Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice.  One in eight women will be diagnosed with breast cancer (BC) in their lifetime, with one in thirty-nine women dying from this disease just in the USA. In the same country, in 2020, approximately 42,170 women were expected to die from BC and it was anticipated that approximately 30% of the cancers detected in women will be BC [BC diagnosis occurs either during a common screening program, before symptoms appear, or after women noticing some breast changes. Screening programs are important for an early detection of BC—that is, in a more treatable stage—resulting in a decrease in mortality ,4.The criterion that defines if a woman is eligible for screening is, normally, only her age. Different countries have different recommendations on which age is the best to start screening; the USA states that women from age 45 to 54 should have a mammography once a year, while 55+ plus women should have a mammography once every two years. On the other hand, the UK NHS says that only women between 50 to 71 should be screened, and only once every three years .Although there are multiple screening programs, they might not serve all women. Some younger women may be at higher risk of developing breast cancer than women in their fifties and, despite that, these women are not eligible for screening. With that in mind, the perfect screening program should not consider age as the only risk factor that determines when to screen women.The question resides in what risk factors are not being considered when choosing the best screening option. Age is one of the best documented risk factors, with the incidence of BC being extremely low before the age of 30 and having a linearly increase until the age of 80 . Body MaMany descriptors of these texture patterns have been documented. Mammographic density is one of those descriptors, normally represented numerically by percent mammographic density (% PD), that is also highly associated with an increased risk of breast cancer ,17,18. IScreening programs all around the world use mammography, that can be acquired in a cranio-caudal (CC) and/or in a mediolateral-oblique (MLO) view, as a standard method for diagnosis, but although widely used, mammography has both benefits and harms. The aim for an early detection of this disease started in the beginning of the 20th century with awareness campaigns, but a decrease in BC mortality was only observed when the first mammographic screening started. On the bright side of mammography screening, life-threatening cancers will be detected early, improving prognosis, and consequently, decreasing risk of mortality. Studies point out that BC mortality rates, decreased at least 20%  thanks tOne of the problems associated with mammography is the rate of false positives. In Europe, the risk of having a false-positive result, for women in the range of 50–69 years having biennial screening, is about 20%. More alarming are the results in the United States, where all screened women will experience one false-positive in their life. These false-positive results have an impact in women lives, especially in day-to-day well-being and in costs concerning healthcare. But the presence of false positive is not the only downside of mammography. A summary of the benefits and harms of mammography in 1000 women with a screening every two years showed that 200 of them will experience a false positive, 30 will have a biopsy due to the false positive result, 15 will be over-diagnosed and three will develop interval cancers – the name given to a cancer that appears between two consecutive mammograms. These interval cancers may have been developed between the two mammograms, however, around 35% of them were already present in the previous mammogram but were overlooked. This means that the patient received a false negative result that can occur because, in mammography, there is an overlap of tissue that can obscure the presence of cancers . Since tNew technologies that allow a risk stratification, in line with the current medicine paradigm of preventive and personalized care, could help overcoming some discussed problems associated with the screening programs.A review of Artificial Intelligence (AI) in the field of Breast imaging was already performed by Le and his colleagues . In thisIn the current work, (1)Aim for a risk assessment, either by differentiating risk groups, predicting a risk value, or proposing new methods for the said assessment, using Machine/Deep Learning (DL) tools.(2)The methodology used should consider textural features with/without epidemiological factors.(3)Mammography images should be used for feature extraction, when that procedure is done.(4)All papers’ publication date should be within the 2000–2020 range.The review done here aims to present a global picture in what is already done in the field of breast cancer risk assessment through computerized methods, using mammograms. A search in Google Scholar using different Boolean operators and the keywords—breast cancer risk, mammography, machine learning (ML), features, parenchyma/texture patterns—was performed. This search produced eight-hundred and forty-two matches that were screened through title and/or abstract. In order to be considered for this review, papers should meet the following inclusion criteria:Papers cited by the accepted manuscripts were also screened through the previously referred criteria. Only the articles that better served the scope of this work were considered, which resulted in 11 included manuscripts. In the start of the millennium, Huo et al. , investiTo be included in the low-risk group, besides having a risk lower than 10%, women could not have any family history of BC. Mammograms from women with the BRCA1/2 mutation were considered high-risk. An important step in dividing the dataset needs to be referred, as it happens in other similar researches. The age of mutation carriers and the “low-risk” group tends to be different, and, in order to avoid bias due to that difference, an aged-match dataset was constructed, and risk analysis was also conducted in this “sub-dataset”.Once the datasets were divided, mammograms proceeded to be pre-processed feature extraction being conducted in a pre-defined region of interest (ROI). Intensity-based features, statistical measures based in absolute pixel value; co-occurrence (GLCM) features , metricsBesides proving the usefulness of the texture features to characterize the difference between low and high-risk women, some interesting conclusions can made from this study considering features’ average values for each group: the textural patterns from high-risk women tend to be coarser and lower in contrast; skewness measure should have negative values for high-risk women; and all the remaining intensity-based features should have higher values for high-risk women.To ensure that a good parenchyma characterization could be done through one projection, the authors made a correlation study between CC-L and MLO-L, and between CC-L and CC-R views for each feature, which provided positive results. A limitation of this study might also be the fact that some of the low-risk women may have the mutation without knowing, which clearly affects features’ discriminative capacity.Li et al.  studied,For size analysis, 2 ROI’s, one with a medium and other with a small size, were directly defined in the center of the larger, pre-defined, ROI, at locations A, B and C. The extracted features were the same that Huo extracted with the addition of a fractal dimension measure . StepwisIn what concerns to ROI size, for location A, the AUC for each individual feature ranged from 0.68 to 0.83 with the lower results being associated to smaller regions of interest. The performance of the LDA approach was of 0.92 in the original ROI, with AUC’s of 0.87 and 0.89 in the medium and smaller ROI’s, respectively. In the size analysis, significance was only achieved for one feature and for the LDA approach between the large and medium ROI and for the fractal dimension between the large and small ROI. The values of the AUC in the LDA approach for region B were substantially lower than the ones observed for region A and, besides that, no statistical significance was observed for this region neither in individual features assessment nor in the LDA approach. Finally, for region C, the only statistical significance was achieved for feature contrast, between large and medium, nonetheless, no statistical significance was obtained in the discriminative capacity between different sized ROI’s.Analyzing ROI location effects, for comparison purposes, only the features selected by the LDA approach for region A were considered. Most of the individual features were statistically significant different between different locations, and the same can be said about the LDA approach that presented AUC’s of 0.92, 0.78, 0.69, 0.84, and 0.79 respectively for each region. A statistically significant decrease was observed in the LDA performance if the ROI was moved away from region A, that is located immediately behind the nipple, which probably explains why most of the approaches in this area of research use this location for feature extraction. The authors point out that the fact that the region immediately behind nipples has the best discriminative capacity may be due to the existence of a dense component in that breast location. The group still states that, in the future, research should extract features from the entire breast and compare the results with the ones obtained with a single-ROI approach that are, besides its limitations, positive.p-value < 0.001) being achieved for all features. The authors proceed to present a figure where a distribution of skewness measure in the population can be observed. From that, it is drawn the conclusion that high-risk women present negative values of skewness, as advanced by Huo in 2000, since these women normally have denser breasts, when compared to lower-risk women, and, in general, high-risk women present lower skewness values than women at the low-risk group. Contrast helps to describe the local tissue variation, and higher values of these features were observed in low-risk women, which leads to the assumption that mutation carriers tend to present texture patterns low in contrast. Besides that, results analysis in terms of feature values lead the authors to state that mutation carriers tend to have coarser textures than low-risk women. Although these conclusions from contrast and coarseness can be made in general, not all women follow this trend. Nonetheless, this study presents itself as another proof that mammographic texture patterns can be successfully used in the field of breast cancer risk assessment.In 2005, Li et al.  aimed toAnother interesting research is the one done by Tan et al. , where tAlthough the previously referred features were computed, they will not be directly used for risk assessment purposes. What is done is that each feature is calculated from the CC view of each breast and then, features that describe the bilateral asymmetry of each individual feature will be computed through the following equations:A set of 180 asymmetry features were calculated and, adding the epidemiologic data, a final set of 183 features was considered. To choose the best features, a forward floating selection method was applied- proposed and described by . Once thClassifier’s performance validation was done using a 10-fold cross-validation methodology and, at each testing step the algorithm outputted a score ranging from 0 to 1. The higher the score, the higher the probability of having an “image-detectable” cancer in the next screening. Feature selection methodology, besides age, selected features from Run-length, Intensity-based, and cumulative projection groups. For classification purposes, using only the first and third subgroups the classifier had an AUC of 0.716 ± 0.020. Considering all the cases, the SVM model correctly predicted 71.3% using a 0.5 score as a decision threshold for a classification between negative/benign cases and positive cases. Some limitations of the developed work must be considered: The fact that the dataset used was produced in laboratory does not reflect the ratio between positive and negative cases in common BC screening programs; the methods used for validation may have resulted in bias and, the fact that the same portion of the dataset was used both for features selection and to evaluate the classifier accuracy may also have