IGF1R) single nucleotide polymorphism.Preeclampsia has been inconsistently associated with altered later life risk of cancer. This study utilizes the Nurses’ Health Study 2 (NHS2) to determine if the future risk of breast and non-breast cancers in women who experience preeclampsia is modified by carrying a protective variant of rs2016347, a functional insulin-like growth factor receptor-1  breast cancer, and non-breast cancers.P = 0.04, with interaction term P = 0.06. For non-breast cancers as a group, women carrying a T allele had an HR 0.76, 95% CI: 0.53–1.08, P = 0.12, with interaction term P = 0.26.Women with preeclampsia had no change in risk of all breast, HR+ breast, or non-breast cancers when not considering genotype. However, women carrying at least one T allele of rs2016347 had a lower risk of HR+ breast cancer, HR 0.67, 95% CI: 0.47–0.97, IGF1R rs2016347 had a reduced future risk of developing HR+ breast cancer, and a reduced but not statistically significant decreased risk of non-breast cancers suggesting a possible role for the IGF-1 axis in the development of cancer in these women.This retrospective cohort study found that women with preeclampsia who carry a T allele of The online version contains supplementary material available at 10.1007/s10549-022-06789-9. IGF1R) gene variant rs2016347 is also considered [IGF1R single nucleotide polymorphism (SNP) might immediately improve breast cancer risk assessment and personalized screening, as well as suggest new breast cancer prevention strategies.Preeclampsia develops in 5–12% of all pregnancies depending on race, ethnicity, and other risk factors within the population and is a leading cause of maternal/fetal perinatal mortality as well as being linked with later life maternal cardiovascular disease (CVD) [Preeclampsia is characterized by the development of high blood pressure after the 20th week of gestation plus the presence of proteinuria or other end organ dysfunction, which distinguishes it from gestational hypertension, the other major component of the broader class of disorders known as hypertensive disorders of pregnancy (HDP) , 6. The The systemwide sequelae of preeclampsia have long been noted and debated; and while preeclampsia is still primarily considered a placental disorder its exact pathogenesis remains unclear. Postulated systemic impacts include vascular ischemia, immune dysfunction, oxidative stress, and dysregulated angiogenesis, along with derangements of the insulin-like growth factor-1 (IGF-1) axis , 19. WitIGF1R rs2016347 exhibits increased mammary gland involution, a histologic predictor of reduced breast cancer risk [IGF-1 plays an essential role in normal breast development; yet upregulation of the IGF-1 axis and increased IGF1R expression appear to be associated with the development of breast cancer –29. A pocer risk .Prebil in 2014 first reported that women with HDP whose germlines possess the T allele of rs2016347 had significantly lower mammographic density, a clinically recognized proxy for breast cancer risk . This waGiven the above inconsistent population-based evidence, we turned to the large and well-studied longitudinal Nurses’ Health Study 2 (NHS2) with its specific data on preeclampsia, HR+ status, and non-breast cancer outcomes to confirm the potentially important modulating impact of rs2016347 T allele inheritance on a woman’s future risk of developing cancer.https://nurseshealthstudy.org/participants/questionnaires) including preeclampsia history and necessary covariates such as age at entry, ethnicity, Body Mass Index (BMI), parity, age at first birth, age at menarche, family history of breast cancer, diet, physical activity, and smoking history. Covariates were modeled as continuous except for ethnicity, age at menarche, smoking history, and family history of breast cancer which were categorical with nominal categories as described in Table IGF1R SNP of interest, rs2016347, on 6577 participants; details on the genotyping process are provided below. A flow chart of study numbers is presented in Fig. This retrospective cohort study was completed within the NHS2, which enrolled 116,430 female registered nurses in 1989 with ages at entry ranging from 25 to 42. The original detailed entry questionnaire collected extensive data as did subsequent follow-up biennial questionnaires  were determined by self-report with subsequent validation by NHS2 staff who obtained consent to review medical records. HR (±) breast cancer status was determined from medical records and/or pathology reports. Participants with HR− breast cancer or unclear HR status were excluded entirely from the HR+ breast cancer analyses, and the low number of documented HR− cases precluded their analysis as a subgroup.n = 2,560), post-traumatic stress disorder , endometriosis , nephrolithiasis (n = 452), venous thromboembolism (n = 295), and ovarian cancer (n = 113). Of note, in the initial genotyping attempts were made to genotype all cases of breast cancer at that time.In order to obtain genetic data for the NHS2, blood samples were collected from 29,611 and buccal smears from 29,392 participants from which DNA was extracted using Qiagen PureGene DNA Isolation Kits . Initial genotyping results of rs2016347 by the NHS2 was available on 6434 (7.4%) of study participants utilizing at least one of five different DNA analysis platforms as previously reported , 45. Then = 50) and had DNA available for genotyping but had not been initially genotyped had their DNA sent to the Brigham and Women’s Hospital/Harvard Cohorts Biorepository for rs2016347 genotyping. Similarly, 138 participants with preeclampsia and non-breast cancers with available DNA had genotyping requested. Because of limitations in DNA quantity/quality, only 31 of the 50 breast cancer specimens and 112 of the 138 non-breast cancer specimens were successfully genotyped, resulting in a final total of 6577 participants with rs2016347 genotyping. Among women with preeclampsia, these included 163 for all breast cancers, 126 for HR+ breast cancers, and 154 for non-breast cancers. The non-breast cancers consisted of 25 cases each of uterine cancer and melanoma, 24 cases of thyroid cancer, 15 of colon cancer, 14 with ovarian cancer, with all other cancer types contributing less than 10 cases each. Genotyping of rs2016347 among study participants was in Hardy Weinberg Equilibrium (P = 0.21), with an overall T/G allele frequency of 0.52/0.48.In an effort to increase power for the genetic analyses, participants with preeclampsia who subsequently developed invasive breast cancer  and 95% confidence intervals (CI) for the association of preeclampsia with cancer outcomes were estimated from Cox proportional hazards models using person-years of time as the time scale. Person-years began accumulating for each of the years the participant remained in the study from the year of their first birth, and stopped accumulating upon the year of the final questionnaire (2015), year of their cancer diagnosis, loss to follow-up, or death, whichever occurred first. Participants that had a breast or non-breast cancer diagnosis after their first pregnancy but before study entry were included in the study as dates of cancer diagnosis were deemed adequately reliable to assure proper temporality. All participants with a breast or non-breast cancer diagnosis prior to preeclampsia history were excluded from the study. Breast cancer models are adjusted for age, ethnicity, BMI, parity, age at first birth, smoking history, family history of breast cancer, physical activity and diet ; non-breast cancers for age, ethnicity, BMI, smoking, and diet. Data for alcohol intake was inadequate for utilization as a covariate. Proportional hazards assumptions for the models were evaluated using standard model-based methods . Cox proCharacteristics of study participants by preeclampsia exposure status are presented in Table Of participants in the analyses, 3133 (3.6%) had a history of breast cancer, 2149 (2.5%) had a history of HR+ breast cancer, and 3459 (4.0%) had a history of a non-breast cancer.Comparisons of the characteristics of participants with all breast cancers, HR+ breast cancer only, and non-breast cancers compared to participants without are provided in Supplemental Tables S1–S3, respectively. Participants with all breast or HR+ breast cancer were older at entry, had lower parity, higher age at first birth, stronger family history of breast cancer, increased smoking history, decreased physical activity, and a slightly better diet than those without breast cancer. Participants with non-breast cancers were older, more likely to be White, have a smoking history, a higher BMI, and did less physical activity than participants without.P = 0.62. When looking at the subgroup of HR+ breast cancers, the risk decreased somewhat, HR 0.88; 95% CI, 0.77–1.01, P = 0.06. Assessment of the risk of developing a non-breast cancer revealed an HR 0.91; 95% CI, 0.83–1.01, P = 0.08. These results are summarized in Fig. Women with a history of preeclampsia had no increase in risk of developing breast cancer when not considering genotype, HR 0.97; 95% CI, 0.87–1.09; The association of rs2016347 genotypes with the outcomes of breast, HR+ breast, and non-breast cancers did not reveal any significant association in participants without preeclampsia, as noted in Table IGF1R SNP rs20162347 genotype, although the impact on HR+ breast cancer was suggestive. Analyses stratified by preeclampsia history do demonstrate that genotype may in fact further influence risk. When looking at the risk for all breast cancers, carrying at least one T allele resulted in an HR 0.87; 95% CI, 0.62–1.24, P = 0.45. However, when looking at HR+ breast cancers only, carrying a T allele was protective, HR 0.67, 95% CI, 0.46–0.97, P = 0.04, with an interaction term for Preeclampsia*T allele of HR 0.68, 95% CI, 0.45–1.02, P = 0.06. Efforts to quantify the relative excess risk due to interaction (RERI) required removal of the additional targeted genotyping (31 of the 126 cases of HR+ breast cancer), and was suggestive of substantial interaction with an additive RERI of −0.46; 95% CI, −1.13–0.22, and multiplicative RERI of 0.69; 95% CI, 0.43–1.10. There were too few adequately documented HR− genotyped participants for meaningful analysis.There was no statistically significant change in risk for cancer in women with a history of preeclampsia when not considering P = 0.12, with an interaction term for Preeclampsia*T allele HR 0.77, 95% CI, 0.49–1.21, P = 0.26. Results of the impact of rs2016347 for the dominant model are provided in Fig. When looking at the risk of developing a non-breast cancer in women with preeclampsia, carrying at least one T allele resulted in an HR 0.76, 95% CI, 0.53–1.08, P = 0.48, HR 0.99, 95% CI, 0.84–1.17, P = 0.92, and HR 1.03, 95% CI, 0.78–1.36, P = 0.86, respectively, as