cancer. Breast cancer was more linked to personal history, occupation, and marital status. Shyness, fear, lack of knowledge and awareness program deficiency were the major perceived barriers. Our findings suggest that proper, appropriate and socially acceptable awareness programs will help to improve knowledge and awareness, and address barriers regarding breast cancer among females in Bangladesh.M.N. Amin: Conceived and designed the experiments; Analyzed and interpreted the data.M.G. Uddin: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.M.N. Uddin, M.Z. Rahaman and M.S. Hossain: Performed the experiments; Contributed reagents, materials, analysis tools or data.S.A. Siddiqui, M.N. Hasan, S.M.N. Uddin: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.M.R. Islam: Analyzed and interpreted the data.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.The authors declare no conflict of interest.No additional information is available for this paper.


Mammographic breast density is associated with a four to six times increased risk for breast cancer. Mammographic breast density varies by ethnicity, geographical region and age. The aim of this study was to document for the first time the mammographic breast density of Jordanian women and to explore its relationship with age.Mammograms completed at King Abdullah University Hospital  between January 2016 and August 2018 were retrospectively reviewed and classified for breast density using the American College of Radiology (ACR) Breast Imaging‐Reporting and Data System (BI‐RADS). Descriptive analyses and Kurskal–Wallis test were used to examine the association between age and mammographic breast density.P < 0.001). In women aged 40–49 years, 83.2% had dense breasts (ACR BI‐RADS (c) and (d)). This percentage decreased to 59.8% of women aged 50–59 years; 38.4% of women in their 60s and 37.9% of women aged 70 years or older (ACR BI‐RADS (c) only).A total of 659 mammograms were reviewed. A significant inverse relationship was observed between age and breast density (The mammographic breast density of Jordanian women has been shown to be high across all age groups. Increased mammographic breast density is associated with increased breast cancer risk and renders mammography a less effective technique for the early detection of breast cancer. Breast cancer screening of Jordanian women should be individualised to develop screening protocols and include additional adjunct imaging to best manage women at high risk. The mammographic breast density of Jordanian women has been shown to be high across all age groups. Increased mammographic breast density is associated with increased breast cancer risk and renders mammography a less effective technique for the early detection of breast cancer. Breast cancer screening of Jordanian women should be individualised to develop screening protocols and include additional adjunct imaging to best manage women at high risk. Breast cancer accounts for almost 1 in 4 cancer cases amongst women worldwide.Mammography is the gold standard screening modality for the early detection of breast cancer, and it has been shown to significantly reduce breast cancer mortality.Mammographic breast density is a strong independent risk factor for breast cancer and women with high density breasts have four to six times increased risk of developing breast cancer as compared to women with low density (fatty) breasts.Breast density can be influenced by several other risk factors for breast cancer. It has been reported that breast density increased by 3–5% after oestrogen and progestin hormone treatment.Mammographic breast density underlies many international differences in breast cancer prevalence and is known to vary amongst different ethnic groups and geographical regions.There is a paucity of information describing the mammographic density profile of the women in Jordan. Current imaging approaches in Jordan are based on what is known from the data of Western countries, highlighting the efficiency of their screening programmes. In designing an effective screening programme in Jordan, the mammographic breast density amongst Jordanian women and its relationship with age needs to be established. With breast cancer incidence the highest amongst Jordanian women aged 40–49 years, the target age groups of breast screening services of most developed countries starting at 50 years of age are unlikely to be appropriate.This study is the first to evaluate the mammographic density profile of Jordanian women and the relationship between age and breast density in Jordan.Institutional review board approval was granted from the Jordan University of Science and Technology (Project Number 20170023). A retrospective review of all mammograms performed between January 2016 and August 2018 using a Mammorex Peruru MGU‐1000A digital mammography unit , at the King Abdullah II University Hospital was undertaken. Each case was anonymised. All cases with a standard two‐view mammogram series  of both breasts were included. If additional projections were performed, only cranio‐caudal and medio‐lateral oblique projections were used to assess mammographic breast density. Cases with incomplete examination of both breasts, breast implants and mastectomy were excluded.Mammographic breast density was defined according to the ACR BI‐RADS 5th version.2 room, and walls were painted with light grey and brown matte paint to maintain minimal specular reflection. The ambient lighting was set to 20 lux at the