resulted in some bias in the process of optimizing the algorithm. Besides that, only asymmetry features were computed, which could lead to some masking effects of the effective texture of the parenchyma.Zheng et al.  advocateThe clustering algorithm used here is a variation of the fuzzy c-means (FCM), which works by giving each pixel/data point a membership degree to each cluster. This degree is related to distance metrics taken between the point and the cluster: the lower the distance, the higher the membership degree. Fuzzy c-means will then, through various iterations, try to minimize the intra-cluster variance while maximizing the inter-cluster variance. Besides breast segmentation for % PD calculation, this algorithm has been used for other purposes. A group of researchers used a variation of the FCM, where the influence of spatial neighbor pixels and similar super-pixels is incorporated in the model, for lesion segmentation on brain and breast MRI as also in mammograms . The ideStill concerning brain MRI, FCM has the potential to be used in a pipeline related to neuro-radiosurgery . The autD, and for structural element size, W, can be considered. The structural elements are centered in the intersection points and will serve as different ROIs for feature extraction, so, each computed feature will have different values across the breast. Although the optimal values for D and W might be different for different regions across the breast, authors considered a fixed and equal value for these components, resulting in a breast that is coated with structural elements. Intensity-based; co-occurrence; run-length; local binary pattern; fractal dimension, and structural features that describe “flow-like structures within the breast” were also considered—the authors provide references for these novel features. In order to look for the optimal W value, approaches with three increasing sizes—small, medium and large—were tested. Each “final value” of the features was defined as the mean value of the said feature across all structuring elements and the association between the computed features and breast cancer was evaluated with a logistic regression classifier with leave-one-out cross validation. Univariate and multivariate analysis were conducted, with feature selection being done in the later one through a forward feature selection methodology applied at each cross-validation loop. Considering univariate analysis and taking all window sizes into account, the average AUC over the GLCM features was of 0.58 ± 0.03, which is better than the one presented by the intensity-based features, being of 0.56 ± 0.05, the same value that was presented by the run-length features. Structural features presented a worst AUC than GLCM features, with a value of 0.57 ± 0.06. Comparing window sizes, the performance seems to be better for small W values, with an average AUC of 0.58 ± 0.07 for a small window against AUCs of 0.57 ± 0.05 and 0.54 ± 0.03 for sizes medium and large, respectively. The feature that presented a higher discriminative capacity was fractal dimension for sizes small and medium, presenting an AUC of 0.69 ± 0.03. In where it comes to multivariate analysis, using a logistic regression, the performance was also better with smaller W sizes and the AUCs values obtained were of 0.85 ± 0.02, 0.81 ± 0.02 and 0.76 ± 0.03 for sizes small, medium and large respectively. All the features outperformed PD% performance and no significance was obtained in the model when PD% was added to the set of features. The lattice-based approach significantly outperformed the single-ROI approach either from the retroaerolar area (AUC = 0.60 ± 0.03) or the central breast region (AUC = 0.74 ± 0.03), despite the W size considered. The results may cause some surprise once, contrary to what was proven by Li in 2004, the central breast region ROI performed better than the retroaerolar area. Given what was discussed about this topic, some conclusion must be drawn: the extraction of features by itself does not result in better discriminative capacity but it is the combination of those features that gives positive results; and W size is important for a better discriminative capacity, with an approach that considers smaller W’s providing better outcomes. Nonetheless, some problems related to the work done here must also be considered: the use of equal values for W and D might be a limitation, since much more combinations could be tested if that condition was not present, what could result in a better discriminative capacity; and PD% calculation was done by considering one of the many possible options available to perform that computation, what could also bias the results.Getting back to Zheng’s research, once % PD calculation was performed, feature extraction could be conducted, and for that, the lattice-based approach needed to be considered. The authors display a grid over the entire breast tissue, where different values for the distance between each intersection point, p-values that assessed the difference between groups at each “prior image” were generated. Given the already explored problem of significantly different ages between high and low risk women, the authors repeated the SVM procedure with two different age-matched groups using a criteria of ±1 year and a criteria of ±3 years. Apart from that, the authors trained and tested three different classifiers and, at each time, they used the features selected through one of the prior images sets. Concerning the results, the AUC increased as the time approached the current evaluation, with the values being 0.666 ± 0.029, 0.710 ± 0.028, and 0.730 ± 0.027. As for feature difference results, different trends can be observed, with features having higher discriminatory capacity across the three “prior” examinations , others having significant discriminatory capacity in one or two of the examinations (run-length), and others with no discriminatory capacity in any of the mammograms (contrast). In line with previous research, one can conclude that although individual features might have good discriminatory capacities, it is the use of a multi-feature ensemble, recurring to a machine learning algorithm, that allows a good breast cancer risk assessment. Considering the predicted risks and defining the midpoint as a threshold, the SVMs had an accuracy of 65.7%, presenting a sensitivity of 46.5% and a specificity of 83.0%. When considering the algorithms trained and tested with the age-matched group, no significant difference in AUCs was observed. This, and the 2013 study are approaches widely different from the common ones since they add time-dependent variables for risk prediction that can be used to develop novel techniques for risk assessment in a personalized fashion. The authors proved a decreasing trend in AUC values from the most recent “prior” evaluation to the oldest but got results that point to the fact that this decrease might not occur linearly.Changes in mammography texture features for breast cancer risk assessment were studied by Tan et al.  in a stuIn this research the authors aimed to avoid the 2013 limitation of the cases not representing a screened population by ensuring that the cases were randomly selected by people who were not involved in model construction, what made the average women age in the “positive” group to be higher than the average in the “negative group” mimicking what happens in screening programs. When analyzing this study, some limitations must be considered: (a) model reproducibility might be affected by different acquisition systems and noise and, therefore, methodologies to reduce acquisition impact must be developed; (b) image features related to local region bilateral asymmetry were not used and might improve the obtained AUC; (c) this model does not include epidemiological/ risk factors which is a flaw, when compared to existing models; (d) the low accuracy values for individual features might be an obstacle to clinical use; (e) an examination of how features varied across the prior examinations was not considered but might be an interesting line of research to pursue.p < 0.001). The same cannot be said about PD%, that was of 16.7% for cancer cases and of 16.2% for controls, but with a p-value of 0.50. The LDA classifier provided an AUC of 0.68 (95% CI 0.64–0.73), while the Volpara methodology presented an AUC of only 0.52 (95% CI 0.47–0.57), this difference was tested and achieved statistical significancy, proving that the classifier is able to extract more useful information than the measures of PD% done by the software. Intensity-based, co-occurrence, gradient and MRELBP features were amongst the chosen ones by feature selection. The six selected features were not all correlated, and some were only correlated to another two selected features, which proves, by the relative positive results obtained in the discrimination between cases and controls, that the LDA classifier could combine information from both correlated and uncorrelated features. Nonetheless, it can be noted that the obtained AUC was relatively low when compared to other studies and the authors pointed that this might happen due to differences in age and ethnicity of the women used in this study. Nonetheless, this study provides a proof that the methodology used for risk stratification in Caucasian women can be used, here, in Asian population at the same time that also provides new features that have a great discriminative capacity in what concerns to breast cancer risk assessment.In 2019, a group of researchers  aimed foStill referring to Asian population, Gandmokar et al. , in the m × m being the size of the patch and c being the number of channels in the patch, to a label patch, M × M being the size of the patch and having one channel per label. Although the image and the label patches may have different sizes, they are centered in the same location. Then, for training purposes, training data will be used to map X to Y. The training data consists in  pairs extracted from random locations across the images that are concerned with this part of the work. The mapping from X to Y does not occur directly, what happens is that abstract feature representations are learned across multiple layers. In other words, the input enters the algorithm and crosses multiple layers, with the output of one layer being the input of the next, where multiple transformations are made and learning is performed, until the last layer is reached, and a final feature representation is obtained. Finally, a classifier will be used to map the final feature representation to Y. For testing purposes, the hypothesis that was trained will be applied to a new image in all possible patches within the said image—using a sliding window. When doing this, a problem can arise: if the predicted output region is bigger than a pixel, there are predictions that may overlap. The problem is solved by calculating the average probability for each class. Mammogram analysis, conducted in a multi-scale fashion, is done by applying the discrete scale space theory, through a Fourier implementation. Algorithm unsupervised architecture consists of four layers: a convolutional layer, a pooling layer, and two final convolutional layers. Going deeper in convolutional architecture, what happens is that the convolutional layer will receive the input data, convolve it, do some transformations, and then send the results as a non-linear activation function to create an activation/features map. The output of the layer can be fed to another convolutional layer or to a pooling layer. The pooling layer was defined based on the goal of the study, once it is invariant to small distortions, but it is highly sensitive to small-scale details. Features are learned for each scale alone and only merged after the learning process.Deep learning, a sub-field of machine learning that can learn directly from a raw input, is also used for breast cancer risk prediction. In 2016, Kallenberg et al.  aimed toA is the automated segmented region and B is the segmentation done by the radiologist. For this dataset, the correlation coefficient had a value of 0.85 (95% CI: 0.83–0.88), the Dice scores for fat and dense tissue were, respectively, of 0.95 ± 0.05 and 0.63 ± 0.19. The algorithm trained for this dataset was used to estimate % PD in the Dutch dataset and the cases had a value of 0.19 ± 0.11, the controls had a slightly smaller value—0.15 ± 0.11. The correlation of % PD between both breasts was of 0.93 (95% CI: 0.92–0.95) and