noted in Fig. Risks for all breast cancer, HR+ breast cancer only, and non-breast cancer demonstrated no impact from carrying a T allele in women without a history of preeclampsia, with HR 0.95, 95% CI, 0.82–1.10, IGF1R T allele of rs2016347. Without considering inheritance of this functional IGF1R SNP, these women exposed to preeclampsia, much like those in the CTS initially evaluated without IGF1R genotyping, would otherwise appear to show no significant reduction in their later life breast cancer incidence , if present at all [IGF1R rs2016347 genotype differences [Results of this NHS2 study of predominantly White (> 90%) women demonstrate a 33% lower risk of developing HR+ breast cancer in those with a self-reported history of preeclampsia who also inherited the protective s modest –20%, if IGF1R T allele of rs2016347 that causes a significant reduction in IGF1R mRNA expression observable in multiple normal organ and host tissues [Our current NHS2 findings, as well as those from the prior CTS study, now strongly suggest that the degree of breast cancer protection from preeclampsia depends on a mechanistic interaction between two population exposures: preeclampsia and inheritance of the functionally blunted  tissues . Moreove tissues –30. At a tissues –55. Our  tissues , as well tissues .IGF1R rs2016347 T allele inheritance in populations statistically powered to assess ovarian, colorectal and uterine cancer development.Many population studies are unable to discern HR+ from HR− breast cancer outcomes, and this likely explains why a recent UK Biobank cohort analysis which lacked data on HR status was unable to show a significant lowering of overall breast cancer risk in women with HDP carrying the rs2016347 T allele . HoweverA limitation of this study may have been that preeclampsia exposure was determined by self-report, yet studies of maternal recall of preeclampsia have found that misclassification is minimal; and a validation study of preeclampsia in the NHS2 demonstrated a positive predictive value of 89% –59. As wWhen comparing genotype-preeclampsia interactions on breast cancer risk between NHS2, UK Biobank and CTS cohort studies, it is important to note that rs2016347 allele frequencies are very similar in each of the 3 cohorts. However, differences in their population risk factors may play a role in their varied outcome risks. All three cohorts are largely White and match in most characteristics except age, with participants in the NHS2 being 20 years younger at study entry and thus potentially more likely to have developed premenopausal breast cancer . LikewisIGF1R rs2016347 have a 30–40% lower risk of developing HR+ breast cancer, pointing to the need to consider incorporating this exposure information (preeclampsia history and IGF1R rs2016347 genotyping) into risk assessment models and personalized breast cancer screening recommendations. Importantly, this breast cancer protective interaction has yet to be validated in non-White women who have the highest exposure rates to preeclampsia and may be more likely to develop premenopausal HR− breast cancer. Of interest, this NHS2 study also observed a nominal reduction in non-breast cancer incidence, supporting the statistically significant UK Biobank cohort findings of reduced non-breast cancer development in women with both exposures. Taken together, these cohort studies strongly implicate a mechanistic role of the IGF-1 axis in cancer development, supporting targeted prevention strategies designed to blunt IGF-1 axis expression.This retrospective NHS2 cohort study in largely White non-Hispanic women validates earlier CTS findings and confirms that women with preeclampsia who have inherited the common T allele of Supplementary file1 (DOCX 21 KB)Below is the link to the electronic supplementary material.


Apart from a few such known mutations, the genetic risk for breast cancer is highly polygenic, including various molecular pathways as for any common complex disease.,Strong genetic predisposition to breast cancer due to rare high-risk functional mutations in susceptibility genes, such as ,,Genome-wide association studies (GWASs) have identified close to 200 common genetic susceptibility variants that explain around 18% of the familial relative risk for breast cancer.International Journal of Epidemiology, Chatterjee and co-workers present a coherent and extensive evaluation of calibration and discrimination of a risk prediction model, integrating a 313-variants PRS and traditional risk factors, in 15 European-ancestry cohorts from six countries .In this issue of the ,,,,,,Chatterjee and co-workers emphasize the importance of evaluating absolute risks in order to determine the clinical utility of risk models.,In fact, achieving high discrimination with normally distributed biomarkers, modestly associated with disease, for any common complex disease appears fundamentally implausible.In relation to their modelling, Chatterjee and co-workers projected that an improved PRS, achievable through larger GWASs, could lead to better risk stratification, i.e. in combination with other risk factors, it could achieve an AUC of slightly over 0.7. The authors emphasized that the iCARE models predict the risk of overall breast cancer, rather than specific subtypes. The risk models presented were also aimed at the general population and did not adequately capture the risk for women with strong family histories or carrying high-risk mutations. Thus, incorporation of subtype-specific risk predictions could result in improved identification of women who would benefit most from specific interventions. The potential additional role of epigenetic markers in the prediction models of breast cancer is also under active investigation, for example in relation to DNA methylationIn addition, recent large-scale work has indicated that the breast cancer PRS strongly modifies breast cancer risk in the high-impact mutation carriers.In relation to genetic risk and population health, we should also remain vigilant in public communication and keep in mind that the knowledge of risk, phenotypic or genetic, might not actually effectively change human behaviour.M.A.-K. is supported by a research grant from the Sigrid Juselius Foundation, Finland.M.A.-K. conceived the idea. T.W. and M.A.-K. drafted and subsequently revised the text together. Both co-authors approved the final version. M.A.-K. acts as the guarantor for the work.None declared.


Early prediction of breast cancer is one of the most essential fields of medicine. Many studies have introduced prediction approaches to facilitate the early prediction and estimate the future occurrence based on mammography periodic tests. In the current research, we introduce a novel machine learning tool for the early prediction of breast cancer. Three basic resources are used to identify the most essential risk factors; including the BCSC (Breast Cancer Surveillance Consortium) dataset, a medical questionnaire, and multiple international breast cancer reports. The BCSC dataset has been normalized and balanced; consequently, the questionnaire and the medical reports are analyzed in order to define the degree of importance and a potential weight factor of each risk factor. These weights are used to scale risk factors and then the optimizable tree-based ML model is trained using the balanced weighted risk factors datasets. Three balanced versions of the BCSC dataset are used; oversampled, down-sampled and mixed datasets. Each risk factor has a weight  assigned based on a mathematical modelling of the questionnaire and the international breast cancer reports. The experiments are applied on the weighted and non-weighted versions of the database, and they indicate that the performance increases significantly by using the weighted version of the risk factors. The tests prove that the down-weighting of the non-essential risk factor increases the accuracy and reduces errors. The overall accuracy of the weighted balanced datasets reaches 100%, 95.8% and 95.9% for down-sampled, oversampled and mixed datasets respectively. Weighting the risk factors of the BCSC dataset improves the performance by increasing the accuracy and reducing the false rejection and false discovery rates for all versions of balanced datasets. The weighting approach can also be used to improve the estimation score of breast cancer by scaling the individual scores of risk factors.  Nowadays, data analysis is one of the most developing fields of computer science due to the fact that the size of datasets is exponentially increasing day after day. Cancer prediction is one of those fields, using data analysis and Machine Learning (ML) algorithms for the estimation of cancer  . ML techniques can improve the performance of cancer prediction, the estimation accuracy of which has increased significantly (15%-20%) due to using the ML algorithm during the last years . Breast cancer prediction itself can be used to define those potentially high-risk women and guide them to improve their lifestyle, avoiding future therapy and costs .Cancer prediction is generally based on risk factors . Half of the cancer cases are caused by some known risk factors . For breast cancer, many risk factors, such as early menarche, late menopause, obesity, age at first birth, and hormone therapy affect the exposure period of breast tissue to hormones that lead to cancer .In the field of breast cancer predictions, some studies used the logistic regression approaches , while other studies used neural networks . Other data mining algorithms were used like decision trees , Naïve Bayes methods , Support Vector Machines , Random Forests (RF) , optimization algorithms , etc.For breast cancer estimation research, many datasets, like the Breast Cancer Surveillance Consortium dataset , consisting of 280,660 records, had been used in many pieces of research . Another international dataset is the Breast Cancer Information Management System (BCIMS) dataset, consisting of 16,000, cases  and was used by many studies such as Hou et al. (2020) and Zhong et al. (2020). Some other researchers collected their datasets from specialized medical centers or hospitals .Shieh et al., (2016) proposed a breast cancer prediction model using the information of the clinical and polygenic risks. The Bayes estimation and conditional logistic regression models are used together to study the common effect of ordinary and polygenic risk factors on the future risk of breast cancer. The researchers used 486 cases of the BCSC dataset and found that the prediction accuracy increased from AUC=0.62 to AUC=0.65 after adding the polygenic risk to the model. They concluded that 18% of the cases were classified as high-risk cases in the common model, while it was only 7% for the ordinary risk factors model.Li and Sundararajan (2018) applied several ML approaches for the prediction