CASP8 gene region in a group of the Iranian population.Multiple genome-wide and candidate-gene association studies have been conducted to search for common risk variants of breast cancer. Recent large meta-analyses and consolidating evidence have highlighted the role of the caspase-8 gene in breast cancer pathogenesis. Therefore, this study aimed to identify common variations and haplotypes associated with risk and overall survival of breast cancer with respect to underlying susceptibility variants in the rs3834129, rs2037815, rs7608692, rs12990906, rs3769821, rs6435074, rs3754934, rs3817578, rs10931936, rs1045485, rs1045487, and rs13113, were performed using PCR-based methods, including ARMS-PCR, AS-PCR, RFLP-PCR, HRM-PCR, and TaqMan-PCR.In a case-control study with a total of 1008 samples (455 cases and 553 controls), genotyping of 12 candidate polymorphisms, consisting of rs3834129, rs3754934, rs12990906, and rs10931936 were associated with the risk and overall survival of breast cancer. Several haplotypes were also identified an associated with a higher risk of breast cancer, including a three-SNP haplotype rs3817578-rs10931936-rs1045485 . rs3754934-C allele showed an association with a lower risk of death in all patients  and in the hormone-receptor-positive group , as well as CC genotype in the hormone-receptor-positive group .CASP8 gene variations in breast cancer. The risky haplotypes are likely to have one or more underlying breast cancer susceptibility alleles. Understanding the mode of action of these alleles will aid individual-level risk prediction. It also may help identify at-risk patients to provide them with better surveillance.The present study suggests a diagnostic and prognostic role of The online version contains supplementary material available at 10.1186/s12920-023-01484-0. Breast cancer is the most common cancer among women, accounting for 11.7% of all new cancers and 24.5% of all female cancers. Moreover, it is the fifth cause of cancer death and first-ranked in women  were associated with breast cancer risk. Significant results have been reported in Tables The CTG haplotype of Genotypes, haplotypes, and diplotypes were extensively analyzed for a potential correlation/association with breast cancer clinicopathological characteristics and overall survival. Significant results have been presented in Tables rs3834129 (p = 0.034) and rs2037815 with menstrual age (p = 0.026), rs1045487 with the diagnosis age (p = 0.022), rs13113 with BMI (p = 0.029), rs7608692 with molecular category (p = 0.039) and rs3754934 with ER status (p = 0.008).Evaluation of the genotypes with respect to clinicopathological features specified the association of p < 0.001). Furthermore, three six-SNPs diplotypes were correlated with the stage of the disease (p = 0.017), HER2 status (p = 0.043), and BMI (p = 0.004).Haplotype analysis identified a four-SNPs haplotype correlated with ER status ] in all patients ] ] . This result was again confirmed by a 7% increased risk in the European population [rs3754934 polymorphism in the dominant model had a 51% reduced risk of breast cancer in our population, a study of this variant in the British population did not indicate a significant association [Regarding the pulation . While apulation , a meta-pulation . Consistpulation . In addipulation . Howeversed risk . This reCASP8, including a 3-SNPs, a 4-SNPs, and a 5-SNPs haplotypes, associated with 58–78% increased risk of breast cancer in the study population. In two previous studies considering different polymorphisms of CASP8, several haplotypes, including rs7608692, rs3834129, rs3817578, and rs1045485, have been reported to be associated with a 28–31% increased risk of breast cancer [rs3834129 and rs1045485 have been introduced as prominent risk-related variants in line with the present study.Association studies have confirmed the higher statistical power of haplotype analyses compared with alleles or genotypes analysis itself , 31. In t cancer , 12. In CASP8 variants related to pathological factors [While a previous study has not provided such associations , another factors . Conside factors . These rCASP8 rs3754934 in the study population showed a relationship with prognosis. Previously, the association of rs3769821 [rs1045485 [rs3834129 deletion allele was associated with poor prognosis in the German population, which contradicts the protective effect of this allele in breast cancer [The importance of polymorphisms is known as prognostic markers, as polymorphisms can play a leading role in altering the uptake and absorption of chemotherapy drugs and may influence the response to chemotherapy and, ultimately, the outcome of the disease , 35. Hows3769821  and rs10CASP8 gene region can add more evidence to the literature about the overall role of the gene in breast cancer and improve the information about the genetic basis of the disease. Based on the results of this study, which was conducted for the first time in the Northeastern female population of Iran, CASP8 gene polymorphisms, haplotypes, and diplotypes may be used as predictive markers for the risk and prognosis of breast cancer. In addition, identified haplotypes and diplotypes which carry certain risk-related alleles may have the ability to be used in multigenic tests to calculate individual risk levels for personalized medicine purposes.The present study with a carefully selected range of genetic markers across the These findings, however, suggest that there is a difference in the allele frequency of considered variants in Iranian populations compared to Asian-related reports. This finding may indicate profound differences in the genetic background of populations and consequently different effects of alleles. Given that the eleven variants studied in this project were studied for the first time in Iran, highly-quality controlled frequencies obtained in this project can be used in calculating the appropriate sample size for future studies. However, identifying the mechanism of action of these haplotypes can also help to identify the tumorigenic process and may lead to opening new windows to the identification of therapeutic targets.Below is the link to the electronic supplementary material.Supplementary Material 1


Sex-steroid hormones are associated with postmenopausal breast cancer but potential confounding from other biological pathways is rarely considered. We estimated risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal estrogen receptor (ER)-positive breast cancer, while accounting for biomarkers from insulin/insulin-like growth factor-signaling and inflammatory pathways.This analysis included 1,208 women from a case-cohort study of postmenopausal breast cancer within the Melbourne Collaborative Cohort Study. Weighted Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, estrogens, androgens, and sex hormone binding globulin (SHBG). Analyses included sociodemographic and lifestyle confounders, and other biomarkers identified as potential confounders.Increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone , androstenedione , dehydroepiandrosterone , total testosterone , free testosterone , estrone , total estradiol  and free estradiol . A possible decreased risk was observed for SHBG .Progesterone, estrogens and androgens likely increase postmenopausal ER-positive breast cancer risk, whereas SHBG may decrease risk. These findings strengthen the causal evidence surrounding the sex hormone-driven nature of postmenopausal breast cancer. A recThe quality of the evidence in this review was largely determined by dose-response effects and large effect sizes . No extrStudies that adjust for other biomarkers typically compare results with and without adjustment for other sex-steroid hormones and/or SHBG , 13–22. The aim of this study was to estimate risk ratios for sex-steroid hormone biomarkers in relation to postmenopausal breast cancer in a case-cohort of postmenopausal women within the Melbourne Collaborative Cohort Study (MCCS), while accounting for other biomarkers from the insulin/IGF-signaling and inflammatory pathways.2.2.1.The MCCS includes 24,469 women aged 40–69 at recruitment from 1990–1994 . At base2.2.2.2.1.2. Women were considered postmenopausal if they had had no menstrual periods in the past 12 months and met one of the following criteria: had experienced natural cessation of menses; had a bilateral oophorectomy; were age 55 years or older; or had had no periods in the 12 months prior to baseline and, for participants in a previous case-cohort study, measured estradiol concentration below 109 pmol/L at baseline , had provided a blood sample , had no prior invasive cancer diagnosis (except for keratinocyte cancers); at baseline, they had a body mass index (BMI) ≥ 18.5 kg/mat study , 27). ThAn eligible tumor was defined as invasive adenocarcinoma of the breast  that was ER-positive. Tumors of unknown hormone receptor status were included as 88% of breast cancer diagnoses among eligible women of known ER status were ER-positive. ER-negative and progesterone receptor (PR)-positive cancers were also included as this tumor subtype may be misclassified and accounts for only 1–4% of diagnoses –31. UnspIn total, 1,412 women were selected for the case-cohort study, including 999 in the subcohort and 459 cases (46 from the subcohort) . The sub2.2.2.Of the 1,412 selected women, 286 (20%) had unknown menopausal status and/or HRT use. Eligibility was confirmed for all selected women using the distribution of measured estradiol values at F2 for naturally postmenopausal women who were not taking HRT . Thirty-two women with estradiol values at or above the 99th percentile of this distribution  were excluded, regardless of age, menopausal status, or HRT use. Menopausal status and/or HRT use could not be determined for six women missing estradiol measurements. One woman was excluded as she did not participate in F2 despite providing a blood sample.Four cases outside the subcohort were retrospectively disqualified as cases; three diagnoses were ascertained from death certificate only and one woman was diagnosed with non-adenocarcinoma breast cancer. To minimize the impact of death as a competing risk, follow-up was chosen to end on participants’ 86th birthday (Online Resource 2). Thus, 44 cases outside the subcohort were excluded and eight cases within the subcohort were analyzed as non-cases. Thirteen users of exogenous insulin were excluded so that measured insulin concentrations were of endogenous insulin.post hoc exclusions comprised 1,312 women, 969 in the subcohort and 378 cases   .2.3.Plasma samples of selected women were randomly ordered and allocated into 21 batches containing approximately equal numbers of cases. The samples were shipped on dry ice in two dispatches to the International Agency for Research on Cancer (IARC).The plasma concentrations of all biomarkers were measured at the Nutrition Metabolism Branch, IARC. Plasma concentrations of sex-steroid hormones and SHBG were measured as previously described . In brie2.4.Biomarker data were cleaned and normalized to correct for effects of batch, dispatch, and time since last meal (12% of study participants were not fasting at blood collection). The normalization technique was adapted from Viallon et al. . Normali2.5.