the obtained AUC for differentiating cases and controls was of 0.59 (95% CI: 0.57–0.62). On the other hand, the texture scoring represents the probability of a given pixel to be a part of the cancer class. In order to get one texture score per image, the scores from 500 patches randomly selected across the breast area were averaged. Besides the developed algorithm, two other methods were used and evaluated in the performance of this task; one that is based in multiscale local jet features, and other that uses static histograms. To avoid bias, the algorithm was tested multiple times and outperformed the two well established mammographic texture scores, with an AUC of 0.61 (95% CI: 0.57–0.66) vs. AUCs of 0.60 and 0.56 (95% CI: 0.51–0.61) for local jet and static histogram approaches, respectively. Applying the algorithm to the Dutch dataset resulted in an AUC of 0.57 (95% CI: 0.54–0.61) in the differentiation between cases and controls and produced a correlation for the mammographic texture of 0.91 (95% CI: 0.90–0.92), between left and right breast. Based on their results for correlation and % PD based classification, the authors advoke that this methodology is close to the ones that are present in the scientific community and, based in the outperformance of their methodology in the texture scoring task, they proceed to state that this could be a better alternative to the handcrafted texture extraction that is the current state-of-the-art. One of the downsides of this approach is that the authors assumed that changes in mammography due to cancer occur in a generalized way across the breast, but the opposite can also be true, with texture changes being visible only in restricted areas, and an algorithm that could take this hypothesis into account should be considered as a future development of the considered work.The approach proposed by the authors aims for an overcomplete feature representation, which means that this representation is larger than the input, and resorts to the concept of sparsity. Sparsity can be used in feature representation by: (1) forcing most of the entries to be zero and leaving few non-zero entries to represent the input signal; (2) narrowing the number of examples that activate each unit. In this work, both approaches are combined, leading to, respectively, “a compact encoding per example” and to “example specific features”. Sparse overcomplete approach is robust to noise and, since each example is going to be represent by specialized features, this methodology is designed to unscramble hidden aspects in the data. As it was said, the algorithm will be used in two different tasks and applied in three different datasets: the density dataset contained both MLO and CC views for both left and right breast but, for each woman, only one view was available; the texture dataset contained cancer cases and controls, that were matched both for age and time of the first image available; finally, the Dutch breast screening dataset was composed of cancer cases and healthy controls and the same matching as before was made. As for the classification part, a two-layer neural network was used, with one layer being a previously used and trained convolutional layer, and the other being a SoftMax classifier, meaning that the previously learned parameters will be tuned through a supervised methodology. Broyden–Fletcher–Goldfarb–Shanno algorithm was used as optimizer, and 5-fold cross-validation was performed for a classification task that considers: “pectorales muscle and background”, “fatty tissue”, and “dense tissue” as labels for the density scoring; in what concerns texture scoring, “cancer” and “normal” are the considered labels. Regarding results, for density scoring, the output is a score, from 0 to 1, that represents the probability of a given pixel to belong to the “dense class”. Classification was done by choosing a value of probability to be a threshold, and the best results were obtained with a threshold of 0.75. The results explored the correlation between mammographic % PD done by the authors and by the radiologist, and a performance measure called Dice, that is given by:In 2014, Petersen et al.  sought tBack to 2016, when Qiu et al.  tried toMainly, the reviewed articles, in terms of extracted features, had in common three major groups , and then present many feature-group variations, with spectral analysis being also vastly considered. In what comes to the feature extraction procedure, older papers used a manually single-ROI approach, while more recent ML studies opted to diversify the region analyzed. Some authors used several ROIs across the breast, others segmented the breast in different regions and extracted features from them, and yet, some research consider the entire breast for feature extraction. In papers that compared their approach with the classical single-ROI methodology, authors usually find that their procedure outperformed the use of a unique ROI. This may happen because, considering breast tissue heterogeneity, a single region does not account for this diversity, and therefore a tissue characterization that takes into account the entire breast (or more than one region) appears to be more robust. In terms of classifiers, the papers varied widely, from LDA to SVM, passing through decisions trees and logistic regression. More studies should be performed to assess if there is a classifier that is clearly superior to others. Nonetheless, the LDA approach proposed in the first analyzed paper achieved higher results than the other three classifiers, which would point that this classifier, for this type of tasks might outperformed the others. However, the work proposed by Tan in 2019, that used an LDA, was outperformed by a work conducted in 2013, that used an SVM. As it can be perceived, there is not a clear conclusion to be made in terms of what classifier is the best. Nevertheless, the results obtained by the reviewed papers allowed to conclude that texture analysis along with machine learning algorithms can be correctly employed in risk analysis, either by differentiating risk groups, or by giving a risk score to each patient. Besides understanding that this type of methodology can be used, the research also points out that procedures that consider the entire breast for feature extraction might provide more useful information. While many of these studies were conducted in Caucasian population, the study presented by Tan in 2019 allowed to understand that ML algorithms and texture analysis can also be used, with good outcomes, in Asian populations. The results of the deep learning approaches, although lower than the ones presented by the classical ML approach, appear to be very promising, especially because dismisses the laborious work of extracting handcrafted features, and allows the possibility of automatically finding predictors that better serve the purpose of the study.Given the articles discussed in this paper, excluding the ones that use deep learning, two great future endeavors should be examined: first, considering the substantial differences in age and other risk factors between high-risk and low-risk groups, studies should start using larger matched-groups and consider other risk factors than age, in an approach analogous to what was done in 2019 , but witWhile the development of new methodologies in both machine and deep learning, that suppress the weaknesses discussed in this section, might result in better outcomes, authors should start looking for breast cancer risk assessment in the perspective of transforming these algorithms and methods into real clinical applications.The extensive review performed here allowed to have a general idea of what has been done for breast cancer risk prediction using textural analysis, that is sometimes combined with important risk factors. Although there are some downsides that can be pointed out to research’s methodologies, they serve as a proof of concept that parenchymal texture patterns provide important information about breast cancer risk and should, once methodology’s flaws are overcome, be used in clinical practice, and have a positive effect in millions of women that are diagnosed with breast cancer each year, worldwide.


Menopausal hormone therapy (MHT) increases risk of developing breast cancer (BC), and women are often advised to avoid its use for this reason. In this analysis we examined the size of this effect using data from a large cohort of women attending breast cancer screening in Manchester, UK. We additionally explored the extent to which risk from MHT might be modified by current BMI, early adulthood body mass index (BMI) (age 20 years), and age of first pregnancy. Identifying modifying effects would help enable better estimation of risk associated with MHT for an individual woman. Results indicated that women using combined oestrogen and progestagen MHT were at greater risk than those receiving oestrogen-only MHT. The Relative risk associated with MHT was less for obese women than non-obese women. After adjustment for current BMI, the effect of MHT did not appear to be substantially modified by early BMI or age of pregnancy.Menopausal hormone therapy (MHT) has an attenuated effect on breast cancer (BC) risk amongst heavier women, but there are few data on a potential interaction with early adulthood body mass index (at age 20 years) and age of first pregnancy. We studied 56,489 women recruited to the PROCAS (Predicting Risk of Cancer at Screening) study in Manchester UK, 2009-15. Cox regression models estimated the effect of reported MHT use at entry on breast cancer (BC) risk, and potential interactions with a. self-reported current body mass index (BMI), b. BMI aged 20 and c. First pregnancy >30 years or nulliparity compared with first pregnancy <30 years. Analysis was adjusted for age, height, family history, age of menarche and menopause, menopausal status, oophorectomy, ethnicity, self-reported exercise and alcohol. With median follow up of 8 years, 1663 breast cancers occurred. BC risk was elevated amongst current users of combined MHT compared to never users , risk was higher than for oestrogen only users . Risk of current MHT was attenuated by current BMI  per 5 unit increase in BMI. There was little evidence of an interaction between MHT use, breast cancer risk and early and current BMI or with age of first pregnancy. Approximately 80% of women going through the menopause experience symptoms . MenopauMHT use increases breast cancer risk amongst women in the general population but also in women with increased risk due to familial cancer . Many wo2) negated the impact on risk of high later attained BMI [Increased body mass index (BMI) after the menopause  and weight gain throughout adult life after the age of 20  are consistently associated with increased risk of postmenopausal breast cancer . High boLate age of first pregnancy (after the age of 30 years) and nulliparity are associated with increased risk of breast cancer. Women with first pregnancy after the age of 30 have approximately twice the risk of developing breast cancer compared to women with first pregnancy before the age of 20, and nulliparous women have a 30% increased risk compared to parous women . Risks aHere we sought to examine the association between combined and oestrogen only MHT use and breast cancer risk in a cohort of women from the National Health Service Breast Screening Programme (NHSBSP), in Greater Manchester, UK. We examined whether BC risk associated with these types of MHT are modified by BMI at study entry (age 46–84 years) and/ or early adulthood BMI (age 20), or age at first pregnancy or nulliparity.The Predicting Risk of Cancer At Screening (PROCAS) study has been described in detail elsewhere. In total 57,902 women aged between 46 and 84 years in the National Health Service Breast Screening programme (NHSBSP) were recruited from five areas of Greater Manchester  between October 2009 and June 2015 . RecruitData collection was based on a two-page questionnaire, which was sent to participants with a consent form between their invitations to attend screening and scheduled screening appointment .The self-reported questionnaire gathered information on risk factors for breast cancer including: previous breast cancer diagnosis, breast or ovarian cancer in first and second degree relatives, hormonal risk factors, including age at menarche, oophorectomy and hysterectomy, menopausal status, age at menopause, parity and age at first pregnancy, physical activity