of breast cancer. They used only 10,000 cases and eight risk factors of the BCSC dataset. SVM and Bayes classifiers were used for the final risk estimation and got accuracy results of 96.6% and 91.26% for SVM and Bayes classification, respectively.In 2020, Rajendran et al., (2020) used the supervised ML algorithms on imbalanced class data for the prediction of breast cancer on the BCSC dataset. In order to perform balancing, they used three approaches: Synthetic Minority Oversampling, under-sampling and fusion of both techniques. They also used Bayes classifier, Bayes networks, Random Forests (RF), and random trees as classifiers. The best accuracy they obtained was 99.1%, under False Positive (FP) equals 21%. The problem with this research was that they used only 10,252 instances after applying the balancing techniques; besides that, the results showed low sensitivity of 78.1%.A new model for predicting breast cancer in Chinese women had been introduced by Hou et al., (2020). They used 7,127 cases of the Breast Cancer Information Management System (BCIMS) dataset and chose specific risk factors based on the fact that they must be known and collected by the same measurement techniques. Consequently, 10 risk factors had been chosen and different prediction models were used, like RF, deep neural networks DNN and XGBoost. They got an accuracy of 72.8 for both DNN and RF, while the XGBoost accuracy was 74.2%.The Evaluation of many ML classifiers for the prediction of breast cancer under incomplete datasets was suggested by Teja et al., (2020). They evaluated the RF, Logistic Regression (LR) and custom Neural Network (NN). The Area Under Curve (AUC) was used for the performance evaluation on the BCSC dataset. AUC achieved 0.645, 0.634 and 0.649 for LR, RF and NN respectively.Ming et al., (2020) collected a breast cancer prediction dataset from Geneva University Hospitals. Their dataset included 112587 individuals and 14 variables. They applied different ML algorithms  and obtained accuracy between 84.3% and 88.9%. However, the dataset variables related not only to breast cancer but also to other tissues, so that more risk factors needed to be included.Most previous studies did not consider the nature of the used breast cancer dataset. Each dataset has some properties that must be understood in order to get a proper accurate estimation as mentioned by the BCSC dataset (2021). For example, the BCSC dataset needs to consider the “count” as a very important variable in order to achieve correct results. Besides that, the BCSC dataset is unbalanced, so it needs a balancing step before any estimation model. The research aim is to develop a new tool for predicting breast cancer based on BCSC risk factors. We have taken into account the “count” variable for good estimation. In addition to that, balancing has been applied as a pre-processing step. The last new option that have been done is the weighting mechanism, in which a weight number of each risk factor is assigned in order to enhance the performance. The following paragraphs will contain the used materials and the proposed approach in detail. Finally, the results and discussion section will be introduced.DatasetIn the current research, the BCSC dataset is used. It includes 280و660 records and 12 risk factors, which are described in Proposed systemThe proposed risk-estimation model of breast cancer is described in i=Risk_factori/max(Risk_factori)                               (1)Risk_factori=1,2,…,M Where M is the number of risk factors. The normalization step makes the value of each risk factor ranging from 0 to 1.The second step is balancing, in which the dataset must be manipulated in order to achieve the balancing between target categories. The original BCSC dataset has two target categories . While the “1” category has only 3.32%, the “0” category has 96.68% of all samples, which is why the BCSC is extremely unbalanced. The minimal appearance of the minor category in the unbalanced datasets leads any classifier to generate inaccurate predictions due to the inappropriate training . So for the original BCSC dataset, any classifier can produce more than 96% accuracy only if it has recognized all “0” class samples, even if all “1” class samples have been incorrectly estimated as “0”. To solve this problem, the so-called balancing mechanism is applied. Three different balancing approaches were applied. The first is oversampling, in which the samples of the minor class are duplicated many times so that their percentage increases. Duplication will enhance the training significantly. The second approach is down-sampling, in which some of the majority-class samples are removed until decreasing its percentage to the required value. As for the last approach, duplicating the minor class samples and eliminating some of the majority class samples are performed until achieving the desired balance.To perform the third step, the weighting algorithm is applied. To achieve a good, accurate weighting, two branches were taken. First, a questionnaire of the risk factors listed in the BCSC dataset was created. The aim of this questionnaire was to establish medical knowledge, so it was sent to 40 specialist physicians working in the field of cancer treatment and diagnosis. Afterward, the results of the questionnaire were analyzed to define the expert’s opinion of the impact of each risk factor in the final score of cancer risk. The second branch of this study investigated the international medical reports from which the recent discussions of breast cancer risk factors were obtained to define the impact from another point of view. Machine Learning Model selectionAfter getting the final impact (weight) of each risk factor, the final step is the selection of the ML model. Multiple ML prediction algorithms are available, but the optimization tree model is chosen due to its ability to tune the hyperparameters, deal with missing or noisy data, and handle redundant attributes values . The decision tree algorithm first considers all samples of the dataset as the root node. The basic challenges are selecting the best attribute to be the root node and then deciding to split the node into all attributes and select the one with the best split performance. Decision trees actually compute the Information Gain (IG) as illustrated in Equation 2  across all possible attributes and then choose the attribute with the lowest IG. This means that the selected attribute is the one that separates the training samples the best.Where H(T) is the entropy of the parent node of the tree T, H(T|a) is the entropy of the child node a (attribute a), k is the number of subsets generated by each split, pi is the percentage (probability) of class i in the node T, pr(i|a) is the percentage of class i given that the split child (attribute) is a.Now, for the optimizable tree classifier, three different parameters are tuned. These parameters are the criterion (the attribute selection measure), the splitter (the split strategy) and the maximum depth of a tree.Prediction Tool DesignAfter getting the final optimizable decision tree classifier, the tool is built based on the trained model. The tool is designed using MATLAB App designer. The preprocessing of the risk estimation datasetPreprocessing of the risk estimation dataset includes two basic steps, which are normalization and balancing. For the oversampling approach, the “1” minor class has been duplicated five times until its percentage became 14.64%, while the majority-class percentage became 85.36%. For the down-sampling approach, we minimized the majority-class samples by a factor (3.524 times) until getting a 10.78% percentage for the minor class and 89.22% for the majority one. For the last approach, we duplicated the minor class samples and eliminated some of the majority class samples until getting 17.1% and 82.9% for the minor and majority classes, respectively.Risk factor weighting resultsqi) of each risk factor is defined based on Equation 3:In order to obtain the risk factors weights, the results of the questionnaire are analyzed and the questionnaire-based degree of importance , hormone therapy.Age, menopause, density and race are the risk factors with the largest medium-risk levels.Hispanic, breast procedure (brstproc), and surgical menopause have the lowest risk levels.Factors with a high DOI (more than 0.4) are nrelbc, age, hormone therapy.Some other risk factors like age at first birth, menopause, density, Body Mass Index (BMI), last mammogram before the index mammogram (lastmamm) and race have medium DOI (between 0.3 and 0.4) areOther risk factors with a low DOI (less than 0.3) are Hispanic, brstproc and surgical menopause.Ri was calculated according to Equation 4:The international medical reports, on the other hand, indicate other opinions. So, we concluded the information about risk factors, and then this information was compiled and classified according to the number of times the factors were mentioned in the list of the essential risk factors (Ess_Numi), then within the list of the secondary-risk factors (Sec_Numi), and the risk degree DOI(4)Where n is the number of medical studies that have been analyzed and the denominator (4) is the maximum DOI. Based on the analysis of the previous studies and previous breast cancer medical reports, like Breast Cancer Facts & Figures (2019), Cancer Facts & Figures (2020), Breast cancer risk factors (2009) and Breast Cancer Risk and Prevention (2019), we supposed that 90% of the essential risk factor effect and 10% percent of the secondary risk factors will be summed to constitute the final DOIR value.Fi) is inferred from the medical questionnaire-based degree of importance  instead of a scalar risk score.Khozama S. proposed the idea and design of the work. Mayya A. collaborated in editing the layout of the paper and coding some parts of the software. Both authors contributed in formulating the mathematical equations, writing the manuscript, and testing the software.This work was supported by Pázmány Péter Catholic University, Budapest, Hungary. The research project has been partially supported by the European Union, co-financed by the European Social Fund through the grant EFOP-3.6.3-VEKOP-16-2017-00002.The current study deals with secondary public data (breast cancer risk factors), so it doesn’t need from approval of scientific body. This paper is a part of the dissertation submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in Pázmány Péter Catholic University, Budapest, Hungary.Dataset for this study is completely public; therefore, no ethical approval is required. Data collection and sharing was supported by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (HHSN261201100031C), available at: http://www.bcsc-research.org/.The study is not registered in any registering dataset.The authors declared no conflicts of interest.