– 4 mol/L) and the following association constants: 6 × 104 L/mol ; 4 × 104 L/mol ; 0.68 × 109 L/mol (binding of estradiol to SHBG); 1.6 × 109 L/mol (binding of testosterone to SHBG) [Concentrations of free estradiol and free testosterone  were calculated from normalized values of estradiol, testosterone and SHBG using the law of mass action assuming a fixed albumin concentration of 40 g/L (5.97 × 10to SHBG) –37.2.6.Descriptive statistics were presented as medians and interquartile ranges (IQRs) or as frequencies and percentages, where appropriate. Weighted modified Poisson regression with a robust variance estimator was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of postmenopausal ER-positive breast cancer, per doubling plasma concentration of progesterone, androstenedione, DHEA, total and calculated free testosterone, estrone, total and calculated free estradiol, and SHBG. Case weights were one, and weights for non-cases were the inverse of the sampling probability for non-cases .a priori using causal diagrams informed by expert consensus and literature review. Sociodemographic and lifestyle confounders included: education; country of birth; socioeconomic disadvantage; diet at baseline ; alcohol consumption at baseline; smoking status at baseline; adiposity at baseline; physical activity at F2; age at blood collection; and age at menopause. The identification, measurement and modelling of sociodemographic and lifestyle confounders are described in Online Resource 7. As age at menopause could only be measured for naturally postmenopausal women , this variable was not included in the adjustment set for the primary analyses. Sensitivity analyses were conducted, restricting to naturally postmenopausal women with a recorded age at menopause to include this variable in adjustment sets. Biomarkers that were identified as potential confounders a priori but had correlations ≥ 0.50 with the biomarker of interest were not included in the primary analysis (Online Resource 8).Confounders including other biomarkers were identified 2-scale. A one unit increase on the log2-scale represents a doubling in biomarker concentration. Analyses were repeated without adjustment for other biomarkers (where applicable). In addition, analyses that modelled concentrations of each sex-steroid hormone biomarker as quartiles corresponding to the distribution of normalized biomarker values in the subcohort were performed without adjustment for other biomarkers.The primary analyses modelled all biomarker concentrations as continuous, normalized values on the logAll analyses were complete-case analyses. The linearity assumption was tested for the continuous, normalized biomarker values using restricted cubic splines and Wald-tests. All statistical analyses were performed using Stata 16 .3.post-hoc exclusions, 87 were excluded due to missing sociodemographic and lifestyle confounder data .Of the 1,312 women eligible after der data . In addi3.1.The calculated overall intra-assay and inter-assay CVs were below 10% and 15% respectively for most biomarkers (Online Resource Table 4.1). The estimated intra-batch and inter-batch reliability ICCs were above 80% and 70% respectively for most biomarkers (Online Resource Table 4.2). The validity coefficients for the true and measured values of estradiol and testosterone were 0.987 and 0.997, respectively. Correlation plots are presented in Online Resource 5.3.2.For the primary analyses, increased risks of postmenopausal ER-positive breast cancer were observed per doubling plasma concentration of progesterone , androstenedione , DHEA , total testosterone , calculated free testosterone , estrone , total estradiol  and calculated free estradiol  . A decreResults did not appreciably differ in analyses without adjustment for other biomarkers , except 3.3.The highest versus lowest levels of biomarker concentrations were associated with increased risks of postmenopausal ER-positive breast cancer for progesterone , androstenedione , DHEA , total estradiol  and calculated free estradiol  . RRs wer4.Higher plasma concentrations of progesterone, estrogens and androgens, and decreasing plasma concentration of SHBG, were associated with increased risks of postmenopausal ER-positive breast cancer in this case-cohort of postmenopausal women. Similar results were obtained with and without control for other biomarkers that were identified as potential confounders, suggesting that confounding by the insulin/IGF-signaling and inflammatory pathways was minimal. The exception was SHBG; a somewhat stronger inverse relationship was observed with adjustment for adiponectin, leptin, insulin and IGF-1. Results of the sensitivity analyses in the subset of naturally postmenopausal women with a recorded age at menopause were not sensitive to adjustment for age at menopause. Rather, the deviations observed from the primary analyses could be explained by reduced precision in the subsample, or differences between women who were naturally postmenopausal (with a known age at menopause) and women who were assumed to be postmenopausal for other reasons.a priori using a causal diagram that was informed by literature review and expert opinion. Causal diagrams can minimize the pitfalls of other confounder selection methods, including overadjustment bias [A strength of our study was that careful consideration was given to biomarkers from the insulin/IGF-signaling and inflammatory pathways that may confound the associations between biomarkers of the sex-steroid hormone pathway and risk of postmenopausal ER-positive breast cancer. Biomarkers that may be potential confounders were identified ent bias , 39, 40.The validity of our results depends upon the extent to which the measurements of the chosen biomarkers accurately represent the biological components of the inflammation, insulin/IGF-signaling and sex-steroid hormone pathways implicated in breast carcinogenesis. A major strength of our study was the use of a highly sensitive liquid chromatography-mass spectrometry method to measure the plasma concentrations of sex-steroid hormones in postmenopausal women with high precision and accuracy. We were able to demonstrate the validity of this method using reference standards for estradiol and testosterone. The measured and true values of estradiol and testosterone were highly correlated. Further, intra-assay and inter-assay CVs, as well as intra-batch and inter-batch ICCs, calculated from quality control samples indicated that the biomarker measurements were reliable, with few exceptions that may be attributable to batch and dispatch effects (Online Resource 4). We adopted a novel analysis approach to correct for batch effects, dispatch effects and time since last meal, whilst retaining meaningful biological variation in the biomarker measurements . Furthera priori assumption that progesterone does not have a direct effect on postmenopausal ER-positive breast cancer . This assumption was based on the systematic review by Drummond et al. [Our findings were generally consistent with previous studies, including a recent systematic review by Drummond et al. , a previd et al. , which fd et al.  – warranOur study confirms the causal role that sex-steroid hormones and SHBG play in the etiology of postmenopausal ER-positive breast cancer. We strengthen the causal evidence by demonstrating that potential confounding from other biological pathways implicated in breast carcinogenesis is likely non-substantial. Of note, two recent systematic reviews found insufficient evidence to establish a causal link between the inflammation and insulin/IGF-signaling pathways and breast cancer , 11. Fut


Breast Cancer incidence in the UK is estimated to rise to 71,000 per year by 2035. Preventative strategies could significantly reduce this. Preventative therapy reduces women’s risk of oestrogen receptor positive breast cancer, but uptake remains low. Having established a preventative therapy clinic as part of a wider breast cancer prevention project, we explored qualitative data to inform future preventative efforts.Women aged 30 to 60 who had benign diagnoses at a symptomatic breast clinic or were under mammographic surveillance in the moderate risk family history clinic were invited to participate in the study. Those who expressed an interest and completed an initial questionnaire had their breast cancer risk calculated using the IBIS risk calculator. Those at increased risk were invited to a consultation about preventative therapy.182 women were identified as increased risk  ethical approval by the Westminster Ethics Committee. IRAS project ID 265619, ClinicalTrials.gov Identifier: NCT04574063. Recruitment began in September 2020 and was completed in October 2021. Breast Cancer is common: Data from 2015 to 2017 suggest a UK incidence of 55,200 per year . Such a In 2013 a meta-analysis of nine trials  was performed to assess the effectiveness of selective oestrogen receptor modulators (SERMs) in prevention of breast cancer. This indicated that women at increased risk of developing breast cancer who were given SERMs had a 30% reduction in relative risk . In the A crude calculation of the potential population impact of preventative therapy for breast cancer in the UK suggests that if 15.5% of women were eligible and all took tamoxifen, more than 250,000 breast cancers could be prevented over the course of these women's lives. As 17% is the threshold for being considered at increased risk, many women at increased risk would have a higher lifetime risk than this and stand to benefit more. More than 250,000 could therefore be considered a conservative estimate. While statistics from the US may not be directly applicable to the UK population, these figures do give an indication of the number of women who potentially could be spared a cancer diagnosis and all the anxiety, treatment and side effects that this invokes.In the UK, NICE introduced guidance in 2013 advising clinicians to offer preventative therapy to women at increased risk of breast cancer. Hackett et al.  performeBetween 2015 and 2020, part of the Cancer Strategy for England was to recommend that general practitioners (GPs) prescribe tamoxifen for breast cancer primary prevention, and yet only half of GPs surveyed knew that tamoxifen could be used for preventative therapy.  