position of the reader using a calibrated photometer . Mammograms were displayed on 5‐megapixel RADI FORCE GX540  monitors, which were calibrated in accordance to Digital Imaging and Communications in Medicine (DICOM) standard.Radiologists classified mammographic breast density in a 18‐mTo measure inter‐reader agreement between radiologists for classifying mammographic breast density, Cohen’s kappa statistics and 95% confidence intervals (CIs) were used. Kappa value was explicated as the following: 0.0–0.2 as slight agreement, 0.21–0.41 as fair agreement, 0.41–0.60 as moderate agreement, 0.61–0.80 as substantial agreement and 0.81–0.99 as almost perfect agreement.P values < 0.05 deemed to be statistically significant.To examine the association between age and breast density, descriptive analyses were performed. Kurskal–Wallis and Spearman’s non‐parametric statistical tests were conducted to evaluate the correlation between age and breast density. All analyses were performed using SPSS software , with The inter‐reader agreement between the three radiologists was almost perfect agreement with kappa value of 0.93 (95% CI: 0.90–0.96). All comparisons between paired radiologists showed almost perfect agreement; radiologists 1 and 2 , radiologists 1 and 3  and radiologists 2 and 3 .A total of 659 mammograms were reviewed with a median age of 49 years . The majority of women, 375 (56.9%) were classified as ACR BI‐RADS (c) (heterogeneously dense). The distribution of women in each mammographic density category is described in Table The study population was categorised into five age groups, the highest frequency was (37.8%) in age group between 40 and 49 years. The results of the distribution of breast density patterns with age group in the study population are summarised in Table r = −0.397, P < 0.001) as shown in Fig. Using Kurskal–Wallis test, there was a significant inverse relationship demonstrated between age and breast density overall . The guidelines do not take into account the breast density profile of Jordanian women and its relationship to age which until now has remained undocumented.It is well established that mammographic breast density is reflective of reproductive life and typically decreases with increasing age.The literature reports that mammographic sensitivity in 2D mammography decreases from 89.2% in low to 67.9% in high mammographic densityThe literature has shown that for women aged less than 50 years, rates of interval cancer increases from 19% for entirely fatty to 98% for the extremely dense breast.The routine addition of ultrasound to mammography in women with high mammographic density has been shown to increase sensitivity from 50% with mammography or ultrasound alone to 77.5% when combined.When combining MRI, with mammography for screening women of high risk of breast cancer, sensitivity increased to 93% compared with 33% of mammography alone.Since the implementation of DBT, several studies have examined the combined effect of DBT and mammography.Given the importance of the relationship between breast density and breast cancer risk, mammographic breast density notification laws should also be enacted. In the USA, 38 states have passed laws that mandate notification of mammographic breast density to women after mammographic imaging. Depending on patients’ individual risk factors, supplemental screening may be required.Another important consideration of this current work is regarding the appropriate age to start mammography screening and the most appropriate target age group. In Jordan, mammography screening guidelines currently allow screening to commence at the age of 40 years with women being allowed to return every year thereafter. The highest breast cancer incidence in Jordan is amongst women in the 40–49 years age groupIt should be noted that there are some limitations in this study. Women included in this study were only a subset of the population of the North and Middle areas in Jordan, a larger and more even representation of the population is needed in future studies. Another limitation is the sample distribution. The study reviewed 508 women aged 40–59 years but only 89 aged 60 years and over. If more equal numbers of women were used results may change. Additionally, the visual ACR BI‐RADS grading of mammographic breast density used in this study can be subjective. The potential for subjectivity should be reduced by the involvement of specialised and highly experienced radiologists. Although in this study radiologists demonstrated almost perfect agreement , it is possible that the high mammographic density rating of the Jordanian population is due to the threshold that the radiologists use which may have raised the mammographic breast density. Therefore, it should be acknowledged that it is more accurate to use automated methods.The findings of this study confirm the inverse relationship of a women’s age and mammographic breast density. The results suggest that a significant proportion of Jordanian women have high density ACR BI‐RADS (c) and (d) breasts, whereby they could potentially benefit from the use of additional imaging methods such as ultrasound, MRI or DBT. The current study emphasises the importance of developing individualised breast cancer screening guidelines, which are derived according to woman age, breast density, and overall risk for breast cancer. Further research is required to validate the results of this study.The authors declare that they have no conflicts of interest.