The “Ballet after breast cancer” study sought to investigate the feasibility and acceptability of a 16-week classical ballet intervention for breast cancer survivors, delivered face-to-face and/or online.Breast cancer survivors were recruited to take part in 2 × 1-h ballet classes per week for 16 weeks. Primary outcomes of feasibility and acceptability were assessed according to rates of enrolment and attendance and participant feedback via questionnaire. Secondary outcomes included quality of life (QOL), upper-body disability, shoulder range of motion (ROM), muscular strength, aerobic capacity, and physical activity levels. Associations between rate of attendance and changes in secondary measures were explored.Thirty-one participants  enrolled in the program. Twenty-nine women commenced the intervention [53.3 ± 10.8 years (Mean ± SD)], attending 77.6%  (Mean [95% CI]) of sessions. Based on these rates of enrolment and attendance, and participant feedback, the program was deemed feasible and acceptable to participants. Significant improvements in shoulder ROM and reductions in sedentary behaviour were achieved. Participants also reported improvements in physical capacity and psychological, social, and cognitive wellbeing.The “Ballet after breast cancer” program, delivered face-to-face and/or online, was feasible and acceptable to breast cancer survivors. Improvements in shoulder ROM achieved doing ballet were pertinent given the adverse effects of upper-body morbidity on breast cancer survivor QOL. Improvements in physical activity behaviour and perceived benefits to wellbeing also support the use of ballet to mitigate QOL impairment after treatment.The physical demands and the fun, creative, and social characteristics of ballet promote improvement across multiple domains of health and wellbeing. Ballet shows promise as an activity to improve QOL and increase long-term engagement in health-promoting physical activity after breast cancer.The online version contains supplementary material available at 10.1007/s00520-022-07420-9. Breast cancer is the most diagnosed malignancy worldwide. In 2020, female breast cancers represented 11.7% of global cancer diagnoses and accounted for 29% of cancer diagnoses in Australian women –5. With One such strategy is the promotion of dance for health. Dance is a fun, creative, and social activity, which demonstrates potential to improve multiple aspects of health and wellbeing. The positive effects of dance have been observed across healthy and clinical population groups, including cancer patients and survivors –18. DancClassical ballet, like other styles of dance, is an attractive and enjoyable form of physical activity. Ballet has additional characteristics, such as a unique use of the upper-body, which sets it apart as an ideal style of dance for breast cancer survivors. Individuals with treatment-related UBM such as lymphedema, pain, or restricted shoulder ROM are poised to benefit from exercises promoting positive strength/endurance, proprioception, and mobility adaptations to the upper body. The characteristic ballet “port de bras” or “carriage of the arms” .The study was prospectively registered on the Open Science Framework (osf.io/j87vf).d = 0.5) of physical activity on QOL, with 85% confidence, a total sample of 31 participants would be necessary. Accounting for 15–20% attrition, a target sample of 40 participants was deemed suitable for the study  (Mean [95% CI]) of scheduled ballet classes, thus exceeding the feasibility threshold of 75% attendance. G1 and G2 attended 88.3%  and 73.0%  of sessions, respectively.Participant quotations italicised), 80.9% of participants reported the class frequency as “the right amount” and necessary to “maintain momentum” and achieve meaningful improvements in skill and physical fitness  and was expecting these ballet classes to be a similar level of challenge. However, the classes didn’t challenge me and I didn’t feel I was exercising”.According to evaluation responses  of participants rated enjoyment and perceived benefit of ballet classes as ≥ 4/5. The classes were enjoyed as an opportunity to “One participant experienced light headedness during a face-to-face class, whilst wearing a surgical mask as a COVID-19 precaution. No other adverse events were reported. Minor exercise modifications were provided for participants with joint discomfort or restricted shoulder ROM, where necessary. For example, holding arms in first (below shoulder height), instead of fifth (overhead) position, or replacing jumps with a Plié (Knee bend) and Relevé (Rise).p = 0.04). Participants increased their weekly duration of MVPA by 279.2 min , but this change was not significant (p = 0.39). Unadjusted p-values indicated significant reductions in upper-body strength (p = 0.01) and borderline significant improvements in aerobic capacity (p = 0.05). However, following adjustment for multiple testing, no significant changes were observed in aerobic capacity, upper-body strength, or lower-body strength indicated by comparable pre- and post-intervention performance in the 6MWT (p = 0.18), maximal push-up test (p = 0.06), and 30 s chair stand (p = 0.22), respectively. Improvements in shoulder flexion, extension, and abduction were achieved for affected and unaffected sides combined. ROM improved by an average of 6.1° flexion , 5.8° extension , and 6.8° abduction   reported post-intervention and atypically low baseline MVPA levels due to COVID-19 restrictions.One feature distinguishing the present study from previous dance interventions was the online delivery. Some aspects of the group class experience were lost by moving online, and this may account in part for the lack of substantial changes in outcome measures, for example, the opportunity to form relationships with other class participants. Previous literature has identified this as a key contributor to the psychosocial benefits of taking part in a group/partnered dance intervention, but this was diminished in the current study because of the online delivery , 17, 27.In lieu of a sufficient sample size and control group, the extent to which changes could be attributed to the intervention was inferred. Exploratory analyses sought to elucidate the effect of rate of attendance on changes in secondary outcomes. This may have led to an over- or underestimation of intervention effectiveness as attendance only accounts for one aspect of program engagement. Other factors, such as exercise compliance and social interaction with classmates, may have contributed to program engagement and the magnitude of changes in secondary outcome measures.Despite the limitations associated with study design, the “Ballet after breast cancer” program demonstrated many strengths. This was the first program to deliver classical ballet classes following a traditional format, to breast cancer survivors after primary treatment. In spite of the restrictions imposed on the study due to the COVID-19 pandemic, the program demonstrated unanticipated strengths. Online delivery was attractive to individuals unable/unwilling to attend in-person classes for reasons including safety, availability, time commitment, and travel. Furthermore, the sense of routine, social interaction, and regular physical activity the program provided may have curtailed an otherwise significant increase in stress, social isolation, sedentary behaviour, and impaired QOL, as has been observed globally during the pandemic , 42. TheClassical ballet is a feasible and acceptable mode of activity after breast cancer, leading to qualitative reports of improved physical and psychosocial wellbeing. The “Ballet after breast cancer” study provided preliminary evidence to suggest ballet can contribute to improving shoulder ROM and reducing sedentary activity. Delivering the program online helped to overcome several barriers which prevent engagement in physical and social activities after breast cancer, including barriers related to the COVID-19 pandemic. The findings of this study exposed avenues for investigating the comparative effectiveness of ballet and honing its implementation after breast cancer.Supplementary file1 (DOCX 2400 KB)Supplementary file2 (DOCX 1852 KB)Below is the link to the electronic supplementary material.


Breast cancer is uncommon in men and its aetiology is largely unknown, reflecting the limited size of studies thus far conducted. In general, number of children fathered has been found a risk factor inconsistently, and infertility not. We therefore investigated in a case–control study, the relation of risk of breast cancer in men to infertility and number of children.We conducted a national case–control study in England and Wales, interviewing 1998 cases incident 2005–17 and 1597 male controls, which included questions on infertility and offspring.Risk of breast cancer was statistically significantly associated with male-origin infertility  1.18–3.49)) but not if a couple’s infertility had been diagnosed as of origin from the female partner (OR = 0.86 (0.51–1.45)). Risk was statistically significantly raised for men who had not fathered any children (OR = 1.50 (95% CI 1.21–1.86)) compared with men who were fathers. These associations were statistically significantly present for invasive tumours but not statistically significant for in situ tumours.Our data give strong evidence that risk of breast cancer is increased for men who are infertile. The reason is not clear and needs investigation. Breast cancer is comparatively rare in men, and its aetiology is largely unknown. There are several commonalities with the disease in women, including a genetic component with several specific genes and SNPs known  and a reInvestigation of the relation of male infertility and reproductive history to breast cancer risk has been hampered, however, by the rarity of the tumour. Few studies have investigated infertility –10 or reThe study is of case–control design, with potential cases being all male residents of England and Wales with in situ or invasive breast cancer diagnosed in these countries at ages < 80 years during 1 January 2005 to 31 August 2017. These cases were identified from clinician reports to us and comprehensive listings from national population-based cancer registries.Since attempts to recruit population-based controls now give very low and selective recruitment rates , 17, we Potential cases and controls were asked if they would take part in the study, and if so were interviewed, usually in their homes, by trained research nurse interviewers using a structured questionnaire. The nurses also took a blood sample  for genetic analyses. The questionnaire enquired about demographic factors and potential risk factors for breast cancer including infertility and offspring. For infertility, we asked whether the subject or their partner had “ever had problems trying to have children for which you or she went to a doctor or infertility clinic’’ and if so we asked for the outcome of the consultation, with responses recorded according to which partner(s) were stated to be the cause of the infertility, and if the case was stated to be infertile, what treatment if any they had received. For offspring, we asked about each biological child the man had fathered, including any who had died.p < 0.05 in a two-sided test to indicate statistical significance.Analyses of the study data were by standard methods for case–control studies , calculaTo examine misclassification and potential confounding, we conducted several sensitivity analyses: excluding subjects whose quality of responses to the overall questionnaires were rated by the interviewer as ‘not well’ or ‘very poorly’; excluding subjects with Klinefelter