Smith etThe UK Academic Health Science Network (AHSN) announced in March 2021 that tamoxifen for preventative therapy would become an NHS England “Rapid Uptake Product”  because Women aged 30–60 who were assessed in the symptomatic breast clinic and discharged with a benign or normal diagnosis were invited to join the PIONEER study . This was a randomised controlled pilot study which invited women to engage in lifestyle change to reduce breast cancer risk. Benign diagnoses were defined as anything other than cancer or DCIS. Women under surveillance in our moderate risk family history clinic were also invited to participate. They were given a participant information leaflet. The onus was on the participant to contact the study team should they wish to take part.Once contact had been made, eligibility was confirmed (see criteria below) and a link was sent by email to an online consent form and initial questionnaire. The participant was given a unique ID to complete the questionnaire. The questionnaire covered lifestyle risk factors and included questions needed to complete the Tyrer–Cuzick (IBIS) breast cancer risk calculation (Version 8b), which was the calculator used in the PIONEER protocol. Mammographic density was also included for women who were over the age of 40 and had a mammogram as part of their clinical assessment.or high-risk ≥ 8% ten-year risk or ≥ 30% lifetime risk)  health”. Before I was able to speak about preventative therapy another woman reported that she was unwilling to take ‘experimental’ medicine.One person highlighted the cost of prescriptions as a potential barrier. In England, most patients will pay for prescription medications. The current cost for a prescription is £9.35. Prescriptions are often made monthly, to allow for reviews of medication. If a woman were to pay £9.35 each month for 5 years, the cost of this medication would be £561. For some this may be a trivial amount of money, but for others, this may be the deciding factor in whether to start taking preventative therapy.Another theme that emerged was one of deferring a decision about preventative therapy. A number of women reported either that they were trying to have a baby or were unsure whether they had completed their family. One woman reported that she was struggling with her mental health and didn’t feel able to ‘rock the boat’ by adding a new medication into the mix. Other women were struggling with menopausal symptoms and wanted to see if these would settle down before considering preventative therapy. Two women wanted to discuss the possibility of starting preventative therapy with their partner. One woman stated that she had children with special needs. She stated, “right now, I have to be as well as possible for my children.” She went on to say that her day-to-day life was very demanding in this stage of life. One woman waited 11 months to contact the team asking for a prescription, suggesting that some women want to take time to make this decision.Some women who were perimenopausal, asked if they could still take tamoxifen after the menopause. This led to discussions around anastrozole. Anastrozole reduces the risk of breast cancer by 50% if taken daily for 5 years. This additional risk reduction seemed to give pause for thought. “Is it worth waiting till I’m through the menopause so that I can get extra risk reduction?”.The participant with the highest risk calculated stated “I’ll probably get breast cancer one day”, but she remained unconvinced about preventative therapy. Some women said words to the effect of ‘what will be will be’.Three women, who had had close contact with someone with breast cancer, asked whether taking tamoxifen would reduce their treatment options should they go onto develop breast cancer anyway. Explaining the difference between hormone positive and hormone negative cancers and the possibility that “preventative” therapy may in fact be treatment of pre-clinical disease, can be confusing. The mechanisms of action of preventative therapies are not fully understood, and so many women’s questions are answered with ‘we don’t know’. This uncertainty around some aspects of preventative therapy may be off-putting for some.Women who had seen those close to them suffer with side effects of tamoxifen often cited this as a concern, though rarely was this presented as the only reason for not taking preventative therapy. Two women were unable to take tamoxifen due to a history of a clotting disorder. One woman stated, “it’s a shame that [tamoxifen] doesn’t reduce menopausal symptoms—then it would be a no brainer!”.One woman stated that she was concerned by the increased risk of endometrial cancer. Statistics from the NICE decision aid were quoted, which state endometrial cancer risk rises from 0.3% to 0.6%. This was of greater concern than the risk of DVT and was also of greater concern than her existing moderate breast cancer risk. Another woman was concerned about her history of endometriosis and the risk, if any, that this posed on her endometrial cancer risk.“I know I’m at increased risk of breast cancer, but I’m really struggling with my menopausal symptoms. Can I take HRT?” This is a question that was surprising given the context of the conversation. It highlights that breast cancer risk is not at the forefront of many women’s minds, even if they are at increased risk of developing breast cancer. The future risk of breast cancer is eclipsed by difficulties with day-to-day life now.One woman whose lifetime breast cancer risk was over 30%, said that she had thought her risk was more like 60%, so was reassured by the conversation. Despite this “reassurance”, at the end of the conversation she said, “sign me up! I’m not afraid of things that are going to make me better off”. Interestingly this same participant had recently improved her lifestyle by increasing her exercise and improving her diet. However, there is a possibility that women who overestimate their risk may feel that their lifestyle is validated by receiving a lower calculated risk than they anticipate. Fortunately, this was not the case for this participant.Some women who only had a slight increase in either their 10-year or lifetime risk, were comfortable that their risk was only slightly above the rest of the population and did not think their breast cancer risk outweighed the downsides of preventative therapy (Table Although only one formal appointment was offered, women were invited to ask additional questions and 39 women contacted us by email following their preventative therapy conversation. Their questions covered issues such as interaction with family planning arrangements. The need for repeated discussion and time to consider the information given suggests that provision of information before the clinic appointment may improve uptake at the time of the consultation.This work has demonstrated that the uptake of preventative therapy among women at increased risk of breast cancer in a dedicated clinic is low and reasons for this are diverse, some amenable to improvement, and others not. Given the estimated number of women eligible in the general population, it is likely that lack of awareness is a major factor despite this option being widely reported in mainstream press over the last decade –19.This Strengths of this study include the large number of consultations conducted by a specialist researcher, and the open lines of communication allowing follow-up questions and subsequent requests for prescription. However, there are limitations to this work. Firstly, this preventative therapy clinic was created to fulfil a duty of care to women who were found to be at increased risk while participating in a pilot of lifestyle change to reduce breast cancer risk. As such, no formal qualitative methods were used to collect data. Nonetheless, interesting themes have emerged which may be of use in designing similar clinics in the future. Furthermore, women who had a consultation had already volunteered themselves to take part in a lifestyle change study and may represent a particularly motivated group. Conversely, given the primary aim of the study was lifestyle change, women may have been reluctant to deviate from lifestyle modification alone and take medication. Either way, these factors may limit the generalisability of the results. The majority of participants were white and, while this is representative of the local population, a more ethnically diverse population may result in different findings.While it was estimated by Freedman et al. that 15.While few women took up the offer of a medication to reduce the risk of breast cancer, all women were keen to accept additional mammographic screening which would marginally increase their risk of breast cancer but would give them “peace of mind”. This paradox was emphasised to women in the study. Women’s eagerness for screening suggests that identification of women at moderate and high-risk could lead to substantial uptake of more frequent mammography among such women. It remains to be seen whether this would translate to improved breast cancer survival.All but five of the eligible women were open to discussion suggesting an appetite to hear about risk reduction options. Before recruitment began, the study team had been optimistic that a designated preventative therapy clinic would lead to a greater uptake of preventative therapy than hurried symptomatic or family history clinics had yielded to date, because of the dedicated 30-min discussion with a clinician, who has risk communication training. However, tamoxifen and anastrozole uptake was only 9% and 6% in the relevant age groups. By comparison 14.7% tamoxifen uptake was found by Hackett et al. 67% of HA retrospective study of high-risk women in the USA showed 24% had a current or past history of chemoprevention use . Of noteFor those of us who work in the field of breast cancer, even considering the potential for side effects, it is perplexing that women are not more willing to use medical options to reduce their risk of breast cancer. This is particularly the case as women could always stop the drug if they find the side effects are too intrusive. However, most of the women in this study were more concerned about avoiding additional medicines and side effects. While the option of taking low dose tamoxifen was discussed with women who raised concerns about side effects as their main barrier to considering preventative medicine  their reThe conversation around preventative therapy is very different to that of a doctor treating cancer. Societally, we are not used to the idea of an ‘optional’ treatment. If preventative breast cancer medication could be marketed in a similar way to cardiovascular disease preventative medications, perhaps uptake would be much greater. Most patients who take statins or aspirin consider this to be quite ‘normal’. According to the British Heart Foundation, approximately 7–8 million people in the UK take statins . If sociAlthough few women took up the offer of preventative therapy in the timescale of this study, they have been provided with the relevant information and some may choose to consider it again in the future. Raising awareness at the time of identification of risk may lead to more uptake over time. It is important that women know how to contact the clinical team when they feel timing is better for them.While uptake in our clinic was low, preventative medicine may still be cost effective. With NNT of 29 for tamoxifen , and an Use of breast cancer risk calculators identifies more women at increased risk of breast cancer than prediction based on family history alone. We suggest that breast cancer risks calculations should be standard practice in symptomatic breast clinics. Those identified as “increased risk” should be offered enhanced screening and the opportunity to discuss preventative therapy. Reasons for not accepting medical therapy are diverse and important. The breast cancer community has yet to find the optimum timing and formula for discussing preventative therapy and must consider women’s own informed preferences above artificial targets.