levels, alcohol intake and ethnicity. Questions in relation to MHT use included name of preparation, duration of use and when MHT was last used (if no longer on MHT). Women were also asked to record their height, current weight and recalled weight at age 20 as a proxy of early adulthood BMI. Current BMI and BMI at 20 were calculated from these variables. Completed questionnaires were collated by the study team and entered into the study database. n = 895). The primary outcome was diagnosis of a new breast cancer , from entry to PROCAS onwards, as identified through the NHSBSP and the Somerset and North West Cancer Intelligence services. Follow-up (median eight years) was censored at date of breast cancer diagnosis, date of death, date lost to follow-up, e.g., moving out of the area, or date cancer databases were last checked (April 2020). The current analysis excluded women with breast cancer diagnosed prior to study entry  MHT. MHT status was missing for 514 women who were excluded from any further analyses . The analyses included pre/peri and postmenopausal women enrolled in the PROCAS study. Women were considered postmenopausal if they indicated on the questionnaire they had been through the menopause  or reported they had both ovaries removed  or current use of MHT or age at menopause was unknown but age at time of questionnaire completion was 55 years or over as based on criteria defined by Phipps et al. [2. For current BMI, 3943 were unknown or beyond these cut-offs. For early BMI, 6300 values were unknown or beyond these cut-offs. Median value of the cohort was assumed for missing values .We excluded attained BMI and early BMI values which were >60 or <16 kg/m2)).Differences in demographic characteristics and breast cancer risk factors across MHT groups  were tested using one-way ANOVA and Chi-square tests where appropriate. Adjusted comparisons of continuous measure used linear regression (with covariates for age (years), current and early BMI  regression was used to model the relationship between MHT use and diagnosis of breast cancer. Follow-up was censored at date of breast cancer, date of death or date of last follow-up (April 2020). Results were expressed as hazard ratios (HR) and 95% Wald confidence intervals (95% CI).Fully adjusted Cox regression models included the following established risk factors for breast cancer: age (1 year), height (5 cm), BMI (5 units), early BMI (5 units), ethnicity (white/other), age at menarche (1 year), age at first pregnancy  years, parity, age at menopause (1 year) menopausal status , oophorectomy, self-reported exercise (1 h/week) and alcohol (1 unit/week), MHT status , MHT type  and family history (first or second degree). For the fully adjusted analysis, missing values were imputed as the median value of the cohort. We did not include the available mammographic density data in the models since there is some evidence this may be part of the pathway for reduced risk alongside higher early BMI . The mulTo assess whether current and early BMI modified the relationship between MHT and the risk of breast cancer the following two way interaction terms were also included in the fully adjusted models: current/ former MHT use* current BMI *  and curr2) and early (21.6 kg/m2) BMI. The reference group was never use of MHT, current BMI below the median and early BMI below the median. The median was used to dichotomise results and aid with interpretation  MHT type (combined oestrogen and progestagen or oestrogen only)  and (ii)N = 514). The denominator for the main analysis was 56,489 with the endpoint being a new diagnosis of breast cancer   .Demographic characteristics and BC risk factors according to MHT groups are shown in 2), were less likely to have a family history of ovarian and/ or breast cancer (26.6% vs. 28.3%), a lower deprivation score (median: 17.6 vs. 19.4), and younger age of first pregnancy (median 23 vs. 24 years). As expected, former users were the oldest group, also reflected by the higher percentage of participants being postmenopausal. When compared to never users, current MHT users had a higher percentage of oophorectomy (27.5% vs. 5.6%), lower BMI at study entry (median: 26.0 vs. 26.4 kg/m2), were less likely to have family history of breast and/ or ovarian cancer (26.6% vs. 27.1%), and lower deprivation score (median: 17.6 vs. 18.7). All differences cited have p < 0.001 due to large sample size. Women with known MHT status were lighter at entry to PROCAS, more likely to be white, and from less deprived backgrounds than those with unknown MHT status , had longer use of MHT (median: 7 vs. 5 years), higher percentage use of oestrogen only MHT (51.7% vs. 41.0%), lower BMI at entry . Compared with never use, current MHT use was positively associated with breast cancer , while there was little evidence of an association for former users  . The fuln = 2129) , compared with never users. Risk amongst current users of oestrogen only was (n = 2278)  compared with never users  were at increased risk of an ER+ diagnosis BC  .2, 95% CI 1.16 to 1.30; Current BMI was positively associated with breast cancer risk  and ≥ and < median for BMI age 20 years (21.6 kg/m2) (Risk of breast cancer amongst MHT users was stratified for ≥ and < median for current BMI (26.4 kg/m6 kg/m2)  Part C.Higher BMI at age 20 appears to attenuate the effects of high current BMI on BC risk both amongst women who are not using MHT and amongst current MHT users  . Risk waCurrent combined MHT use increased risk amongst women irrespective of their current and early adulthood BMI  Part A. Women who had a late pregnancy had an increased risk of breast cancer  . Late fiRisks were comparable with combined MHT use amongst women with age of first pregnancy <30 years  and > age 30 or nulliparity . However, risks appeared lower amongst current oestrogen only MHT users with age of first pregnancy <30 years  compared to >age 30 or nulliparity  . In this cohort, women with a lower current BMI were more likely to be current MHT users compared to women with a higher BMI. We have confirmed that current use of MHT increases breast cancer risk with excess risk mainly attributed to use of combined MHT. Higher early adulthood BMI had a small reduction in risk across never, former and current MHT groups. Late first pregnancy or nulliparity increased risk across never, former and current use of MHT groups. Neither BMI at age 20 or late first pregnancy or nulliparity had a specific modifying effect on the breast cancer risk related to overall MHT use. Observations of lower risks with oestrogen only MHT amongst women with high early BMI and early age of first pregnancy are interesting and require further study in larger cohorts.Previous studies have reported that women with higher BMI were less likely to have used MHT ,15,16. POur findings of increased breast cancer risk with current combined MHT, particularly with ER+ cancers concur with those found by the Collaborative Group . We did In these analyses we observed that higher current BMI attenuates the BC risk associated with overall current (combined and oestrogen only) MHT use. The attenuation of BC risk associated with oestrogen only MHT amongst currently heavier women has previously been reported . PostmenThe stratified analysis in the current study suggests the attenuation that effects of oestrogen only MHT amongst currently heavier women is mainly seen in women who were heavier at an early age, and are not seen in formerly lighter women. The confidence intervals of these associations are quite wide due to small numbers of current users. However this observation raises the possibility that the apparent attenuation of oestrogen only MHT breast cancer risk amongst currently heavier women may be related to early rather than current BMI effects. Previous reports summarised in the collaborative overview did not examine the effects of early BMI.2 (top 25% centile) neither attained adult BMI nor adult weight gain was associated with breast cancer risk [2) [A previous analysis within the PROCAS cohort reported that for women with early adulthood BMI >23.4 kg/mcer risk . The obscer risk ,22,23. Acer risk . Reduced2 top % centile2 top % centilerisk [2) ,27 and drisk [2) . Also, irisk [2)  could inrisk [2)  and exprrisk [2) . Higher risk [2) . The synrisk [2) . In addirisk [2) .A prospective study among 483,241 women and 7656 breast cancers studied whether MHT-associated BC risk is modified by life course patterns of BMI. The study reported that current users of MHT who reported being overweight at the ages of 7 and 15 (self-identified as being heavier than their peers) were at higher risk  compared to never users of MHT who were overweight as young. However, risks were higher amongst current MHT users who remained at normal weight throughout adult life  or who had gained weight  . The autWe found that late age at first pregnancy increased risk amongst never, current and former MHT users. Age at first pregnancy does not modify risk associated with overall MHT use and breast cancer. Oestrogen only MHT appeared to be associated with a greater risk amongst women with a late first pregnancy or nulliparity compared to women with age of first pregnancy <30. There was no modification of the risk of combined MHT. Risk of late pregnancy relates to a prolonged duration of undifferentiated state of the mammary tissue . SynergiOur stratified analysis aimed to study the independent and combined interactions of current BMI, early BMI, MHT use and BC risk. Also whether these interactions differed with combined and oestrogen only MHT. Our sample size limits the ability to study all of these relationships with sufficient power. The observed trends in higher early weight and early pregnancy reducing the risk of oestrogen only MHT breast cancer requires further investigation in larger cohort or consortium studies. Strengths of this analysis are that it was conducted in a large UK population. Many confounders associated with breast cancer risk were taken into consideration. The independent effects of both current BMI and early adulthood BMI were elicited in the models by including BMI at two different time points. Additionally, detailed information regarding MHT use and type were collected and breast cancer diagnosis updated on a regular basis. Sensitivity analysis showed that the models were consistent. All information used in this analysis were self-reported, including current weight, height, weight at age 20, age at first pregnancy, name of MHT, how long MHT was used for and when MHT was stopped for former users. It is well known that there is a bias of underreporting weight and over reporting height . HoweverImplications for practice for this research include that clinical risk assessment of suitability of a woman to commence on MHT, should include consideration of their current BMI and potentially early adulthood BMI and the type of MHT to be prescribed. The findings of this study support recommendations to maintain a healthy weight across the life course. Breast cancer risk is similar among women with higher current BMI who never used MHT and women with lower BMI who use MHT. The smaller increase in risk with MHT amongst heavier women should not deter these women from losing weight. The highest BC risk is seen amongst current MHT users with a high current BMI, especially those with a low BMI at an early age. Women at increased weight taking MHT will have higher risks of other MHT associated adverse effects including venous thromboembolism, stroke and endometrial cancer . Weight Future studies need to investigate the associations between current and early BMI and age of pregnancy and MHT associated breast cancer risk. These studies could determine whether there are specific BMI ranges where these effects occur and whether the range is different within different ethnic groups.Combined oestrogen and progestagen MHT was associated with the highest BC risk. This risk was not modified by early or current BMI and age of pregnancy. Exploratory analysis amongst oestrogen only MHT users showed an attenuation of risk with early BMI greater than or equal to the median compared to less than the median and with age of first pregnancy less than 30 years compared to equal or greater than age 30 or nulliparity which require further study. Identifying characteristics which modify a woman’s MHT associated BC risk will allow their individual risks and benefits to be assessed and appropriate prescription of MHT to manage troublesome menopausal symptoms.