syndrome, and then excluding also those with other factors that are potential confounders, namely certain testicular conditions, severe obesity, and previous primary cancers that might have involved chest radiotherapy and that directly or via their treatments might have affected fertility; and analyses restricted to married men, as an alternative to adjustment for marital status, since one of the two control groups (the ‘Generations Study’ controls) was by consequence of its source married. We also conducted sensitivity analyses adjusting additionally for alcohol consumption, smoking, family history of breast cancer, and liver disease, in case these might be confounders, although there is no strong evidence that they are.From the cancer registries and consultant notifications, we identified 3187 men resident in England and Wales diagnosed during the study period with breast cancer at ages under 80 years. Of these, 433 died (427) or emigrated (6) before we could make contact with them, for 21 we could not identify the consultant or the consultant did not participate, for 28 the consultant deemed the patient unsuitable to be approached, and 707 did not reply to our invitation or declined to participate. The remaining 1998 (62.7%) were interviewed. They were mainly  participated, and we approached 1,109 potential Generations Study controls, of whom 984 (88.7%) participated.P = 0.90; cases P = 0.39). The odds ratio for ever-infertility was statistically non-significantly raised  0.94–1.77). When analysed by outcome of the infertility consultation, there was a statistically significantly raised risk for men who said that they were diagnosed as the source of the couple’s infertility (OR = 2.03 (1.18–3.49)), but not for those whose partner was the source of the infertility (OR = 0.86 (0.51–1.45)) or for whom no source was identified (OR = 1.26 (0.71–2.24)). The same pattern was true in analyses excluding men with Klinefelter syndrome (infertility OR = 1.23 (0.89–1.69); self as source of infertility (OR = 1.81 (1.02–3.20)) and in analyses also excluding men with potentially confounding conditions (Table p = 0.02) for men who were themselves infertile (not in Table). Risk was also statistically significantly raised if we aggregated men who were either diagnosed as the source or said the source was unknown but they had been diagnosed with a low sperm count (OR = 2.17 (1.29–3.67), P = 0.004, not in table).One hundred and twelve (5.6%) cases and 80 (5.0%) controls reported that they had had infertility problems for which they or their partner had consulted a doctor or infertility clinic Table . The prep < 0.001) for childless men. Analysis by number of children showed a highly statistically significant inverse trend if zero was included as a value (p < 0.001) but a borderline statistically significant trend if it was not (p = 0.04). These results remained statistically significant when men with Klinefelter syndrome were excluded (not in table), when in addition potentially confounding conditions were excluded . There was no relation of risk to age of the men at birth of their first child (Table P = 0.04) and the OR for men who reported that they were diagnosed as the source of infertility was less raised (OR = 1.68 (0.96–2.93)), and there were modest changes for men whose partner was the source of infertility (OR = 0.76 (0.45–1.29)) or for whom no source was identified (OR = 1.12 (0.63–2.01)).One thousand, six hundred and fifteen (80.8%) cases and 1423 (89.1%) controls had fathered any children (Table p = 0.04), and for men with no children (p < 0.001), remained statistically significantly raised (not in Table). Sensitivity analyses adjusting additionally for alcohol consumption, smoking, liver disease and family history of breast cancer (see Methods) did not change the results materially (not in Table).Sensitivity analyses excluding men whom the interviewers considered to be relatively unreliable gave similar results to those above, and in particular risks for men who were themselves the cause of the couple’s infertility (n = 1838) and in situ (n = 160) tumours (Table n = 187) and − ve (n = 1376) tumours separately each gave similar results, with statistically significantly raised risks for male infertility  and for having no children  (not in Table).In analyses separately for invasive  children born outside marriage; they may have remained childless by choice, not infertility; and they may report a low sperm count even if it was not the reason for infertility. Additionally, the self-reports were reliant on the men’s recall and understanding—the evidence would have been stronger if infertility could have been validated from medical records, but this was not practical across an entire country over many decades. However, although all of the above sources of misclassification might plausibly have led to dilution and hence underestimation of any true risks, there is, as noted above, no obvious reason why this should have been differential between cases and controls, and hence have led to bias.The lack of a significant association of male breast cancer with infertility in most of the previous literature does not argue substantially against the association that we found: the few published studies –10 have Our finding of greater risk for men with no biological children compared with any children is congruent with infertility as a risk factor. Most previous studies have not found such an effect, but based on very small numbers , 15, 24,We found significantly decreasing risk with increasing numbers of children. However number of children beyond one is difficult to interpret as an indicator of male fertility, since it may more reflect social and cultural factors than fertility per se. The same is true for age at first birth. Similarly, it is difficult to interpret analyses confined to married men because of the changing relation of marital status to fatherhood in Britain, such that the meaning of ‘married’ in relation to potential for fatherhood has changed over time. Before 1980, 10% or fewer of births in England and Wales were outside marriage, but the proportion has since soared such that by 2000, 40% were outside marriage and subsequently almost 50% . In our Our data showed that the association of infertility with male breast cancer risk was clearly present for invasive tumours, but not significant, based on much smaller numbers, for in situ tumours: this does not appear to have been investigated previously.The reason for the association of male infertility with breast cancer risk demonstrated in our data is uncertain. Infertility can result from a wide range of factors, including genetic, congenital anomalies of the genitourinary tract, other anatomical reasons, and sexual dysfunction, but most cases are idiopathic .The main source of testosterone secretion in men is the testis, so one potential link between infertility and breast cancer risk would be via hormonal effects of testicular abnormalities. For instance, mumps orchitis can lead to testicular atrophy and long-term reduced testosterone production , and canIt has been hypothesised that prenatal oestrogen exposure can lead to raised risk of male infertility , in whicIn summary, our large case–control study gives strong evidence that male infertility is associated with raised risk of breast cancer in men. The reasons are uncertain and need to be investigated.


The effect of antipsychotics on breast cancer remains controversial.Embase, Scopus, PubMed, Web of Science, Cochrane Library, and Hebei Medical University Library were used for the literature search. Observational studies with original data for the effects of antipsychotics on breast cancer were used. Studies of bed quality, those with inadequate sample size, incomplete follow-up works, or studies that did not meet the criteria were excluded. Meta-analysis was performed using R version 4.1.2. The odds ratio (OR) and its 95% confidence interval (CI) were used to evaluate the proportion of breast cancer in different groups. To detect possible sources of heterogeneity, subgroup and meta-regression analyses were employed.Pooled data from 11 relevant studies with 1,499,001 participants suggested that individuals exposed to antipsychotics were more likely to suffer from breast cancer than those who were not exposed . No significant difference in breast cancer prevalence between the atypical and typical antipsychotic groups was found . Prolactin (PRL)-increasing and PRL-sparing antipsychotics posed a similar risk of breast cancer . Furthermore, the use of antipsychotics is attributed to increased mortality in patients with breast cancer . Those exposed to antipsychotics at the maximum dose were more likely to suffer from breast cancer than those exposed to the minimum dose.Antipsychotic exposure is an independent risk factor for breast cancer. No significant difference in the risk of breast cancer between typical and atypical antipsychotics was noted. Those exposed to antipsychotics at higher doses are more likely to suffer from breast cancer. Moreover, the use of antipsychotics is attributed to increased mortality in patients with breast cancer. PRL-increasing and PRL-sparing antipsychotics pose a similar risk of breast cancer.https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022307624. Increased health awareness, effective prevention strategies, and improved access to medical treatment are extremely significant to curb the increasing global burden of breast cancer, particularly in the most affected countries . Furtherhttps://www.crd.york.ac.uk/PROSPERO/, CRD42022307624). All the included studies were confirmed to be free of ethical and moral concerns. Data for the review were anonymously analyzed. Therefore, the ethics committee waived the need for consent.We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)  and MetaThe online databases included Embase, Scopus, PubMed, Web of Science, and Cochrane Library. The Hebei Medical University Library was browsed for related books and documents with the administrators’ help. The literature search was performed on 19 December 2021, without language and date restrictions. The search terms and strategy were described in the We excluded studies that may lead to an imprecise conclusion. Retrospective or prospective cohort studies and case–control studies were accepted. Due to the lack of relevant results, we excluded randomized controlled trials (RCTs), as introduced later. The difference in study design may bring uncertainty to the evidence provided in this study. The study type should be clearly defined. Observational studies should introduce the sources of epidemiological data. Sources, including hospital medical records, public health databases, and insurance agencies, were acceptable. Case−control studies should provide a clear selection criterion for the populations in control groups. Moreover, the baseline characteristics of the control groups should be strictly the same as those of the case groups. Participants in each group should be sufficient to avoid contingency, which means at least 100 participants for each group. Follow-up work should be completed, ensuring that all data are available. Criteria for breast cancer or antipsychotics should be clearly defined, which should be consistent with universal knowledge. Studies that received “critical risk” overall during quality assessments were excluded to avoid evidence of bad credibility. Patients with primary HPRL not caused by psychotropics at the beginning of the follow-up work should be excluded. Moreover, in cases of metastatic cancer unrelated to psychotropics, patients with multiple immune diseases caused by perimenopausal endocrine disorders should