To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women.BC cases and matched controls were enrolled in three sub-Saharan African countries, Nigeria Cameroon, and Uganda, between 1998–2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD.Of 6418 participants, 55.7% (3572) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without . Biopsy-confirmed BBD was associated with BC . BBD did not significantly mediate the effects of any of the selected BC risk factors.In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors. Breast cancer is the second leading cause of cancer-related deaths worldwide, with a lifetime risk of 1 in 23 among Eastern African women and about 1 in 46 among Western African women , with soStudies examining the relationship between BBD and breast cancer have mainly been conducted in non-African populations . CurrentGiven the paucity of data on BBD and breast cancer risk in African women, and to address this crucial gap in the literature, we conducted this study aiming to a) examine the relationships between established breast cancer risk factors and BBD, b) assess the association between BBD and breast cancer; and c) evaluate the mediating effect of BBD on the associations between breast cancer and selected risk factors, in an ethnically diverse dataset of women in Nigeria, Uganda, and Cameroon.The Nigerian Breast Cancer Study (NBCS) started at Ibadan, Oyo State, Nigeria, in March 1998 and, using the same protocol and data collection instruments was subsequently expanded to include centers in Lagos, Nigeria, Yaoundé, Cameroon, and Kampala, Uganda in 2011, to become the African Breast Cancer Study (ABCS). The study protocol was approved by the institutional review boards in all centers. The study setting and design in the various sites have been previously described .At The University College Hospital (UCH) in Ibadan, cases were recruited at or soon after their clinic visits following clinical confirmation of invasive breast cancer. Controls were recruited from several randomly selected communities within the UCH catchment area in Ibadan. We also recruited hospital controls from general outpatient and ophthalmology clinics in UCH. Lagos State Teaching Hospital (LASUTH) served primarily for case recruitment. At Mulago Hospital in Uganda, cases were recruited through the Department of Surgery’s Breast and Endocrine Unit; controls were randomly selected from the general outpatient clinics and surgical wards and matched to cases on age and ethnicity. At Yaoundé General Hospital in Yaoundé, cases were enrolled through the Department of Oncology, and controls, matched by age and ethnicity, were recruited from general medicine and obstetrics and gynecology departments. By the end of 2018, 6438 patients were enrolled in ABCS. After excluding males (n = 14) and patients with considerable amounts of missing risk factor data (n = 6), 6418 remained and were analyzed in this study.After providing written informed consent, participants completed a structured interviewer-administered questionnaire that collected information on demographics, anthropometrics , history of BBD, family history of breast cancer, menstrual and reproductive history , physical activity, past medical history, and hormonal contraceptive use. History of BBD was self-reported, physician-diagnosed, and/or biopsy-confirmed. In the current analysis, the history of BBD was considered positive if it was diagnosed at least a year before breast cancer diagnosis (for cases) or study recruitment (for controls) to keep the correct time sequence for causal interpretation. Histologic types of BBD were also enquired, including fibroadenoma, breast cysts, intraductal papilloma, and atypical hyperplasia, as selectable options, and an open-ended option for other histologic types. Inflammatory conditions such as mastitis and breast abscesses were not considered positive BBD history for this study. We also collected data on whether BBD was biopsy-confirmed, age at BBD, date of BBD diagnosis, breast side of BBD, and whether surgical resection was performed.Demographic characteristics were compared between cases and controls using chi-square or Fisher’s exact tests for categorical variables and Student’s t or Wilcoxon rank-sum tests for continuous variables. Logistic regression examined the relationships between BBD and demographics, anthropometric measurements, and breast cancer risk factors. Three multivariable logistic regression models were fit to assess the association between BBD and breast cancer. Model 1 included demographics . Model 2 included reproductive factors  and variables in Model 1. Model 3 included all covariates in Model 2, anthropometries , and family history of breast cancer. Both crude odds ratio (OR) and adjusted OR (aOR), with 95% confidence intervals (95% CI), were calculated.paramed in STATA [not through BBD, and the natural indirect effect We evaluated BBD as a possible mediator of breast cancer and selected risk factors which were based on dual association with breast cancer and BBD in our sample and the existing literature. We followed recommendations of the AGReMA statement in reporting mediation analysis results . Alcoholin STATA . We esti2 lower BMI on average. The duration of breastfeeding  and age at menarche were also significantly associated with breast cancer (Supplementary Table 1 for the study participant description by country and the distribution of ethnic groups.t cancer . See SupOf 360 (5.7%) women who self-reported BBD at least a year before breast cancer diagnosis (cases) or study recruitment (controls), 27.2% reported having biopsy for BBD, 15.6% did not have biopsy done, and 57.2% had unknown/missing biopsy status. Fibroadenoma was the most common subtype in Nigeria (54.5%), while in Cameroon and Uganda, breast cysts were mostly reported . Overall, 30.7% of women did not know the BBD subtype. The median age at BBD diagnosis was 31 years (IQR: 25–40) and the median interval from BBD diagnosis to breast cancer diagnosis/interview was nine years (IQR: 3–19) .Characteristics of study participants with and without self-reported BBD  were comOverall, breast cancer was associated with higher odds of a positive history of BBD . After adjusting for multiple covariates  and biopsy-confirmed BBD (aOR = 3.11). This finding is consistent with previous studies which have shown varying degrees of breast cancer risk associated with histologic subtypes of BBD , 38. A sTo our knowledge, our study is the first to assess the mediating effects of BBD on breast cancer risk in a heterogeneous population of African women. Our analysis suggested that BBD did not significantly mediate any selected breast cancer risk factors although it may account for about 10% of the association between age at menarche and breast cancer and 8% between mean breast-feeding duration and breast cancer. Although we did not find any published studies assessing BBD as a mediator of breast cancer risk, a published study examining the mediating effect of mammographic density (MD) on breast cancer risk found that about 17% of the risk conferred by BBD was mediated by MD .Our study is not without limitations. First, the relatively low frequency of reported non-inflammatory BBD may indicate that BBDs were underreported and thus probably underestimated. This could be partly because women in the African countries are not routinely screened for breast cancer, and as such, majorly palpable breast disease will present to the physician for evaluation. Education is significantly associated with health-seeking behavior in African women . With abIn conclusion, our findings highlight that BBD, especially when biopsy-confirmed, is an important risk factor for breast cancer and that BBD does not appear to mediate the effects of traditional breast cancer risk factors in African women. Assessing the differences in breast cancer risk for distinct histologic subtypes is worth further investigation in this population. The effects of uni- vs. multi-focal lesions and surgical treatment for BBD need also to be examined in future studies. Nonetheless, our findings suggest that when women in these regions present with BBD, they are at an elevated risk for breast cancer and should be considered in breast cancer risk assessment. Future studies using modern imaging technologies and molecular pathology are needed to confirm our findings and assess the contributions of distinct histologic subtypes of BBD to breast cancer risk.


The aim of this retrospective cohort study was to analyze the cumulative incidence of breast cancer following gout and to investigate the association between gout and subsequent breast cancer in 67,598 primary care patients in Germany.This study included adult female patients (≥ 18 years) with an initial diagnosis of gout in 1284 general practices in Germany between January 2005 and December 2020. Individuals without gout were matched to gout patients using propensity score matching based on average yearly consultation frequency during the follow-up period, diabetes, obesity, chronic bronchitis/COPD diagnoses, and diuretic therapy. The 10-year cumulative incidence of breast cancer in the cohorts with and without gout was also studied using Kaplan–Meier curves, which were then compared using the log-rank test. Finally, a univariable Cox regression analysis was conducted to assess the association between gout and breast cancer.After up to 10 years of follow-up, 4.5% of gout and 3.7% of non-gout patients were diagnosed with breast cancer. A Cox regression analysis revealed a significant association between gout and subsequent breast cancer in the total population . In the age-stratified analyses, gout was only strongly associated with subsequent breast cancer in the age group ≤ 50 , but the association was not significant in women over 50 years old.Taken together, the findings of our study provide evidence for the association between gout and subsequent breast cancer diagnosis, particularly in the youngest age group. Breast Cancer (BC) is the most common cancer type in women worldwide, with an estimated 2.1 million newly diagnosed cases in 2018 –5. NotabIn view of the above, the association between BC and gout as a common inflammatory disease has attracted much interest during the past few years. Briefly, gout is the most common form of inflammatory arthritis worldwide. Despite being one of just a few curable rheumatic diseases, both the prevalence and incidence of gout are increasing across the globe so that it is being hypothesized in the literature that a modern gout epidemic is currently developing that is on a similar scale to the obesity epidemic , 15. ThiStrasak et al. showed in a prospective study including more than 28,000 older Austrian women (aged ≥ 50 years) that hyperuricemia (> 5.41 mg/dL) was independently associated with an increased risk of total cancer mortality . In particular, malignant neoplasms of the breast and female genital organs were positively associated with hyperuricemia [In the past, uricemia . In lineuricemia –27.Aiming to explore this topic in more detail, we conducted a retrospective cohort study including 67,598 primary care patients in Germany to investigate the cumulative incidence of breast cancer as a function of gout and to explore the association between gout and subsequent breast cancer using univariable Cox regression analysis.This retrospective cohort study was based on data from the Disease Analyzer database (IQVIA), which contains drug prescriptions, diagnoses, and basic medical and demographic data obtained directly and in anonymous format from computer systems used in the practices of general practitioners and specialists . The datThis study included adult female patients ≥ 18 years) with an initial diagnosis of gout (ICD-10: M10) in 1,284 general practices in Germany between January 2005 and December 2020  based on average yearly consultation frequency during the follow-up period, diabetes, obesity, chronic bronchitis/COPD diagnoses, and diuretic therapy within 12 months prior to the index date. For the non-gout cohort, the index date was that of a randomly selected visit between January 2005 and December 2020  in the period of up to 10 years following the index date as a function of gout. Differences in the sample characteristics and diagnosis prevalence between the gout and non-gout cohorts were compared using the Wilcoxon signed-rank test for continuous variables, the McNemar test for categorical variables with two categories, and the Stuart-Maxwell test for categorical variables with more than two categories.P values of < 0.05 were considered statistically significant. Analyses were carried out using SAS version 9.4 .The 10-year cumulative incidence of breast cancer in the cohorts with and without gout was also studied using Kaplan–Meier curves which were then compared using the log-rank test. Finally, a univariable Cox regression analysis was conducted to assess the association between gout and breast cancer. The results of the Cox regression model are displayed as hazard ratios (HRs) and 95% confidence intervals (CIs). In addition, Cox regression analyses were conducted separately for different age groups. The present study included 33,799 women with gout and 33,799 women without gout. The basic characteristics of study patients are displayed in Table After up to 10 years of follow-up, 4.5% of the gout and 3.7% of the non-gout cohort were diagnosed with breast cancer Fig. . This reThe regression analysis showed a significant association between gout and subsequent breast cancer in the overall population . In the age-stratified analyses, gout was only strongly associated with a subsequent breast cancer diagnosis in the age group ≤ 50 , but the association was not significant in women over 50 years old (Table p < 0.0001). In particular, the authors found an independent, positive association with deaths due to malignant neoplasms of the breast and female genital organs [p < 0.001). Gout was most closely associated with prostate cancer, with an age- and sex-adjusted HR of 1.71 (1.45–2.02) [Our study revealed differences in the cumulative breast cancer incidence between the gout and non-gout cohorts. The Cox regression analysis indicated a significant association between gout and subsequent breast cancer in the overall population. In line with our results, Strasak and colleagues reported in a prospective study that high serum uric acid (SUA) levels (> 5.41 mg/dL) were independently associated with an increased risk of total cancer mortality  . Another45–2.02) . Finally45–2.02) .In the past, the literature has indicated that SUA exhibits antioxidant properties by scavenging reactive oxygen species (ROS) and, therefore, protects against cancer . HoweverFinally, the age-stratified analyses in our study revealed that gout was only strongly associated with subsequent breast cancer in the age group ≤ 50 . Interestingly, a national population-based study from Korea found a higher risk of cancer, as well as both all-cause and cancer mortality, in middle-aged patients (41–55 years) with gout compared to the general population. The subgroup analysis of this study showed that the risk of stomach cancer, head, and neck cancer and hematologic or lymphoid cancers was higher in middle-aged gout patients than in controls, whereby the risk of BC was not calculated. Notably, these results are in line with our observation that middle-aged gout patients have the highest cancer risk compared to controls .Based on the fact that the median age at menopause among women from industrialized countries ranges between 50 and 52 years , one canOur retrospective cohort study has several strengths: The german disease analyzer (DA) is a large European outpatient database containing data from 2898 practices with about 7.8 million patients in Germany. The representativeness of the diagnoses it contains has already been validated , 44. FurIn conclusion, our study provides evidence for the association between gout and subsequent breast cancer diagnosis, particularly in the youngest age group.