To measure the baseline prevalence of cardiovascular disease (CVD), its modifiable and non-modifiable risk factors in breast cancer patients, and determine their association with adjuvant treatment decision-making.From 2016 to 2017, 2,127 women newly-diagnosed with breast cancer were prospectively recruited. Participants’ cardiovascular biomarkers were measured prior to adjuvant treatment decision-making. Clinical data and medical histories were obtained from hospital records. Adjuvant treatment decisions were collated 6–8 months after recruitment. A priori risk of cardiotoxicity was predicted using the Cardiotoxicity Risk Score.Mean age was 54 years. Eighty-five patients had pre-existing cardiac diseases and 30 had prior stroke. Baseline prevalence of hypertension was 47.8%. Close to 20% had diabetes mellitus, or were obese. Dyslipidaemia was present in 65.3%. The proportion of women presenting with ≥2 modifiable CVD risk factors at initial cancer diagnosis was substantial, irrespective of age. Significant ethnic variations were observed. Multivariable analyses showed that pre-existing CVD was consistently associated with lower administration of adjuvant breast cancer therapies . However, presence of multiple risk factors of CVD did not appear to influence adjuvant treatment decision-making. In this study, 63.6% of patients were predicted to have high risks of developing cardiotoxicities attributed to a high baseline burden of CVD risk factors and anthracycline administration.While recent guidelines recommend routine assessment of cardiovascular comorbidities in cancer patients prior to initiation of anticancer therapies, this study highlights the prevailing gap in knowledge on how such data may be used to optimise cancer treatment decision-making. Cardiovascular disease (CVD) is emerging to rival cancer recurrence as a leading cause of death in women with early breast cancer . The incUntil very recently, guidelines for cardiovascular risk assessment in adults with cancer had been scarce –10. PartA priori risk of cardiotoxicity following adjuvant therapy was also predicted.Through an inception cohort study, we measured the prevalence of CVD, its modifiable  and non-modifiable (family history) risk factors, prior to administration of anticancer therapies in women newly diagnosed with breast cancer in a multiethnic setting. Importantly, amidst the lack of evidence-based standards, we assessed whether the baseline presence of CVD or presence of multiple risk factors of CVD was associated with adjuvant treatment administration, to gain an insight on whether these factors currently influenced cancer treatment decision-making in routine clinical practice. The study population comprised women who were newly diagnosed with stage I up to stage IV breast cancer between January 2016 and December 2017. Four different hospitals were selected across Klang Valley, an urban conglomeration in Malaysia, to allow recruitment of a demographically diverse sample of patients; National Cancer Institute , Kuala Lumpur Hospital , University Malaya Medical Centre  and Subang Jaya Medical Centre . In total, 2,127 females with a first time histological-diagnosis of breast cancer were consecutively recruited within 6-8 weeks of diagnosis, prior to initiation of cancer therapy. Patients with recurrent breast cancer were excluded.Ethics approval was obtained from the relevant institutional committees. Written informed consent was obtained from all participants. Ethical principles according to Helsinki’s declaration were observed throughout the conduct of the study.Participants were recruited during their hospital visits. Data on ethnicity , age at diagnosis, highest-attained education  and medical history , other comorbidities and medications) were collected via face-to-face interviews. Data on smoking status and family history of premature heart disease (history of coronary heart disease before the age of 60 years in first-degree relatives) were also gathered. All medical and drug histories were verified with medical records.2). Obesity was recorded if the calculated BMI was 30 kg/m2 and above. Systolic and diastolic blood pressure readings were taken using automated sphygmomanometers. Hypertension was recorded based on self-report or if patients were on hypertensive medication and/or had a repeatedly elevated BP reading; systolic blood pressure >140 mmHg or diastolic blood pressure >90 mmHg (measured at least twice).During the on-site screenings, height and body weight were measured. Body mass index (BMI) was calculated as weight divided by height squared  < 1.3 mmol/L . DiabetePatients who were newly detected with abnormalities in their blood pressure or serum glucose/lipid levels were provided with referral letters for further management.Data on tumour characteristics were obtained from hospital records , distant metastasis at initial diagnosis (yes or no), TNM stage , progesterone and oestrogen receptor status  and human epidermal growth factor receptor 2 status ).Patients’ baseline data on risk factors of CVD were accessible through the medical records to all treating physicians prior to adjuvant treatment decision-making. Data on administration of type of surgery, radiotherapy, chemotherapy, chemotherapy regimen, endocrine therapy, type of endocrine therapy, targeted therapy and type of targeted therapy were retrieved from the hospital records within 6–8 months after recruitment.Individual risk of cardiotoxicity was subsequently calculated using the Cardiotoxicity Risk Score, a risk-stratification tool introduced by the Mayo Clinic  that allCategorical variables were summarised using frequency and percentages. Continuous variables were expressed in mean ± standard deviation. Overall prevalence of cardiovascular comorbidities with 95% confidence intervals (CIs) was estimated for participants, followed by stratification by age and ethnicity.Among patients with non-metastatic disease, multivariable logistic regression analyses were used to determine the association between preexisting CVD, family history of premature heart disease and clustering of risk factors of CVD (presence of ≥2 modifiable risk factors) with administration of individual modalities of adjuvant treatment. Models were adjusted for age (years), ethnicity , type of hospital , tumour size , lymph node status , oestrogen/progesterone receptor status , human epidermal growth factor receptor 2 status , tumour grade , adjuvant chemotherapy administration , adjuvant radiotherapy administration , adjuvant endocrine therapy administration  and trastuzumab administration .Complete case analyses were performed, and multiple imputation was employed as a form of sensitivity analysis.In this large cohort of multiethnic Asian women  with breast cancer, the mean age at diagnosis was 54 years (11.6). Mean tumour size at presentation was 3.6 cm (2.9). Overall, 22.4% presented with stage I disease, while 38.9%, 27.9% and 10.8% presented with stage II, stage III and stage IV breast cancers, respectively. A vast majority (70%) had oestrogen receptor positive tumours. Among 1,876 patients with non-metastatic breast cancer, 63.1% received chemotherapy , 63.1% received adjuvant radiotherapy, while approximately 20% of patients (126/618) with human epidermal growth factor receptor 2 (HER2) positive tumours received trastuzumab (not shown). Missing values ranged between 0.2% (diabetes mellitus) and 12.1% (tumour grade).n = 75), with 31 patients having suffered a previous myocardial infarction  except smoking and family history of early-onset coronary heart disease  . Of these, a majority comprised coronary heart disease (farction . Thirty farction . Close t disease . Signifi < 0.05) . ClusterIn the multivariable analyses examining the association between cardiovascular profile and adjuvant treatment administration, presence of pre-existing CVD was significantly associated with lower odds of receiving chemotherapy (adjusted odds ratio (OR): 0.32, 95% CI: 0.17–0.58). None of the patients with pre-existing CVD received trastuzumab. Radiotherapy or endocrine therapy administrations were also less likely in women with pre-existing CVD, albeit not reaching statistical significance . InteresUsing the Cardiotoxicity Risk Score, approximately one fifth of breast cancer patients were predicted to have low risk of cardiotoxicity (19.9%), while a majority appeared to have high (33.7%) or very high risks (29.9%) of developing cardiotoxicities . PatientResults from sensitivity analyses using multiple imputation did not change the study inferences.a priori risks of cardiotoxicity.Findings from this multi-ethnic setting revealed a high baseline burden of modifiable risk factors of CVD among women presenting with breast cancer, including in those who were very young. While pre-existing CVD appeared to be consistently associated with lower administration of adjuvant breast cancer therapies, presence of multiple risk factors of CVD at initial cancer diagnosis did not seem to influence adjuvant treatment decision-making. This, however, must be considered in light of our findings that a high burden of cardiovascular comorbidities at baseline coupled with administration of anthracyclines puts women with breast cancer at high et al [The baseline prevalence of hypertension, diabetes and dyslipidaemia among the multiethnic breast cancer patients in this study appears to be substantially higher than previously reported , 15–17. et al  through The ethnic differences in prevalence of risk factors of CVD, and predicted risks of cardiotoxicity among women with breast cancer in the present study to some extent may explain the disparities in survival following breast cancer between the Malay, Indian and Chinese patients that we had previously reported from Southeast Asia . This isIn the current study, we have shown that presence of pre-existing CVD such as myocardial infarction and stroke appeared to be consistently associated with lower administrations of adjuvant therapies. Our observation is in keeping with results from a large population-based study in Canada, which also showed that women with breast cancer who had pre-existing myocardial infarction, congestive heart failure, arrhythmia or cerebrovascular accidents were less likely to receive the recommended treatment for their cancer including chemotherapy  and radiotherapy  .Our findings that clustering of modifiable cardiovascular risk factors such as hypertension, diabetes, obesity and dyslipidaemia was not associated with administration of most adjuvant therapies corroborate the notion that these risk factors are presently not factored into cancer treatment decision-making . This hoa priori risks of developing cardiotoxicity, the Cardiotoxicity Risk Score that we had used to estimate the above is yet to undergo external validation [However, there is presently an unmet need for accurate cardiovascular prognostic prediction rules for cancer patients . For inslidation . It mustper se, and other cardiac outcomes in this inception cohort study due to a lack of resources.The strengths of the current study include our prospective design where cardiovascular comorbidities and their influence on adjuvant treatment administration have been examined in an unselected cohort of newly diagnosed breast cancer patients, in which the biomarkers were measured at baseline prior to adjuvant treatment decision-making. Nonetheless, while we are in a position to follow-up patients over long term to measure the association between the individual cardiovascular risk factors and survival outcomes, we are unable to measure the incidence of cardiotoxicity While many professional guidelines including those from the American Society of Clinical Oncology  and the From a regional perspective, this study highlights the pressing need to accelerate the establishment of coordinated partnerships between the oncology and cardiology specialities to improve cardiovascular outcomes following breast cancer in the low- and middle-income settings. Although it is currently unclear whether aggressive management of modifiable risk factors of CVD may reduce the risk of cancer therapy-induced cardiotoxicity, it is conceivable that optimal control of these factors may lower the risk of cardiovascular events leading to improved overall survival following breast cancer , 21–22. The authors have no conflict of interest to report.The authors have no funding to declare for this work.