be excluded. Non-population-based virtual statistical models were not accepted. Review articles, case reports, or protocols were not applied in the present systematic review.Three reviewers independently performed the literature identification , under the direction of an epidemiologist (XW). Any disagreement was reported to XW. All the data were directly extracted from the literature. We recorded the number of breast cancers patients. The number of antipsychotics use and the types and dosage of antipsychotics were also recorded. We further extracted the characteristics of studies, including age at enrollment, follow-up duration, psychiatric diagnosis, the basis for grouping, and the country and region in which the trial was conducted. Details are described in Quality assessment for each study was performed using the risk of bias in non-randomized studies of interventions  in R verData synthesis and statistical analyses were performed by one reviewer (GZ) and double checked by another (XY).2 test was calculated as a measure of heterogeneity, in which I2 values of 25%, 50%, and 75% indicate low, moderate, or high heterogeneity, respectively (Odds ratios (ORs) and their 95% confidence intervals (CIs) were used to evaluate the proportion of breast cancer in different groups for the meta-analyses. To detect possible sources of heterogeneity, subgroup analysis and meta-regression analysis were employed. All statistical effects were calculated using a random-effect model. A two-tailed α = 0.05 was set as the statistical significance. The Iectively . We conshttps://www.R-project.org).All statistical analyses were performed in R version 4.1.2 using the package “meta”  , as shown in the forest plot in schizophrenia, 1.84; 95% CI, 1.36–2.49; subgroup differences, p < 0.01) , participants exposed to antipsychotics were at a higher risk of breast cancer than those not exposed to antipsychotics , with significant heterogeneity , as shown in the forest plot in In comparison B , no significant difference in breast cancer prevalence was found in different groups , with significant heterogeneity , with very low heterogeneity, as shown in The studies by Taipale  OR, 1.3 OR, 1.3 suggesteThe present systematic review and meta-analysis investigated epidemiological evidence about breast cancer and antipsychotics. Data from 11 observational studies of 1,499,001 individuals indicated that antipsychotics are an independent risk factor for breast cancer and suggested no significant difference in the effect of antipsychotics of different generations. Particularly, the associations were stronger in younger individuals (younger than 45 years old). The association was stronger for female Chinese populations. Moreover, our results revealed that individuals exposed to antipsychotics at the minimum dose are more likely to suffer from breast cancer than those exposed to the maximum dose. Furthermore, the use of antipsychotics is attributed to increased mortality in patients with breast cancer. Those taking PRL-increasing and PRL-sparing antipsychotics for more than 5 years showed no difference in the risk of breast cancer. It is worth noting that typical antipsychotics were generally reported to cause an increase in PRL. Only some of the atypical antipsychotics, including risperidone, 9-hydroxyrisperidone, and amizupired, have caused an increased PRL level.To date, the present review is the first meta-analysis that comprehensively summarized the effects of antipsychotics on breast cancer and had the largest sample size. The studies by Taipale  and AntoA large meta-analysis by Correll  showed tThe study by Anna George  only enrin vitro cytological studies and animal experiments corroborate this view , variability in study designs (including the source of data and follow-up duration), measurement tools, sociodemographic factors, frequency and type of testing, local policies, and natural environment. The variability in antipsychotics was another notable source of heterogeneity. As mentioned above, the growth of breast tumor tissue is affected by both pituitary PRLs and paracrine/autocrine PRLs. However, paracrine/autocrine PRL varies greatly among different patients. If PRL plays a key role in antipsychotics and breast carcinogenesis, autocrine and paracrine PRL may introduce considerable heterogeneity into our study. Moreover, the type and dose of antipsychotic drugs varied widely among the included studies, which may have introduced heterogeneity.In comparison A, the significant difference became insignificant when some studies were excluded. Although sources of this uncertainty or heterogeneity seem to be detected, the conclusion of this review should be explained with caution. Several studies have reported potential associations of mental disorders with the risk of breast cancer incidence and its recurrence or mortality , 53. We Questions remain regarding breast cancer and antipsychotics. The most significant among them is the lack of RCTs or cohort studies reporting changes in the prevalence of breast cancer following antipsychotic treatment termination. Data were unavailable to confirm the effect of antipsychotic treatment termination or PRL lowering on the prevalence of breast cancer, which requires more studies. Due to the autocrine/paracrine of PRL, although the pooled data suggest that PRL-increasing and PRL-sparing antipsychotics pose a similar risk of breast cancer, we cannot deny the role of PRL in breast cancer. Studies are needed to accurately evaluate the effects of autocrine/paracrine PRL on breast cancer and antipsychotic drug-induced breast cancer.Our review demonstrated that antipsychotic exposure is an independent risk factor for breast cancer. Moreover, no significant difference in the risk of breast cancer between typical and atypical antipsychotics was noted. In addition, those exposed to antipsychotics at higher doses are more likely to suffer from breast cancer. Moreover, the use of antipsychotics is attributed to increased mortality in patients with breast cancer. Lastly, PRL-increasing and PRL-sparing antipsychotics pose a similar risk of breast cancer.The original contributions presented in the study are included in the article/KZ led the study and contributed to team management, revised the manuscript, polished the language, and contributed to drafting the articles or critical revision for important intellectual content. KZ approved the final version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. ZG contributed to the data analysis and manuscript formatting. ZG, YX and HS contributed to the literature search, study design and identification, data acquisition, and recording of the characteristics of the studies. WX evaluated the quality of the observational studies and reviewed and rectified the data. ZG, YX, HS, WX, and JN contributed to the data interpretation and critical revision of the manuscript. HS embellished the images and further interpreted the data. KZ contributed to the literature downloading and contacted the literature authors. All authors contributed to the article and approved the submitted version.


During the COVID-19 pandemic, an Internet-Mindfulness-Based Stress Reduction (iMBSR) program was delivered and may be better than an in-person approach. Our study evaluated the effects of iMBSR intervention on mental health, self-efficacy, and body image in women with breast cancer in Taiwan.n = 41) program or a waitlist control group (n = 26), without heterogeneity between group characteristics. Patients from both groups were measured at baseline and postintervention using three scales: Depression, Anxiety, and Stress Scale (DASS-21), General self-efficacy scale, and Body Image Scale. Descriptive dataset analysis, paired t-test, and Student’s t-test were used to evaluate the data.Sixty-seven women with breast cancer were allocated to a 6-week iMBSR (p = 0.041) with medium effect sizes. Significant benefits were found for body image  and self-efficacy , with large effect sizes (Cohen’s d = 0.73).Although iMBSR did not significantly improve depression and stress between groups, iMBSR could improve anxiety (Δmean: −2.0 vs. −0.4, Our preliminary study supports iMBSR as a program that can improve mental health, body image, and self-efficacy in women with breast cancer. During the COVID-19 pandemic, medical professionals can use Internet-based clinical health education. A 2019 cancer registry report indicated that breast cancer had the second highest mortality rate in Taiwan and the highest incidence among cancers in women and increased by ~4.49% per year .Adverse effects of breast cancer treatment, such as cognitive impairments , alopeciBased on Bandura’s theory, self-efficacy refers to the ability of an individual to control their motivation, behavior, and how they face obstacles. The theory essentially attributes an individual’s belief in efficacy to their chance of achieving success in a particular situation . TherefoStudies have highlighted that mindfulness-based interventions have a wide range of benefits, including decreased perceived stress, anxiety, depression, and fatigue , improveWith the outbreak of COVID-19 in December 2019  and its Patients with breast cancer are at higher risk of psychological stress, and the additional stress of the COVID-19 pandemic can increase their vulnerability . InterneA study in Denmark involving women with breast cancer and older men with prostate cancer who underwent an Internet-delivered MBI (iMBI) program found that iMBIs could improve anxiety and depressive symptoms immediately after the intervention . AnotherConsiderable research explores the effects of iMBSR on physical and psychological problems in patients without cancer, and using iMBCT to resolve emotional distress in patients with cancer. However, there has been little research on iMBSR’s effects on self-efficacy and body image in women with breast cancer. Therefore, our study aimed to explore the effect and feasibility of iMBSR on mental health, self-efficacy, and body image in women with breast cancer during COVID-19.Both the iMBSR and waitlist control group used purposive and snowball sampling. Potential participants were recruited from visiting breast surgery clinics, cancer wards, and breast cancer affiliate websites. Inclusion criteria were as follows: had been diagnosed with stage 0–IV breast cancer within the past 5 years; aged ≥20 years old; could communicate in Mandarin; received at least recent adjuvant therapy ; and Eastern Cooperative Oncology Group performance score ≤ 1 . ExclusiOur study has several required online software to facilitate online teaching and recruiting participants . The recAll participants submitted written informed consent during the orientation. Variable measurement was performed at two time points, including pretest and posttest. (1) Pretest: This was completed before the program’s second session. Baseline scores were based on the participants; responses to items across the scale. (2) Posttest: After the sixth week of training, participants completed a posttest within a week. Participants received a TWD 100 electronic coupon after completing the pre and postintervention questionnaires.The frequency and duration of our mindfulness training were six 2-h weekly group coaching sessions led by a professionally trained psychologist using online learning with Microsoft Teams. We used Jamboard, a digital interactive whiteboard developed by Google, to measure the number of patients participating online before class. Our training aimed to improve these metrics: mindful eating, body scan, breath awareness practice, mindful walking, and sharing of group experiences. The informal training in our study was conducted