The overall incidence of breast cancer is different all over the world and even within a nation. The present study aims to investigate the stratum-specific incidence trends of breast cancer in southern Iran.In this retrospective cohort study, the data of Fars Population-Based Cancer Registry was used during 2001–2018. New cancer cases with ICD-O-3 codes C50.0 to C50.9 were categorized based on age group, morphology, and topography. Age-specific incidence rates of breast cancer were calculated during 2001–2018. Annual overall and truncated age-standardized incidence rates and their 95% Confidence Intervals (CIs) were also calculated. Afterward, the Annual Percentage Changes (APCs) of the age-specific and age-standardized incidence rates of breast cancer during 2001–2018 were calculated using Joinpoint regression software.An increasing trend was observed in the incidence of breast cancer among women during 2001–2018 ).However, the trend was increasing less during the recent years. The APC of age-standardized rates decreased from 15.03  in 2007 to 6.15 in 2018. The most common morphology of breast cancer was invasive ductal carcinoma  and its trend was similar to the general trend of different types of breast cancer. The most common site of breast cancer was the upper outer quadrant. Most breast cancer cases were female and males accounted for 2.45% of the cases. Among females, 40–55 was the most prevalent age group.The incidence of breast cancer among women living in southern Iran showed an increasing trend from 2001 to 2018. However, the rate of increase exhibited a milder slope during the more recent years. Based on the higher prevalence of breast cancer in the 40–55 age group observed in the present study, it offers valuable insight into the potential reduction of the breast cancer screening age from 50 to 40 years for healthy Iranian women. However, before implementing such a policy change, it is crucial to conduct additional studies that specifically examine the cost-effectiveness, as well as the potential benefits and risks associated with this alteration.The online version contains supplementary material available at 10.1186/s12905-023-02757-7. Breast cancer is the second most commonly diagnosed cancer in women following lung cancer . AccordiThe incidence of breast cancer is higher in developed countries, and it is the most fatal cancer in the female population . There aSimilar to other cancers , 12, theInvestigating the trends in the incidence and mortality of breast cancer in the United States (US) from 2009 to 2018, along with a review of the recently reported census worldwide, reveals an increasing trend in the incidence rate without a corresponding increase in the mortality rate. This can be attributed to improved screening practices and better clinical management of patients . HoweverIn this retrospective cohort study (Supplementary Table 1), the data of Fars Population-Based Cancer Registry (FPBCR) was used for breast cancer incidence during 2001–2018. This registry was founded in 2001 as a pathology-based registry and was then promoted to a population-based system in 2007. Shiraz, the capital of Fars province located in southern Iran, has been recognized as the most equipped cancer service and referral center in the region. Thus, access to cancer diagnosis and treatment services is much better in this city than in other cities in the south of Iran. FPBCR collects data on new cancer reports from almost all diagnostic and therapeutic centers as well as death registries in Fars province. FPBCR is also the most qualified cancer registry in southern Iran in terms of completeness of case diagnosis, comparability, data quality, and timeliness , 12, 16.The present study protocol was approved by the Ethics Committee of Shiraz University of Medical Sciences (ethics code: IR.SUMS.REC.1401.115). All methods were carried out in accordance with relevant guidelines and regulations, and there was no publication of identifying information. Moreover, informed consent was obtained from all subjects and/or their legal guardian (s) at the time of registration.New cancer cases with ICD-O-3 codes C50.0-C50.9 were retrieved and prepared. New annual cases were also counted for the categories defined based on age group , morphology, and topography. Age specific incidence rates of breast cancer were calculated by dividing the number of new cases by stratum-specific mid-year population. . Annual overall and truncated age-standardized incidence rates were also calculated based on the 2000 world population (Supplementary Table 2). The incidence data were truncated at 25 . Then, truncated incidence rates were calculated. Temporal trends of the estimated age-specific incidence rates as well as overall and truncated age-standardized incidence rates were analyzed both overall and in the most prevalent subgroup of morphology (IDC), using Joinpoint regression software (release 4.9.1). The Joinpoint regression program is a specialized software developed by the US National Cancer Institute for analyzing trends in data from the Surveillance Epidemiology and End Results Program. This powerful method identifies and connects multiple line segments on a log scale at “joinpoints” to describe changes in data trends. Significance tests are performed using a Monte Carlo permutation method. Furthermore, the program estimates the Annual Percent Change (APC) and its corresponding 95% confidence interval for each line segment. The APC is evaluated to determine if it differs significantly from the null hypothesis of no change (0%). Notably, in the final model, each joinpoint represents a statistically significant change in trends (increase or decrease), with each trend characterized by an APC value , 19. As There were a total of 9, 657 breast cancer cases including 237 males (2.45%) and 9,420 females (97.55%).Among females, after ‘Not Otherwise Specified’ (NOS), the most common location of breast cancer was the upper outer quadrant . In males, most of the cases were classified as NOS and it was impossible to determine the most frequent location considering the small number of patients.). In addition, the most common morphology was ICD-O code 8500, which was IDC . Most of the cases with IDC belonged to 45–54 (n = 29) and > 75 (n = 29) age groups. There were no cases of breast cancer under 15 years old.The largest number of male patients with breast cancer belonged to > 75 n = 41, 17.2%) and 45–49  age groups  in all types of female breast cancer  and 9.6 , respectively] Table .Breast cancer followed an ascending trend in the age groups under 55 years, except for the < 25 age group that showed a steady trend  ] Fig. ; Table 3).The results showed an increasing trend in the annual overall and truncated age-standardized rates of IDC throughout the period of 2001–2018. . Before 2005, APC was 18.19  and 18.21  for overall and truncated age–standardized incidence rates, respectively. After 2005, these measures were respectively obtained as 7.15  and 7.17   and − 0.6 , respectively] ] (Fig. .9, 50.0]The study results revealed an ascending trend in the incidence of breast cancer among females during 2001–2018, but the trend was increasing less during the recent years. The most common morphology of breast cancer was IDC and its trend was similar to the general trend of different types of breast cancer. The most common site of breast cancer was the upper outer quadrant. Most of the breast cancer cases were female and males accounted for 2.45% of breast cancer cases. In addition, the majority of female breast cancer cases belonged to the 40–55 age group.While the incidence of breast cancer increased among women between 2001 and 2018, the rate of increase has slowed in recent years. The trend in breast cancer incidence is complex and is influenced by a variety of factors, including changes in screening practices, risk factors, and treatment modalities. One possible explanation for the recent slowdown in the rate of increase is that screening programs and women’s awareness have plateaued in recent years. It is important to continue to monitor this trend and to research the underlying causes in order to develop more effective prevention and treatment strategies. A recent study in African countries also found an increasing trend of breast cancer. However, the incidence showed different trends in different regions , VietnamIn the current research, the most common morphology was IDC whose trend was similar to the overall trend of all types of breast cancer. Besides, its trend was increasing less during the recent years compared to the past. Generally, the most prevalent form of invasive breast cancer is infiltrating ductal carcinoma, which involves the abnormal proliferation of duct cells into stroma . HoweverGenerally, there are several risk factors for breast cancer including age at menarche, genetic susceptibility, race, and family history of breast cancer which are not modifiable. On the other hand, changeable risk factors are age at first birth, and factors associated with life style. BMI, alcohol consumption, cigarette smoking, and physical activity are the major risk factors for consideration . Also, hThe prognosis of IDC of breast is highly related to the expression of hormone receptors mainly estrogen receptor, progesterone receptor, and HER-2 through immunohistochemistry staining. Hormone receptor positive breast cancers have better prognosis . Triple The present study results indicated that the most common topographical subtype was the upper outer quadrant, which was similar to the findings of other studies –37. The In the present study, the largest number of breast cancer cases belonged to the 40–55 age group. In some previous studies also, the 40–60 age group was reported as the most common age group for breast cancer , 40. In This study is strengthened by the high generalizability of the study results due to use of data from FPBCR, which is one of the most qualified cancer registries in southern Iran. However, certain limitations should be addressed. Firstly, there was a large number of patients with unspecified topographies. Therefore, analysis of the incidence trend of location was not possible. Secondly, there was a paucity of data regarding the possible biological and clinical variables of breast cancer in the studied population like age at first birth, lifestyle factors, body mass index, parity, menarche age, menopause age, lactation history, and hormonal therapy. Hence, further analyses considering the related risk factors were not possible.The incidence of breast cancer among women living in southern Iran showed an increasing trend from 2001 to 2018. However, the rate of increase exhibited a milder slope in the more recent years. Since breast cancer was most prevalent in the 40–55 age group, our study provided important insights to reduce the age of breast cancer screening from 50 to 40 years in healthy Iranian women. However, additional studies focusing on the cost-effectiveness and potential benefits and harms of this change in policy would provide a more complete basis for implementing such a policy change. As a result, more new cases are diagnosed at earlier stages of cancer and can provide health policy makers with more accurate evidences for resource allocation and planning in preventive, diagnostic, and therapeutic dimensions.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2