Pooled crude odds ratios of breast cancer in ever-users of oral contraceptives was 1.01 , compared with never-users. There was no significant increase in risk among premenopausal women , postmenopausal women , or nulliparous women . Oral contraceptives do not appear to increase the risk of breast cancer among users. However, OC use before a first full-term pregnancy or using them longer than 5 years can modify the development of the breast cancer.Despite numerous studies evaluating the risk of breast cancer among oral contraception users, the effect of oral contraceptive on developing breast cancer remains inconclusive. Therefore, we conducted a systematic review of literature with meta-analysis in order to quantitative estimate this association. The bibliographic database MEDLINE and EMBASE, and reference lists of identified articles were searched, with no language restrictions, from the start of publication to August 2010. We performed a reanalysis and overall estimate of 79 case-control studies conducted between 1960–2010, including a total of 72,030 incidents, histologically confirmed cases of breast cancer and 123,650 population/hospital controls. A decrease was observed in cancer risk in OC users before age 25 years . However, the use of OCs before the first full-term pregnancy had a significant increased risk of breast cancer (OR, 1.14, 1.01–1.28,  Breast cancer (BrCa) is the most frequently diagnosed cancer and the leading cause of cancer death in women worldwide. It accounts for 23% (1.38 million) of the total new cancer cases and 14%  of the total deaths in 2008. About half the BrCa cases and 60% of the deaths occur in economically developing countries [In Western countries, the incidence has increased BrCa over the last 50 years, and these increases were dramatic in both the 1980s and the 1990s ,4. The sThe etiology BrCa is complex. Susceptibility is influenced by both environmental and genetic factors. In 5–10% of the cases, BrCa has a hereditary basis. BrCa susceptibility is generally inherited as an autosomal dominant with limited penetrance. In 15–30% of patients from high-risk families, BrCa is caused by a germline mutation in the BRCA1 or BRCA2 gene . Many riOral contraceptives (OCs) among the most widely used methods are of effective and reversible family planning . Many quDespite the large number of studies in this area, no consensus is present regarding the exact effect of OCs on the risk of BrCa ,17. In tWe systematically searched the PubMed (Medline) and EMBASE bibliographic databases (from their commencements to August 2010), with no language restriction, to identify studies investigating the relation between OC uses and the risk of BrCa developing. For computer searches, we used the following MeSH terms or keywords: ’breast cancer’, ‘breast malignancy’ or ‘breast neoplasm’, combined with ‘oral contraceptives’, ‘contraceptive agents’ or ‘birth control pill’. In addition, we examined the references of identified articles, previous review articles and meta-analysis, and other relevant publications to identify further pertinent studies ,20,21,22We selected studies in a two-stage process. Initially, a preliminary selection of articles was made after the electronic searching of titles. When the citation was relevant or when title/abstract was not sufficient for inclusion/exclusion, full-texts were retrieved and evaluated. Secondly, full manuscripts of selected citation and the additional items derived from a review of literature of selected papers and review articles were obtained. Final inclusion/exclusion decisions were made after independent and duplicate examination of the full manuscripts.Studies were included in the systematic review if they were a case-control design  and published as original articles that permitted assessment of an association between the use of OCs and risk of BrCa. Studies eligible for meta-analysis fulfilled the following criteria: including at least 100 women with incident invasive BrCa, and presenting sufficient detailed results to calculate the odds ratio (OR) and 95% confidence interval (CI). In the case of multiple published studies based on the same population or subpopulation, we took into account only the most recent published report from the study with the longest period of recruitment, unless the earlier reports contained information not available in subsequent reports or represented a comprehensive form of this study. The selection procedure for studies included in meta-analysis is presented in The following data were extracted for each study: (a) clinical and methodological study characteristics such as last name of first author, publication year, country in which the study was performed, name of the study, years of data collection, number of cases and controls subjects, and source of cases; (b) information on use of OC (exposure)—ever/never use, duration of use at least above 5 years, use before first full-term pregnancy, and use in different age groups . To summarize the results associated with menopausal status, we adopted the following definitions: (a) premenopausal women or women younger than 50 years and (b) postmenopausal women or women older than 50 years. To assess the risk of developing breast cancer associated with the use of oral contraceptives with different composition (high- versus low formulation), we classified the available studies (based on the period of recruitment) to the following time ranges: 1966–1985 and 1986–2010 ,24,25,26If the study presented data on breast cancer only in subpopulations according to different ethnicity, age group, parity, etc., we conducted a cumulative estimate of these data for the entire study population. When the breast cancer event rate, OC use rate or control recruitment rate was expressed as a percentage, the number of patients was calculated by multiplying the rate by the total number of women in the relevant group. When data were expressed in a way that did not allow exact figures to be obtained, these data were not included in the meta-analysis.The Newcastle–Ottawa Scale (NOS) score was employed to evaluate the study quality of observational studies, with a maximum score of 9, of which 0–3, 4–6, and 7–9 scores were considered as low, fair, and high quality, respectively . With thMeta-analysis of summary statistics from individual studies was performed utilizing the Medical Package program of STATISTICA 11.0 software . For each study, we constructed separate two-by-two (2 × 2) contingency tables to calculate the odds ratios (OR) and 95% confidence intervals (CIs), cross-classifying OC users and occurrence of breast cancer. The Mantel-Haenszel test was calculated to assess the association between OC use and cancer. An OR of one indicates that the odds of having breast cancer are the same in the case group and the control group; an OR greater than one indicates that the odds of having breast cancer in the case group are greater than in the control group; an OR of less than one indicates that the odds of having breast cancer in the control group are greater than in the case group. Because about one-third of the studies did not present adjusted ORs, only crude ORs were used in the primary meta-analysis. Meta-analyses combining the ORs across studies were conducted using the DerSimonian–Laird random effects model . The ranDue to the high heterogeneity of the studies, the following subgroups were also analyzed: case-control studies of the period of recruitment into the study before 1986/case-control studies of the period of recruitment into the study after 1986; premenopausal women or women younger than 50 years/postmenopausal women or women older than 50 years/women under 25 years old; nulliparous women; ever use of oral contraceptives before first pregnancy; and ever use of oral contraceptives for longer than 5 years.For all the analyses, forest plots were generated to display results, whereby diamonds represent study-specific odds ratios and 95% CIs for individual studies are represented by horizontal lines.To analyze the publication bias, the Begg and Mazumdar and Egger tests were performed. The meta-analysis methodology was based on the guidelines of DerSimonian and Laird  and HiggOur systematic review included 121 population- and hospital-based case-control studies that analyzed the relationship between use of oral contraceptives and histologically confirmed breast cancer. Of these, 79 that met inclusion criteria qualified for the meta-analysis ,108,109.Of the 79 case-control studies, 17 reported a significant positive relationship for OC use and risk of BrCa ,105,109,p = 0.69) (p > 0.05). In addition, we performed separate analysis for assessment of relationship between risk of breast cancer and use of older oral contraceptives formulations containing higher doses of estrogen and oral contraceptives using low-estrogen formulations, based on the periods of recruitment to the study. p = 0.71) and cOR = 1.04 , respectively. Similar results were obtained for the matched control groups in the analyzed subgroups , 1.01; 95% CI, 0.95–1.07;  = 0.69) . The Begubgroups .p = 0.44), compared with controls that had never used OCs (p = 0.90)   . Moreove = 0,05) .p = 0.85), although the use of pills before a first full-term pregnancy has a significant effect on increased risk of breast cancer , compared with never-use (p = 0.02) (As shown in ever-use . Further = 0.02) .Despite numerous epidemiological studies that have attempted to assess the risk of breast cancer among oral contraception users, the effect of oral contraceptive use on the developing breast cancer remains controversial. Several meta-analyses and a very large pooled analysis have addressed this issue. Romieu et al. , in metaEvaluation of carcinogenic risks associated with changes in the formulations of OC over the past decades, especially of lowering estrogen content, also remains unclear. In our meta-analysis, based on accepted criteria, we found no increased risk of breast cancer associated with use of low-potency/low estrogen-dose oral contraceptives, a finding which was comparable with earlier high-potency/high-dose preparations. Our results confirmed earlier reports of Collaborative Group on Hormonal Factors in Breast Cancer  that amop < 0.001) [In our meta-analysis, we performed only separate analysis for assessment of relationship between risk of breast cancer and use of older oral contraceptives formulations containing higher doses of estrogen and oral contraceptives using low-estrogen formulations, based on the periods of recruitment to the study, creating two subgroups—the periods of recruitment 1966–1985 and 1986–2010. However, we have not identified different oral contraceptives, because only 14 of the 79 studies included in the meta-analysis reported the type of oral contraceptive used. The analysis of more than half of these studies showed that no statistically significant differences were found in the incidence of breast cancer between