Several factors during childhood and adolescence are thought to be associated with the development of proliferative benign breast diseases and breast cancer in adulthood. In order to identify them, the authors conducted an extensive review of the literature up to October 2022, searching for clinical studies, reports, and guidelines in English. A thorough Medline/Pubmed and Google scholar database research was performed, investigating the link between diet, exercise, age of menarche, body mass index, ionizing radiation exposure during childhood and adolescence, and proliferative breast diseases and breast cancer in adulthood. A list of keywords, including breast disorders, adolescence, childhood, and breast cancer was included in our search algorithm. Numerous studies concede that the development of breast disease in adulthood is influenced by various risk factors, whose influence begins during early childhood and adolescence. Numerous studies have tried to explore the relationship between early life exposures and the risk for benign breast disease (BBD). Relevant literature has shown that proliferative BBD may also increase the subsequent risk for breast cancer . UnderstSome of the earliest epidemiological studies highlighted the role of factors such as age at menarche onset, age at first birth, total body fat, and adolescent body mass index (BMI) in determining subsequent risk of breast cancer . These oIn order to identify factors acting during childhood and adolescence and are potentially related with the development of proliferative benign breast diseases and breast cancer in adulthood, the authors conducted an extensive Medline/Pubmed and Google scholar database search of the literaturein English up until October 2022. A list of keywords including breast disorders, adolescence, childhood, and breast cancer was included in our search algorithm.In 1988 deWaad and Trichopoulos proposed that an energy-rich diet during puberty and adolescence stimulates the growth of mammary glands and leads to an increased occurrence of precancerous breast lesions . FurtherIn a cohort study during adolescence, Baer et al. examined the relation between type of fat and BBD. Those who were in the top quintile of animal fat consumption had a 33% increased risk of proliferative breast diseases (P-BBD), whereas women who were in the highest quintile of vegetable fat consumption had a 27% reduced risk of P-BBD. The highest quintile of monounsaturated fat consumption was associated with a relative risk of 1.52. No association with total fat consumption was found .As far as meat consumption is concerned, several hypotheses explain how intake of red meat could induce carcinogenesis: its highly bioavailable iron content, growth- promoting hormones, carcinogenic heterocyclic amines formed in cooking, and fatty acids contained in the meat .p Value = 0.05) compared with those at the lowest quintile. In fact, the association was stronger for negative estrogen receptor (ER−) and progesterone (PR+) positive tumors [In a prospective cohort study, Linos et al. found that women who consumed the highest amounts of red meat presented an elevated risk of breast cancer  of 0.75. The association for fruit intake during adolescence was independent of adult fruit intake . Women iSeveral biological mechanisms could explain the protective effect of fiber consumption from breast tumors. Dietary fiber may increase excretion of estrogen by inhibiting the reabsorption of estrogen from the gastrointestinal tract. Additionally, the protective effect may also be partly due to the anti-estrogenic effects of lignans, which have inhibitory effect on cell proliferation in breast tumors .Soy intake during childhood and adolescents as well as its properties have been thoroughly examined. A retrospective cohort of Asian migrants to the USA demonstrated the protective effect of higher soy intake during childhood (OR 0.40). This protective effect was found to be weaker during the adolescent (0.80) and adult years (OR 0.75) .A meta-analysis including seven studies concluded that soy intake and breast cancer risk are variables that seem to be inversely related. More specifically, the meta-analysis reported that among Asian females, the ones who received a daily 20 mg dose of isoflavone had a decreased likelihood of developing breast cancer, when compared to the females that received a 5 mg daily dose . This prIn a prospective cohort study of 9031 females by Berkey et al., it was found that biopsy-confirmed BBD can be associated with certain adolescent dietary factors. In particular, the authors suggested that the consumption of animal (nondairy) fat at 10 years of age was associated with a higher risk for BBD, while the consumption of nuts and peanut butter at 14 years of age was associated with a lower risk for the development of the disease. Both are individually important milestones as they represent dietary exposures before and after adolescent height growth, which is typically completed by 14 years of age [According to the International Agency for Research on Cancer, alcohol is closely linked with invasive breast carcinoma . RelativA meta-analysis thatwas published in 2017 and based on 20 prospective studies found an association between heavy alcohol intake and ER+ breast cancer ). However, the association between alcohol and ER–breast cancers was classified as weak, based on 17 meta-analyses. The study also highlighted the effect of “moderate” alcohol intake; individuals who consumed 12.5 g to 50 g per day—the equivalent of 1 to 3.7 drinks per day—had a higher risk in comparison to non-drinkers, RR = 1.28 (1.10–1.49) .Two prospective studies failed to prove any relation between alcohol consumption before the age of 23 years old and breast cancer risk ,17. HoweThe limited data available demonstrate the link between alcohol consumption during adolescence and adulthood and the occurrence of BBD. Berkey et al., in a prospective cohort study, assessed drinking habits among females aged 16–23. In this analysis drinking 6 to 7 days per week was associated with a more than fivefold risk for BBD, whereas drinking 3 to 5 days per week was associated with a more than threefold risk in comparison to participants who consumed alcohol less than once a week or did not consume any at all . Data frThe aforementioned conclusions, along with reports suggesting that adult alcohol intake does not elevate the BBD likelihood , indicatSeveral mechanisms have been proposed for alcohol’s effects on the breast, but it is still unknown which are responsible for increased risk of BBD and breast cancer. Proposed mechanisms include an effect on circulating hormone levels, the production of carcinogens such as acetaldehyde, and oxidative stress .Monninkof et al. found an inverse relationship between PA in adolescence and breast cancer in approximatelyhalf of the studies that assessed PA before the age of 20 . Some stThe positive impact of adolescent PA on breast malignancies prior or following menopause has been previously reported. PA, however, may need to be sustained until adulthood to retain its protective effect. In the Nurse’s Health Study II, for example, a reduced risk of premenopausal breast cancer was most apparent among women who engaged in high levels of activity during both youth (ages 12–22) and adulthood compared with women with low levels of activity during both age periods, as active women had a 30% reduction of breast cancer risk  .In a recent study by Boeke et al., PA between 14 and 22 years of age appeared as modestly protective for premenopausal breast cancer, but taking into consideration other parameters such as exercise in adulthood and BMI this correlation was reduced. Furthermore, the correlation appeared slightly stronger in younger premenopausal women with estrogen receptor-negative tumors, although the differences were not statistically significant .There is little evidence suggesting that PA during childhood and adolescence has a significant impact on BBD development. Baer et al. detected a protective effect (RR: 65.95% CL: 50–84) in women who performed “strenuous” PA 4 to 6 months per year while they were in high school; however, this effect was not apparent in women who reported more frequent strenuous activity during this time period . A recenSeveral mechanisms may explain the protective effect of PA, and their function is clearer, especially during adolescence. Menarche often delays in adolescent athletes, and there are reduced menstrual cycles or anovulation, which changes sex hormone exposure. High levels of endogenous hormones such as estrogens, androgens, and prolactin increase breast cancer risk . PhysicaIn a meta-analysis by Hidayat et al., which included eighty publications and was published in 2020, extensive PA, both at a younger age as well as later in life, is shown to be associated with a reduced incidence of breast cancer .According to Van den Brandt et al., there is a negative correlation between adult adiposity and premenopausal breast cancer and a positive correlation between adult adiposity and postmenopausal breast cancer . In prosThis inverse relation to premenopausal breast cancer, described above, is also observed for proliferative BBD. Berkey et al. found that higher BMI, as measured during adolescence, was associated with slightly decreased BBD risk. Girls with a BMI in the upper two quintiles of BMI had less than half the risk (OR: 46 95% CL: 26–81) compared with those with a BMI in the lower three quintiles . This fiFollowing the Nurses’ Health Study (NHS) and NHS II, where the rates of individual breast tissue types have been quantified, Oh et al. conducted a thorough study examining the relation of early-life and adult anthropometric measures with breast tissue composition on BBD among women with biopsy-confirmed BBD enrolled in the NHS and NHS II. Regarding the early life anthropometric measures, which is the main concern in this particular literature review, it appeared that the amount of body fat in children was associated with a lower proportion of fibrous stromal tissue. ΒΜΙ at age 18 was also significantly associated with adipose tissue and inversely associated with the proportion of epithelial and fibrous stromal tissue. Oh et al.  indicateSeveral studies suggest that a rapid height growth during puberty may constitute a factor for the development of cancer. When childhood growth is rapid, there is less time available for the repairment of DNA damage caused by exposures to carcinogenic factors .A Danish study by Allgren et al., in which the annual height and weight of children was collected from school health records, reported that height growth from age 8 to 14 years was significantly associated with a high risk of developing breast cancer (RR: 1.17/5 cm increase), (CI 95%: 1.09–1.25), while growth during the peak year showed a marginally significant correlation  . In a BrMore recent evidence supports the link between height growth velocity and BBD. In the GUTS (Growing Up Today Study), Berkey et al. reported that a faster rate of growth was associated with a risk for BBD; girls with peak height velocity >8.9 cm/year were nearly twice as likely to develop BBD in comparison to girls whose peak height velocity was below or equivalent to 7.6/year .Equally interesting, in 2020, the Sisters Study Cohort, a prospective cohort study of US women with a family history of breast cancer, examined all pubertal markers in the context of their family history. It a study by Goldberg et al., it was found that women who reached their adult height at 18 years of age or later had a 13% decreased risk of developing breast cancer compared to those reaching adult height at 15–17 years of age .Earlier age at menarche is related to an increased risk of premenopausal and postmenopausal breast cancer. In a meta-analysis including more than 100 epidemiological studies, each one-year decrease in age at menarche increased the risk of breast cancer by 5% . The undIn the Multiethnic Cohort Study, age at menarche was associated with positive estrogen receptor (ER+) and positive progesterone receptor (PR+) breast cancer, but not with ER−/PR− breast cancer . In addiFurthermore, in the Sister Study, the established risk factor of earlier age at menarche was confirmed and associated with an increased risk of breast cancer for <12 years compared to 12–13 years .The relationship between age at menarche and risk of BBD is not yet well established . In the Tamimi et al. reported a differential impact of the pathologic subtype of BBD on the relationship between age at menarche and subsequent breast cancer risk among women in the NHS II . A reducAge at menarche is determined partially by hereditary factors, but body size, nutrition, and physical activity may also play a role . MenarchIn the Sister Study cohort, Goldberg et al. thoroughly examined the age of menarche, as well as other pubertal factors in relation to breast cancer. In particular, the age of thelarche was shown to be biologically more relevant to breast cancer risk than the timing of menarche, as it represents the onset of the vulnerable period of rapid breast cell proliferation. Thelarche prior to 10 years of age was associated with a 23% greater risk of breast cancer compared with the mean age of thelarche at 12–13 years. A 3% decrease in breast cancer risk was associated with each 1-year delay in age at thelarche. The timing of thelarche was inversely associated with the risk of development both ER+ and ER− cancers .The early onset of puberty appears to trigger the prolonged exposure of breast cells to a highly proliferative, undifferentiated state, making them more susceptible to carcinogenesis. Earlier puberty, caused by the re-activation of the hypothalamic-pituitary-gonadal axis, triggers the increase in endogenous hormones such as estrogen and insulin-like growth factor-1 (IGF-1), which regulate breast development .Children who received therapy with chest radiation for other pediatric malignancies are known to be at increased risk of developing breast cancer later in life. Radiation exposure for girls during peak breast development, typically from 10 to 16 years of age, is associated with the highest risk. Approximately 40% of girls treated with radiation for Hodgkin lymphoma will develop breast cancer; it takes an average of 20 years for it to appear .Breast cancer risk is greatest among women treated for Hodgkin’s lymphoma with high-dose mantle radiation, but it is also elevated among women who received moderate-dose chest radiation  for other pediatric and young adult cancers, such as non-Hodgkin’s lymphoma, Wilms tumor, leukemia, bone cancer, neuroblastoma, and soft tissue sarcoma .Inskip et al. reported that the risk of being diagnosed with breast cancer increased with chest radiation dose, reaching 10.8 (95% CI: 3.8–31) for 40 Gy compared to those who received no radiation . In addiEvidence has also suggested that the risk could be associated with radiation field volume, due to the increased risk  of women treated with mantle field irradiation compared to women with similarly dosed mediastinal irradiation (omitting the axillary nodes) .In a study by Schellong et al., up to July 2012, secondary breast cancer was diagnosed in 26 of 590 female patients with Hodgkin disease (HD). Their age at time of treatment for HD was 9.9 to 16.2 years. Radiation to the supradiaphragmatic fields was between 20 and Gy. The cumulative incidence for secondary breast disease 30 years after treatment for HD was 19% . Women aged 25 to 45 reported a frequency of breast cancer 24 times as high as in the corresponding normal population .Given that radiation therapy is a confirmed risk factor for a second breast cancer, Charlotte Demoor-Goldschmidt et al. characterized for the first time the histological subtypes and the hormonal receptor status of radiation therapy-induced SBC among survivors of a childhood or young adult cancer. In particular, a multicenter retrospective study of 121 women was conducted, with the mean age of the first cancer diagnosis at 15 years and at initial SBC diagnosis at 38 years of age. The main finding of the study associated radiation doses greater than 20 Gy to the mediastinum with triple negative phenotype breast cancers, which in turn were related to an aggressive histoprognostic status .In one of the largest studies of treatment-related breast cancer following childhood cancer, Veiga LH et al. examined breast cancer risk according to radiation dose to the breast and ovaries. Veiga et al. reported the proportional relationship between the overall risk of developing breast cancer and the radiation dose to breast cancer location and the inverse relationship between the overall risk of breast cancer and the radiation dose to ovaries. Specifically, the odds ratio (OR) in a radiation dose of 10 Gy was 3.9, and it was significantly elevated even for an irradiation dose lower than 5 Gy .In the same study, high doses to the ovaries (>=15 Gy) were related to lower odds of breast cancer associated with the radiation to the breast, while women receiving less than 1 Gy to the ovaries faced higher breast cancer risk. For both ER+ and ER– invasive breast cancers, the protective role of ovarian radiation was reported .Several other studies, including the Late Effects Study Group (LESG) cohort study, considered survivors of Hodgkin lymphoma, who were diagnosed and were treated during childhood, as a high-risk group for developing subsequent malignant neoplasms. On this basis, Holmqvist et al. pointed out the elevated risk of breast cancer in female survivors, and specifically it was associated with a 25.8-fold increased risk when compared to the general population. According to the study, the patients who face the highest risk of developing breast cancer by the age 50 years are those who were diagnosed with HL between ages 10 and 16 years, those treated with chest radiotherapy, and those who had received no or a very low dose of alkylating agents. Consistent with this finding is the recommended early screening at the age of 25 years or after 8 years from the time of HL diagnosis that would benefit this group of survivors after HL .In 2016, Henderson et al. attempted to identify the risk factors for breast cancer among childhood cancer survivors who did not receive chest radiotherapy. Henderson et al. conducted the Childhood Cancer Survivor Study, in which 3768 females without a history of chest radio therapy as part of their cancer treatment plan during childhood participated. The development of subsequent breast cancer, on the ground of nonirradiated field, was associated with exposure to anthracyclines or alkylators. Henderson et al. reported a 2.5- to 6-fold increased risk of breast cancer in women received anthracycline chemotherapies. The findings of the study confirmed the potential of anthracycline agents to trigger malignant transformations in mammalian cell systems as well as the ability of alkylators to disrupt cancer growth or cause DNA damage .2 or more of anthracycline was observed. Furthermore, breast cancer predisposition gene mutations were identified, and it was suggested that the anthracycline treatment-related risk was independent of autosomal dominantly inherited cancer predisposition mutations, especially for TP53 [Following the previous study, in 2019Ehrhardt et al. tried to clarify the relation between anthracycline-associated risk for the development of subsequent breast cancer and TP-53 mutation-related gene-environment interactions. In the context of this cohort study, in which the participants were 1467 childhood cancer survivors who were treated at St Jude Children’s Research Hospital, a greater than 13-fold risk for breast cancer in women who received 250 mg/mfor TP53 .In 2017, Hiatt et al. described the impact of socioeconomic position (SEP) on pubertal onset. In more detail, puberty onset was assessed based on the standard methods of Tanner staging and defined by observation and palpation of breast budding for stages B2 or higher as well as by observation of stages PH2 and higher.The main finding of this prospective cohort study was that girls in the lowest SEP index quantile tend to develop pubertal signs of breast budding 7 months earlier than girls in the highest SEP quantile. Given that earlier onset of female reproductive maturity is associated with increased breast cancer rates in adulthood, SEP could be indirectly related to breast pathologies. SEP, therefore, may influence breast and generally pubertal development onset .With regard to BBD, there is limited data available. Cui et al. reported no increased risk of postmenopausal, benign proliferative epithelial disorder (BPED) among women who started smoking during adolescence . RegardiLimited data have suggested an association between drug and antibiotic abuse during late adolescence and breast cancer. A study by Dahlman et al., including 3,838,248 women aged 15–75 years in Sweden, concluded that women with drug use disorders constitute a risk group for incident, fatal, and metastasized breast cancer .FurthermIn this review, we examined the impact of various factors, such as diet and exercise, obesity, and age at menarche on the development of breast disorders. Taking into consideration that their impact begins from early childhood and adolescence, their identification will lead to the understanding of the pathogenesis of breast disease later in adulthood  Figure . ChildhoOur review identified gaps in knowledge that need to be covered in order to organize effective prevention strategies. Because there is growing evidence supporting the idea that parameters such as childhood diet, growth in height, and adolescent alcohol intake are related to breast cancer risk and risk of premalignant proliferative lesions, breast cancer prevention efforts should be initiated at an early age ,72. It hThe available literature provides clear evidence that several nutritional, lifestyle, iatrogenic, and socioeconomic risk factors, already presenting during childhood and adolescence, may significantly influence the development of various benign and malignant pathologies of the breast. However, the need for more robust evidence from prospective and retrospective studies investigating their long-term impact is evident. It is also high time to sensibilize gynecologists and adolescent care specialists and raise awareness regarding the prevention of breast disease, and therefore appropriate guidance and interventions are necessary from early life and should be part of standard care.