cases and controls, depending on the use of different oral contraceptives ,62,65,94< 0.001) . On the < 0.001) . In turn< 0.001) . The hig< 0.001) .Results of our meta-analysis did not always disagree with those from previous reports suggesting that certain groups of women have an increased risk of breast cancer related to OC use. We failed to show appreciable differences in the risk of breast cancer between women who had taken OC and those who had not when we sub-grouped women according to menopausal status. Analysis of cancer risk in premenopausal women showed a slight, statistically non-significant increase  and a marginal decrease in postmenopausal women . In contrast, Romieu et al.  showed tThe controversy also concerns breast cancer risk in young women taking the pill. In our investigation, among women who started using OC at an early age (before 25 years), we saw a slight decrease in risk of breast cancer . By contrast, the meta-analyses that analyzed risk factor for age-related use reported that an early start in taking birth control pills was associated with the risk of cancer. In the Oxford study , the autThe results of our analysis  do not confirm previous papers that reported an increased RR in the range of 1.21–1.30 for oral contraception use by nulliparous women ,21,22. OWe revealed in our meta-analysis that OC use longer than 5 years leads to a modest, but significant increased risk of breast cancer . Romieu et al.  also shoVery important discoveries regarding the etiology of breast cancer concern the mutations in the BRCA1/2 genes. However, none of the works in this meta-analysis have tested cases and controls in this regard. Indeed, only Ursin et al., and Marchbanks et al., mentioned in their discussion sections the possible influence of BRCA1 and BRCA2 mutations on the occurrence of breast cancer with the simultaneous use of OC ,94. It sIn the period analyzed by us (up to 2010), however, there were several papers dealing with the topic of mutations in the BRCA1 and BRCA2 genes. Brohet et al., showed that BRCA1/2 mutation carriers who had ever used oral contraception had a significantly increased risk of breast cancer . Longer use of OC, especially before the first full-term pregnancy, was also associated with an increased risk of breast cancer for both BRCA1 and BRCA2 mutation carriers  . Still, In our meta-analysis, significant heterogeneity between studies is observed, although the specific cause of its occurrence across individual studies could not be easily explained.We were aware of a number of limitations that may affect the interpretation of our results. First, as with all meta-analyses, the validity of the results is limited by the conduct and reporting of the studies from which the data were extracted and pooled. In order to reduce the possibility of publication bias, we limited the search to studies published in indexed journals, and we did not search for non-published studies. Nevertheless, lack of these reports as well as the possibility of not reaching out to all publications in this topic may affect the value of the results . SecondlThe limitations should also take into account other coexisting factors, such as the use of different oral contraceptives, as well as the presence of unexplored genetic factors, such as BRCA1 and BRCA2 mutations. We are, hence, currently preparing a meta-analysis in which we will examine the effect of BRCA1 and BRCA2 mutations on the occurrence of breast cancer.A further limitation lies in the fact that most of the studies in this meta-analysis only look at the effect of contraception on breast cancer in general. Moreover, only some studies assessed the risk of breast cancer after using different oral contraceptives. These articles were too small a research area for the analyses we undertook. Furthermore, in all of the analyzed studies, participants were not tested for the presence of mutations in the BRCA1 and BRCA2 genes.Finally, as with other meta-analyses, given that many studies are not included in the evaluation of additional variables or analyzed differing confounders, we were not able to adjust our overall analysis for these risk factors. Thus, we only used crude (not adjusted) study estimates in our meta-analysis. If there were strong effects from confounding factors, the estimates included in the meta-analysis might be biased. A prospective study needs to be conducted to confirm our findings.In conclusion, the findings of our meta-analysis and systematic review suggest that oral contraceptives do not appear to increase the risk of breast cancer among women who are taking these preparations. However, OC use before a first full-term pregnancy or using them longer than 5 years can modify the development of the breast cancer.


There is growing international interest in adopting a risk-based approach to breast cancer screening, where an individual’s risk would inform screening practices. It has been suggested that primary care will contribute to the delivery of this service by conducting risk assessment and providing primary prevention advice. The aim of our review was to understand what primary care providers think and feel about performing these tasks by examining their attitudes and typical activity in clinical practice (routine behaviours). The results suggest that primary care providers mainly assess breast cancer risk by collecting family history information but feel less comfortable advising on risk-reducing medications. Primary care will need to proactively assess breast cancer risk for women to get the most benefit from risk-based screening and prevention. To promote risk assessment and prevention activities, improved education/training and changes to resources  will be necessary.Implementing risk-stratified breast cancer screening is being considered internationally. It has been suggested that primary care will need to take a role in delivering this service, including risk assessment and provision of primary prevention advice. This systematic review aimed to assess the acceptability of these tasks to primary care providers. Five databases were searched up to July–August 2020, yielding 29 eligible studies, of which 27 were narratively synthesised. The review was pre-registered (PROSPERO: CRD42020197676). Primary care providers report frequently collecting breast cancer family history information, but rarely using quantitative tools integrating additional risk factors. Primary care providers reported high levels of discomfort and low confidence with respect to risk-reducing medications although very few reported doubts about the evidence base underpinning their use. Insufficient education/training and perceived discomfort conducting both tasks were notable barriers. Primary care providers are more likely to accept an increased role in breast cancer risk assessment than advising on risk-reducing medications. To realise the benefits of risk-based screening and prevention at a population level, primary care will need to proactively assess breast cancer risk and advise on risk-reducing medications. To facilitate this, adaptations to infrastructure such as integrated tools are necessary in addition to provision of education. Population based screening programmes aim to detect asymptomatic cancers at an earlier stage to reduce mortality rates and the need for aggressive treatments associated with long term morbidities . A substAdopting an alternative risk-based approach to breast cancer screening has the potential to improve the benefit to harm ratio . The devA key benefit of risk estimation is the ability to identify women at increased risk, affording them the opportunity to benefit from preventative strategies. There are two strategies that have proven benefit in reducing breast cancer risk. The use of selective oestrogen receptor modulators and aromatase inhibitors, commonly referred to as chemoprevention or risk-reducing medication, have been shown to reduce breast cancer incidence ,15. FurtAs the first point of healthcare contact for the general population, primary care has been repeatedly identified as the most opportune setting to conduct breast cancer risk assessment ,19. SecoAs the likely roles of primary care in delivering risk-based screening and prevention will be risk assessment and provision of primary prevention advice including prescription of risk-reducing medication, it is important to assess acceptability of these activities. Acceptability is increasingly being recognised as an important component of the feasibility of complex interventions in guidance documents such as the Medical Research Council (MRC) framework .A previous review identified a considerable evidence base related to the acceptability of primary care involvement in risk-based screening and prevention . This reThe present systematic review aimed to provide a robust and in-depth examination of acceptability beyond identification of barriers and facilitators. It achieves this by employing the theoretical framework of acceptability which recognises the value of participant-reported evaluations of acceptability in addition to behavioural assessments . Howeverratings of acceptability  by primary care providers with respect to (1) breast cancer risk assessment and (2) primary prevention advicethe performance of routine behaviours by primary care providers regarding (1) breast cancer risk assessment and (2) primary prevention advicesources of variation in acceptability and behavioursSpecific objectives were to summarise the evidence base on:The protocol of this systematic review was pre-registered in PROSPERO (CRD42020197676) and follows the reporting guidelines detailed in the PRISMA statement . The proThe following electronic databases were searched: MEDLINE, EMBASE, CINAHL Plus, PsycINFO (each up to 10 July 2020) and ProQuest Dissertations & Theses Global (up to 26 August 2020). Databases were searched from 1989 as the first breast cancer risk model incorporating multiple breast cancer risk factors was published in this year . Search Healthcare professionals who provided primary care services. To account for variation in professional roles between healthcare structures in different countries, samples reported as being primary care providers were regarded as such. In ambiguous cases, authors were contacted to clarify whether their samples provided primary care services in line with the World Health Organisation’s definition .Studies conducted with both primary and secondary care providers were only included if it was possible to separately identify those findings relevant to primary care providers.Data had to be reported about risk assessment and/or providing primary prevention advice in the context of breast cancer. Studies focusing on cancer risk or primary prevention whereby data specific to breast cancer could not be extracted were excluded.(a)Acceptability defined as anticipated or experiential cognitive and emotional responses. Studies had to report one or more of the following outcomes using quantitative methodologies: attitudes, opinions , beliefs, feelings , barriers or facilitators.