Mammography is considered the gold standard for breast cancer screening. Multiple risk factors that affect breast cancer development have been identified; however, there is an ongoing debate regarding the significance of these factors. Machine learning (ML) models and Shapley Additive Explanation (SHAP) methodology can rank risk factors and provide explanatory model results. This study used ML algorithms with SHAP to analyze the risk factors between two different age groups and evaluate the impact of each factor in predicting positive mammography. The ML model was built using data from the risk factor questionnaires of women participating in a breast cancer screening program from 2017 to 2021. Three ML models, least absolute shrinkage and selection operator (lasso) logistic regression, extreme gradient boosting (XGBoost), and random forest (RF), were applied. RF generated the best performance. The SHAP values were then applied to the RF model for further analysis. The model identified age at menarche, education level, parity, breast self-examination, and BMI as the top five significant risk factors affecting mammography outcomes. The differences between age groups ranked by reproductive lifespan and BMI were higher in the younger and older age groups, respectively. The use of SHAP frameworks allows us to understand the relationships between risk factors and generate individualized risk factor rankings. This study provides avenues for further research and individualized medicine. Breast cancer is currently the most diagnosed non-skin cancer in women and ranks 5th among cancer-related deaths worldwide according to GLOBOCAN 2020 data . With anMammography is currently the gold standard for breast screening, with research showing that mammography may be able to detect breast cancer as early as four years prior to cancer being clinically evident . OngoingMultiple risk factors can influence an individual’s predisposition to breast cancer, given the heterogeneous nature of the disease. There are significant risk factors for breast cancer, divided into modifiable factors such as alcohol intake, smoking, parity, obesity, and hormonal replacement therapy, and non-modifiable factors such as age, sex, family history, menstruation history, and genetic predisposition . With thOur previous study  concludeIt is difficult to rank the relative significance of each risk factor using conventional research methods. In response, recent technological advancements in machine learning have allowed its incorporation into clinical decision making to facilitate medical image interpretation, outcome prediction, and treatment selection. Machine learning (ML) is a subfield of artificial intelligence that uses statistical models to analyze large datasets and interpret the complex interactions between multiple variables and patient outcomes through its automated ability to learn and enhance its analysis from experiences . HoweverThis study used three ML models, namely, least absolute shrinkage and selection operator logistic regression (lasso) ,22, extrFor this retrospective single-center study, relevant data were extracted from risk factor questionnaires completed by women who participated in the national breast cancer screening program between 2017 and 2021 at Shin-Kong Wu Ho-Su Memorial Hospital, Taipei. The risk factor questionnaires were standardized and issued by the Ministry of Health and Welfare in Taiwan. Extensive measures were taken to ensure data quality. This study examined the risk factors for women aged 45–54 years. Participants not in this age group were excluded. Other exclusion criteria included participants with a previous history of breast cancer, as well as questionnaires with missing, inconsistent, or illogical data . The parThe mammography result (Y) was separated into binary outcomes, positive and negative, with reference to Breast Imaging Reporting and Data Systems (BI-RADS) classification . PositivA total of 16 separate risk factors were identified as potential predictors of mammography outcomes, all of which were stratified into categories for analysis. To demonstrate and utilize a plug-in such as SHAP to explain the outcome of an ML model, an ML model that has reasonable performance for the data used in this study should be built first. Thus, three commonly used ML models were used in this study: lasso, XGBoost, and RF. Lasso is a logistic regression (LGR) that adds L1 regularization (least absolute shrinkage and selection operator). LGR is an extension of linear regression (LR) that can handle binary classification problems by converting the outcomes from LR to a value space between zero and one using a logit function  . L1 reguThe explainability of an ML method may be limited owing to its mechanism; thus, methods designed to improve explainability have been created and explored recently. Developed by Lundberg and Lee, SHAP was designed to explain the predicted outcomes of an ML model . SHAP exThe aim of this study was to explore important features that may affect subjects with the potential for positive breast cancer in different age subgroups. In the proposed scheme, data from the mammogram findings are first collected, and data preprocessing is conducted to exclude subjects that do not satisfy the protocols of the study. After the data were cleaned, they were further divided into two age subgroups, namely, between 45 and 49 years (AgeSub (45–49)) and between 50 and 54 years (AgeSub (50–54)). The three ML models  were then utilized for modeling both subgroups of data. For each ML model, during the construction process, the data were divided into portions for training (80%) and testing (20%). Because ML methods have hyperparameters that must be tuned, the training portion was further split into portions for training and validation. This study takes the fivefold cross-validation (5f-CV) approach when tuning the hyperparameters. The concept of 5f-CV is straightforward: the training data are randomly split into five folds, and each fold is utilized for validation once. After finding the best hyperparameters for each ML model, the testing data were used for performance evaluation to find the best-performing one for each age subgroup. The metrics evaluated were sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Next, the SHAP method was used to explain the predicted outcomes from the best ML models for each age subgroup. Using SHAP, the overall feature importance rankings of each age subgroup and explanations for individual cases were extracted. Finally, discussions were formed based on the extracted information. The experiment was implemented in Python (version 3.8.8)  and JupyFollowing the scheme mentioned previously, the ML modeling results for the different age subgroups are presented in As shown in As mentioned in the previous section, each case may be affected by features with positive or negative SHAP values. These SHAP values indicate the impact of the features on each output (the predicted outcome from the model). To understand the overall impact of each feature on the outputs, the average impact value (AIV) can be utilized. Calculating AIV is straightforward: first, the SHAP values of each feature are transformed to absolute SHAP values; then, the AIV of each feature is the mean of its absolute SHAP values. Finally, the importance of the features can be ranked according to their corresponding AIV, of which the top ranking feature is that with the highest AIV. In addition to ranking the importance of the features, the SHAP method can explain how each feature impacts individual outcomes in the RF model. For the negative case in AgeSub (45–49) in The model successfully demonstrated age at menarche, education level, parity, breast self-examination, and BMI as the top five significant risk factors affecting mammography outcomes. Breast self-examination, education level and reproductive lifespan were ranked higher in the younger group, whereas parity and BMI were favored in the older group. The RF model demonstrated the greatest efficacy, with the highest AUC in the analysis of both age subgroups . The SHAThe results indicated that age at menarche had the greatest impact on mammography outcomes in both subgroups. Early age at menarche is a well-established risk factor for breast cancer, with earlier ages conferring a higher risk . This haThe second most important risk factor identified by the model was education level. This is supported by numerous studies, including a meta-analysis of 18 cohort studies with over 10 million women that associated higher levels of education with an increased risk of breast cancer . Many stParity was identified as the third most important factor in determining mammography outcomes. Previous studies have concluded similarly, with nulliparous women carrying higher risks of breast cancer . SimilarOur model implicates breast self-examination as the fourth most important risk factor for predicting mammography outcome. Evidence for the benefits of breast self-examination remains controversial. The American Cancer Society no longer recommends self-examination as a screening method for women in the US, whereas the NHS Breast Screening Programme continues to advocate for self-examination for women in the UK. Population studies have shown increased rates of detection for breast cancer in cohorts advised to perform self-examination ,47. HoweBMI was the fifth most influential factor identified when averaged across both age groups, which is supported by abundant literature demonstrating obesity as one of the leading modifiable risk factors in the development of breast cancer ,51. The Reproductive lifespan was identified as a significant factor in the younger age group. This variable describes the years between age at menarche and age at menopause, during which the ovaries produce hormones that have a direct effect on breast tissue development . A prospThe model-agnostic nature of the SHAP methodology provides many classic ML approaches with much needed interpretability and insight into the ‘black box’ phenomenon. SHAP can explain predictions by computing the contributions of individual variables, accounting for local accuracy, missingness, and consistency, to formulate absolute magnitude and directionality of impact in the prediction of the desired outcome. Whilst conclusions from ML models may be extrapolated to wider populations, individual cases often have their own rankings of predictive variables. The directionality of the effect of risk factors on predicting the outcome may also differ for each case, as shown in To further enhance the applicability and accuracy parameters of our model, a larger dataset across multiple centers is necessary to enhance the data quality. While this study focuses on age groups with the highest incidence of breast cancer, future analysis encompassing older age groups would yield significant conclusions, especially pertaining to the post-menopausal population. The retrospective nature of this study makes it prone to selection bias. The prospective validation of the model, possibly in conjunction with mammographic image recognition neural network models, would be meaningful and result in significant clinical implications.Through the analysis of 16 risk factors for breast cancer via RF and SHAP value methodology, this study identified age at menarche, education level, parity, breast self-examination, and BMI category as the five most important factors in predicting mammography outcomes, all of which are supported by the existing literature. Stratifying participants into younger and older age groups allowed for the differences in the magnitude of impact of each risk factor accounting for age to be evaluated. The use of the SHAP value provides transparency and interpretability to ML models, which will hopefully aid clinicians in making medical decisions and increase the acceptability of ML integration into healthcare to alleviate the disease burden. This new methodology will also allow clinicians to identify previously undetected interactions between prognostic variables for each individual case, providing new avenues for research and making progress toward the future of individualized medicine.