(b)Routine behaviours defined as typical or regular activity in clinical practice. Frequency of behaviours reported in a specific timeframe were not eligible for inclusion. Hypothetical clinical scenarios/vignettes or reflections on previous clinical cases were ineligible as these methods ascertain the action taken in a specific situation which may not be indicative of routine behaviours.Either or both of the following:Studies: Full empirical articles of any quantitative design published in the English language. Grey literature including PhD theses, dissertations and unpublished research were eligible for inclusion. Additionally, baseline surveys of intervention studies designed to improve breast cancer risk assessment behaviours or provision of primary prevention advice were included.Studies were included in the review if they met the following criteria:The search results were downloaded into Endnote and duplicates were removed. The library was then uploaded to Rayyan  to complFollowing full text review, detailed information on study characteristics , sample characteristics  and outcome data relevant to the objectives were extracted by the first author for all eligible articles. A second reviewer (RH) verified the data extraction by independently extracting primary outcome data for 50% (15/29) of eligible articles.The Mixed Methods Appraisal Tool (MMAT) was deemed most suitable for quality assessment due to its demonstrated reliability and inclusion of quality criteria specifically designed to appraise quantitative descriptive study designs such as surveys . CriteriIn line with MMAT recommendation, two authors (SB and DF) discussed which quality indicators were most important to consider for each criterion listed and following this a coding scheme was devised and agreed upon. All studies were appraised using the criteria for quantitative descriptive designs to assess the quality of the survey design and outcome measures which were of most interest to the review. A 50% rate of response was a priori regarded as satisfactory for avoidance of nonresponse bias, in line with response rates observed in previously published provider surveys . Two autA meta-analysis was deemed inappropriate as studies varied widely in outcomes, measurement scales and study populations. Instead, a narrative synthesis was conducted with findings tabulated . The outTo aid interpretation and allow meaningful patterns to be identified, outcomes were categorised into broader themes depending on content see . InitialThe searches identified 6750 articles, of which 3164 remained after duplicates were removed . A totalPrimary care providers’ perceptions of responsibility with respect to tasks implicated in breast cancer risk assessment and primary prevention were examined in several studies. Taking a family history was overwhelmingly perceived as a primary care responsibility  and reassuring low-risk patients (46.0–67.7%) . Very loThe discussion and collection of breast cancer family history was reported to be a common task . Rates wProfessional specialty and training level were found to be associated with reported behaviours. A higher proportion of providers specialising in obstetrics and gynaecology reported using risk assessment tools in comparison to family and internal medicine providers ,45. AddiOverall, there was higher endorsement of barriers for primary prevention than risk assessment. The most prevalent barriers for providing primary prevention advice were concern and discomfort prescribing risk-reducing medication and insufficient education/training, in line with barriers to risk assessment . FurtherPrimary care providers endorsed all facilitators to a relatively high degree  . Only onOverall, study quality was poor . A detaiThe results from this systematic review indicate that primary care providers typically take a reactive role in breast cancer risk assessment that is predominantly focused on collection of family history and provision of support following identification of increased risk. Reported use of multi-factorial risk assessment tools was low. Primary care providers reported higher discomfort and lower confidence with respect to prescribing risk-reducing medications when compared to risk assessment. However, few providers reported beliefs suggestive of doubts about the evidence base underpinning risk-reducing medications. Insufficient education/training and perceived discomfort were amongst the most commonly endorsed barriers reported for both activities. The strongest facilitators for offering risk-reducing medication related to availability of provisions such as clear guidelines and tools to facilitate identification of suitable patients. Professional background, training and country were identified as sources of variation in acceptability and behaviours. The methodological quality of included studies was generally poor and common limitations were high nonresponse rates and use of non-standardised outcome measures.Previous systematic reviews have consistently identified primary care providers’ lack of knowledge about breast cancer risk assessment and management as a significant barrier to engagement ,24,25,62Prior to this review, widespread reticence by primary care providers to discuss and prescribe risk-reducing medications has been recognised ,63,64,65This review has been the first to investigate sources of variation in acceptability and behaviours. Examination of routine behaviours illustrated that primary care providers infrequently report using multi-factorial risk assessments such as the Gail or Tyrer–Cuzick models ,8. NonetProfessional background was also associated with outcomes. Providers specialising in women’s health issues reported feeling more comfortable with respect to both risk assessment and primary prevention as evidenced by greater reported use of quantitative risk assessments and fewer negative views about the risks of risk-reducing medications. These perceptions are likely to be the result of specialised knowledge acquired through additional training. For instance, an understanding of the diagnosis and clinical management of hereditary breast and ovarian cancer syndrome is considered essential for obstetrician/gynaecologists . TherefoThe present review has identified methodological biases present in the primary studies. Firstly, high rates of nonresponse were observed in a significant proportion of the studies. Primary care providers who respond to surveys might have more positive views of breast cancer risk assessment and primary prevention than non-responders which may lead to overestimations of acceptability. Furthermore, there was a reliance on convenience sampling procedures in many studies. Outcomes were not assessed using standardised measures with demonstrated reliability and validity. In addition, older studies included in the review may be a poor reflection of current clinical practice given the significant advances made in breast cancer risk assessment and management in recent years. Primary prevention outcomes tended to be assessed in more recent studies which is in line with risk-reducing medications being a relatively new option in comparison to risk assessment. Nonetheless, findings were largely consistent across studies suggesting that methodological limitations with sampling and publication date did not unduly affect the overall conclusions.Substantial heterogeneity across included studies was evident and therefore a meta-analysis was not possible. To allow meaningful patterns to be identified via narrative synthesis, the research team decided how outcomes were categorised and to some extent this process was subjective. Nevertheless, analytical processes were reviewed in reflective team meetings to achieve consensus and ensure a rigorous and robust synthesis.Additionally, a wide range of values were observed for some outcomes indicating uncertainty about the average values presented. This is likely to be the result of heterogeneous outcome measurements, as well as differences in samples included. Consequently, caution is warranted when drawing conclusions about the precision of estimating strength of outcomes.Finally, and perhaps surprisingly, the review did not identify any studies investigating primary care providers’ perceptions of discussing health-related behaviours within the context of breast cancer risk reduction. There is, however, an evidence base focusing on cancer risk more generally. Inclusion of this literature may have provided a more comprehensive understanding of primary care’s perceived role in primary prevention than was possible in this review.The present review suggests that provision of education/training will be necessary but not sufficient to facilitate primary care involvement in breast cancer risk assessment and primary prevention. The findings on facilitators and routine behaviour indicate that adapting infrastructure and providing prompts to utilise available resources are essential to increase the likelihood of primary care providers routinely conducting both activities. For instance, the integration of risk assessment and management tools into practice software or access to web-based applications would facilitate the desired behaviours, as has been demonstrated for cardiovascular risk assessment and management . SeveralNursing staff were underrepresented in the included studies. Decision makers have suggested that nurses could assume increased duties in risk assessment and management to support implementation of risk-based screening and prevention . In relaAdditionally, there is a clear need for more research using populations outside the US to understand the feasibility of primary care assessing and managing breast cancer risk in different healthcare contexts. Future primary studies would benefit from assessing similar outcomes across studies using measures with demonstrated reliability and validity. Wider and more representative sampling frames should be used to obtain better coverage of the target population. Furthermore, recruitment strategies that build personal connections with potential participants such as using physician recruiters ought to be considered to reduce nonresponse rates .Within the context of implementing risk-based breast cancer screening and prevention, the findings of this review suggest that primary care providers are more likely to accept an increased role in breast cancer risk assessment compared to advising on risk-reducing medications. Adaptations to infrastructure will be necessary to promote enactment of breast cancer risk assessment and management behaviours in addition to provision of education. To fully realise the benefits of risk-based breast cancer screening and prevention, guidelines will need to be reviewed to ensure promotion of a proactive approach to breast cancer risk assessment in primary care.