through home practice using mindfulness-guided cloud-based applications such as YouTube . SpecifiInclusion and exclusion criteria for WCG (delay in receiving the intervention) were the same as for the iMBSR group. WCG occurred in parallel with iMBSR. The WCG had manuals for managing symptoms and oral instructions on health and hygiene education. All participants were instructed not to participate in any other stress reduction program or mind–body therapy during the study. We offered an additional iMBSR skills study program to WCG participants following our study’s conclusion.The present study involved research with human participants who were required to sign an informed consent form before participating in the study. All procedures in our research comply with the ethical standards of the Institutional Research Council.n = 356) and compared with n = 720). Multi-group confirmatory factor analysis found that the Chinese version of the DASS-21 effectively discriminates between depression, anxiety, and stress, but less so than the English version.The Depression, Anxiety, and Stress Scale (DASS-21) is a 21-item tool that evaluates the participant’s depression, anxiety, and stress over the past week, on a Likert-style scale ranging from 0 (never) to 3 , with sensitivity to change  and good discriminant validity , developed by Jerusalem and Schwarzer, is a generic tool that is widely used to measure self-efficacy in clinical and nonclinical populations . Zhang eThe questionnaire was divided into two parts. First: basic personal information, including age, marital status, and economic status. Second: disease status, including time since diagnosis (years), cancer staging, and cancer treatment. The questionnaire was collected using an online Google form.To maintain the fidelity of our online mindfulness program, the 6-week intervention was conducted by a qualified clinical psychologist with specialized training in psychology at the Medical Center, Departmental Cancer Center. The instructor assessed the transfer of iMBSR skills by asking questions and discussing the material with the participants, and the patients recorded their practice duration weekly. The first author (YCC) confirmed that the practice had been completed during the intervention. We also prepared a standardized process. First, we set up a LINE messenger group with a mobile app before iMBSR to remind participants of the class schedule, provide class information, and solve the participants ‘problems during the course . Second, we regularly provided support and addressed participants’ needs wherever possible. Third, we used the patient groups to care for each other and share experiences . Finally, if the participants completed the program, we provided an e-coupon as a reward.The sample size of our study was calculated based on the results of our previous study . We assut-tests and Student’s t-tests for normally distributed continuous variables, respectively. The statistical significance level was set to a two-sided p < 0.05 in all statistical analyses.Data analysis was performed using SPSS for Windows 22.0 . The collected questionnaires were coded, and the accuracy of the data was verified repeatedly after input. Descriptive statistics were described for the baseline demographic and clinical characteristics of the patients with breast cancer. The Kolmogorov–Smirnov test (KS test) tested raw data for adherence to the assumptions of normality and equal variance and the detection of outliers before inferential statistical procedures. Self-report data between baseline and 6 weeks were compared for within-group and between-group data analyses using paired t-test results.To explore the magnitudes of difference between groups, Cohen suggested that effect sizes be categorized into small, medium, and large treatment effects and represented by 0.2, 0.5, and 0.8, respectively . A websin = 15) were lack of IT skills, physical discomfort, scheduling conflict, and others. We had established baseline data for 72 participants before the first session, with six dropouts in the WCG and three who changed to the Internet format. The postintervention assessment was completed by 41 and 26 participants in the MBSR and WCGs, respectively. The demographic and clinical characteristics of the patients are listed in p = 0.043).Our study had 88 eligible participants (see p = 0.041). However, depression , stress , and overall DASS-21 scores  were not significantly decreased. Six consecutive weeks of iMBSR sessions also improved body image  and self-efficacy  in women with breast cancer.We used DASS-21 to measure participants’ current psychological problems. The DASS-21 results indicated significantly improved anxiety  and significant decreases in the subscale of depression , anxiety , and stress . The intervention significantly improved the participants’ negative body image  and increased self-efficacy  found improvements in anxiety and depression in women with breast cancer following Internet-based Mindfulness-Based Cognitive Therapy  at risk of coronavirus infection , therebyOur study has several strengths. Our investigation was during the COVID-19 pandemic, highlighting the importance of an Internet-based psychoeducational intervention. The e-delivery prevented exhaustion from long commutes and reduced infection rates, which was critical for patients with cancer and potentially compromised immune systems. Additionally, we initially expected to enroll 48 participants, but we completed the final program with 67, exceeding our expected inclusion rate. However, this study had some limitations. First, we did not measure the long-term effects of iMBSR on patients with breast cancer. Second, the average age of the participant was 49.61 ± 12.03. Some older participants struggled with the Microsoft Teams software and could not join the course, hear the sound, or operate the digital whiteboard. Therefore, the staff should have spent more time contacting individuals struggling with the software.Overall, our findings indicate that we provided women with breast cancer with a convenient and cost-effective method to enhance their empowerment and confidence through mindfulness-based mental health education that taught them how to easily incorporate mindfulness techniques into their daily lives. We propose a future study investigating the acceptability and cost-effectiveness of Internet-based mindfulness interventions for people with different sociodemographic conditions.The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding authors.The studies involving human participants were reviewed and approved by Research Ethics Committee, China Medical University & Hospital, Taichung, Taiwan. The patients/participants provided their written informed consent to participate in present study.Y-CC: study conception and design. Y-CC, C-FC, C-KW, C-TW, L-CL, and Y-CW: data collection and critical revision of the article. Y-CC and C-KW: data analysis and interpretation and drafting of the article. All authors contributed to the article and approved the submitted version.This research was partially supported by a grant from China Medical University, Taiwan (CMU110-N-11 and CMU111-MF-115) and Ministry of Science and Technology, Taiwan (MOST 110-2314-B-039-059 and MOST 111-2314-B-039-016), received by Y-CC.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Colon cancer is the second most frequently diagnosed cancer in women in Norway, where incidence rates of colon cancer increased 3‐fold between 1955 and 2014, for unknown reasons. We aimed to assess the burden of colon cancer attributable to modifiable risk factors in Norwegian women using the data from the Norwegian Women and Cancer (NOWAC) study. Self‐reported information from 35 525 women from the NOWAC study were available. These included the following exposures: smoking status, alcohol consumption, body mass index, physical activity, intake of calcium, fibers, and red and processed meat. Colon cancer cases were identified from the Cancer Registry of Norway. A parametric piecewise constant hazards model was used to estimate the strength of exposure‐cancer associations. Population attributable fractions with 95% confidence intervals (CIs) were calculated considering competing risk of death. The fraction of incident colon cancer attributable to ever smoking was 18.7% (95% CI 4.7%‐30.6%), low physical activity 10.8% (95% CI −0.7% to 21.0%), alcohol consumption 14.5% (95% CI −2.8% to 28.9%), and low intake of calcium 10.0% (95% CI −7.8% to 24.8%). A small proportion of colon cancer cases was attributable to combined intake of red and processed meat over 500 g/week, overweight/obesity, and low intake of fibers. Jointly, these seven risk factors could explain 46.0% (95% CI 23.0%‐62.4%) of the colon cancer incidence burden. Between 23% and 62% of the colon cancer burden among women in Norway was attributable to modifiable risk factors, indicating an important preventive potential of a healthy lifestyle. What's new?Colon cancer incidence among Norwegian women has increased 3‐fold since the mid‐20th century, for reasons that remain uncertain. Here, the authors applied a novel method to calculate the burden of colon cancer attributable to modifiable risk factors, based on data from the Norwegian Women and Cancer Study. Analyses show that smoking accounted for the greatest proportion of colon cancers diagnosed among women in Norway between 1998 and 2018. Yet, less than half of colon cancers diagnosed over this time could be attributed to joint effects of modifiable risk factors, including smoking, leaving a significant proportion of colon cancer burden unexplained. BMIbody mass indexCIconfidence intervalFFQfood frequency questionnaireHRhazard ratioIARCInternational Agency for Research on CancerNOWACNorwegian Women and Cancer StudyPAFpopulation attributable fractionWCRFWorld Cancer Research FundWHOWorld Health Organization1Women in Norway have one of the highest incidence rates of colon cancer in the world, as well as in the Nordic countries. A total of 1617 new cases of colon cancer were diagnosed in Norwegian women in 2020, which makes colon cancer the second most frequently diagnosed cancer in women in Norway after breast cancer. In the same group, the age‐standardized incidence rate was 53.1 per 100 000 women between 2016 and 2020. The incidence rate of colon cancer has increased 3‐fold between 1955 and 2014, while rectal cancer rates have stabilized after 1990s.According to the International Agency for Research on Cancer (IARC/WHO) and the World Cancer Research Fund (WCRF), the established risk factors for colon cancer are smoking, alcohol consumption, body fatness, and processed and red meat consumption, with physical activity, intake of foods containing dietary fibers, calcium, wholegrains and dairy products being established protective factors for colon cancer.Ignoring competing risk of death can overestimate the PAF estimates.The purpose of our study was to assess the proportion of colon cancer attributable to established modifiable risk factors among Norwegian women in the last 20 years using the data from the nationally representative Norwegian Women and Cancer (NOWAC) study and accounting for competing risk of death and joint exposure effects.22.1The Norwegian Women and Cancer (NOWAC) Study has been described in detail previously.After excluding women with prevalent cancer other than nonmelanoma skin cancer, our study sample comprised of 35 525 women that were included in the complete‐case analysis.2.2Assessment of dietary information from the FFQ has been previously described.Habitual intake of red meat included “roast meat ,” “chops,” and “steak,” whereas processed meat included “meatballs,” “hamburgers,” and “sausages.” The amounts of red and processed meat consumed were calculated as grams per day from intake frequencies and collected portion sizes or standard servings. We calculated the total meat intake in grams per week by combining the amounts of red and processed meat.Women were also asked if they were alcohol abstainers. If not, they were asked to report how often they had been drinking beer, wine, and spirits during the past year. Alcohol intake was derived by combining this information and calculated as grams per day (ethanol). Intake of calcium and fibers was calculated using the Norwegian Food Composition table as milligrams per day and grams per day, respectively. In addition, we used information about total energy intake calculated as kJ/day.2) from self‐reported weight (kg) and height (cm). Smoking status (never/former/current) was derived from the questions about smoking history and current smoking. Level of physical activity was self‐reported on a 10‐increment scale from 1 to 10.Body mass index (BMI) was calculated  in the International Classification of Diseases, 10th Revision.2.4We calculated person‐years from the start of follow‐up to the date of any incident cancer diagnosis (except nonmelanoma skin cancer), emigration, death, or the end of follow‐up , whichever came first.We used parametric piecewise constant hazard models to estimate the strength of exposure‐cancer and exposure‐deaths associations and expressed them as hazard ratios (HR) and their 95% confidence intervals (CI). Both age‐adjusted and multivariable models were carried out. The multivariable models included the following risk factors: smoking, BMI, alcohol consumption, physical activity, red and processed meat consumption, fiber, calcium intake, age, and total energy intake.2 were at or below 25 kg/m2; if current and former smokers were never smokers; if those who consumed alcohol were teetotalers; if those who consumed 500 g or more of red and processed meat per week were to consume less than 500 g/week; if those with low physical activity (score 1‐5) had high physical activity level (score 6‐10); if those in the lowest and middle tertiles of calcium intake were in the highest tertile; if those in the lowest and middle tertiles of fiber intake were in the highest tertile. Finally, to assess the simultaneous effect of all the aforementioned factors on the colon cancer burden, we performed the analysis in which all the scenarios mentioned above were combined.The data on exposure prevalence were obtained from the study baseline (1996‐1998). To calculate the PAF of colon cancer attributable to each exposure as well as combined exposures, we used a method that includes death from any cause as a competing risk.Finally, PAF estimates were multiplied by national incidence figures for women in the age‐group 40‐74 from 1998 to 2018, to estimate the number of colon cancer cases that were attributable to all the risk factors separately and combined.In sensitivity analyses, we repeated the models with 10 and 15 years of follow‐up. We also repeated the analyses after excluding participants that had no more than 1 year of follow‐up to control for possible reverse causality. Finally, we carried out a sensitivity analysis in which we additionally adjusted for participants' height, as it is a known risk factor for colon cancer.Analyses were performed in STATA version 16.0  and in SAS 9.4 .3During an average of 18.6 years of follow‐up and more than 600 000 person‐years, there were 430 incident cases of colon cancer, and 2095 deaths in the study sample.2 (41.6% vs 38.4%), be less physically active (59.1% vs 52.1%), be in the lowest tertile of both calcium (39.3% vs 33.3%) and fibers intake (35.6% vs 33.3%), and to be alcohol consumers (75.6% vs 72.4%) compared to women without colon cancer diagnosis. The proportion of those who consumed at least 500 g of meat per week was slightly higher among noncolon cancer cases than colon cancer cases (21.6% vs 20.9%) , more likely to be current smokers (35.2% vs 32.1%), have a BMI of at least 25 kg/mWe observed a 40% increase in risk of colon cancer in current smokers  and 39% in former smokers  compared to never smokers during follow‐up Table . CurrentHigh level of physical activity was inversely associated with risk of colon cancer  Table . The proThe HR of colon cancer for alcohol consumption compared to no intake of alcohol was 1.23 (95% CI 0.96‐1.56) (Table Hazard ratio of colon cancer in relation to the highest vs lowest tertile of fiber intake was 0.96 (95% CI 0.69‐1.34) and the highest vs lowest tertile of calcium intake 0.77 (95% CI 0.57‐1.04), with the corresponding PAF estimates being 1.7% (95% CI −18.1% to 18.2%) and 10% (95% CI −7.8% to 24.8%).The risk of being diagnosed with colon cancer during the follow‐up in women who consumed more than 500 g of red/processed meat per week compared to women who consumed less than this amount ranged from 17% decrease in risk to 39% increase in risk  Table . Only 1.2 was 1.03  . Additional adjustment for participants' height did not change the results notably (results not shown).4Our results indicate that the largest contributor to the colon cancer burden among Norwegian women over the last 20 years was smoking, explaining about one fifth of cases. More than half of all colon cancer cases diagnosed between 1998 and 2018 were not explained by the joint effect of the established modifiable risk factors evaluated. Thus, a large proportion of the colon cancer burden remains unexplained.2, and consuming >2 alcoholic drinks/day. In the same study, ever smoking was the only factor significantly associated with the colon cancer risk in women, explaining 7.6% of the colon burden among women. The prevalence of current smoking (13%) and former smoking (27%) in Australian women in 2014‐2015 used in the study were lower than the respective prevalence among Norwegian women in 1997 (32% and 34%) used in our study, thus explaining the difference in the PAF estimates for ever smoking between the studies.Our findings are partially in line with the results from the pooled analysis of seven Australian cohort studies.In the US, the results from the Nurses' Health study showed that physical activity, smoking, alcohol, calcium, multivitamin intake, and overweight/obesity combined were responsible for 37% of colorectal cancer cases.In another study, processed meat accounted for only 0.9% of colon cancer burden in women in Denmark in 2008 in line with our finding.Several reasons could account for the differences between the results from the current study and previously published research. First, differences in exposure prevalence between countries and populations, and different time points under study is likely to be the main reason for differences in the results between studies. Second, most of the studies focused on colorectal cancer rather than colon cancer alone, which was the outcome in our study. Third, only the study by Vajdic et al used the PAF method that considered death as a competing risk event. Fourth, differences in categorizations of exposures are contributing factor that limits comparability between studies. Finally, the differences in sample size and thoroughness of adjustment between the studies may also have contributed to the differences in the results.Our study has several strengths that should be noted. The NOWAC cohort is nationally representative sample of Norwegian women.There are also notable limitations in the present study. The response rate of 52.7% in the NOWAC cohort is similar or higher to those in other similar population‐based cohorts from the same calendar period. As in all population studies relying on self‐reported questionnaire data, a certain degree of selection bias is most likely present, as the study participants might have a healthier lifestyle compared to the general population of women in Norway. However, the external validation study of the NOWAC cohort has shown that the study participants did not differ from the source population except for slightly higher educational level. In addition, the incidence rates for all cancer sites in the NOWAC cohort were almost identical to the figures from the Norwegian Cancer Registry at the time of validation. Hence the NOWAC cohort is considered to be nationally representative of women in Norway.rs = .17), moderate validity of processed meat consumption (rs = .34), and a relatively high validity of information on fiber (rs = .67), and calcium intake (rs = .55). A significant increase in alcohol consumption was found in the retest (rs = .64), suggesting that alcohol consumption was underreported at baseline. On the other hand, anthropometric measures in the NOWAC cohort were shown to be reliable.We included most of the established risk factors for colon cancer in the multivariable models. In the NOWAC cohort, we did not have information on family history of cancer other than breast cancer. Thus, the lack of data on family history of colon cancer could have led to residual confounding. In addition, intake of wholegrains as the established risk factor for colon cancer was not included in the study. The main source of residual confounding in the present article, however, is likely due to measurement error of the studied risk factors. As the data on the risk factors were collected from self‐administered baseline questionnaires or FFQ, a misclassification of exposures of an unknown degree is likely present. The FFQ used in the NOWAC cohort was validated against four repeated 24‐hours dietary recalls.Diet and lifestyle of our study participants were captured at the baseline only and significant changes in these exposures might have occurred during the long follow‐up, thus potentially leading to further exposure misclassification. However, the results remained unchanged in sensitivity analyses based on 10‐ and 15‐years follow‐up. Undiagnosed colon cancer at baseline may have led to changes at baseline exposure measurements but sensitivity analyses excluding the first year of follow‐up did not indicate reverse causality. Also, the latency period of colon cancer can be as long as four decades.The prevalence of smoking in Norway has been continuously declining, whereas average BMI has been increasing since 1990s.Finally, because of a low intake of processed and red meat reported by our study participants, we were unable to assess the effect of these types of meat separately.In conclusion, established modifiable risk factors for colon cancer could explain between 23% and 62% of the colon cancer burden among women in Norway between 1998 and 2018. Out of the seven established risk factors evaluated, current and former smoking was the factor responsible for the highest proportion of the colon cancer cases.The work reported in the article has been performed by the authors, unless clearly specified in the text. All authors contributed to the study conception and design. Marko Lukic prepared the data, carried out the statistical analysis and drafted the manuscript. Idlir Licaj, Maarit A. Laaksonen, Elisabete Weiderpass, Kristin B. Borch, and Charlotta Rylander critically revised the manuscript. All authors read and approved the final manuscript.This work was supported by UiT The Arctic University of Norway and Aakre‐Stiftelsen Foundation. The funding sources were not involved in the design and conduct of the study; the collection, management, analysis, or interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.The authors declare that they have no competing interests to declare that are relevant to the content of this article.The NOWAC Study was approved by the Regional Committee for Medical Research Ethics and the Norwegian Data Inspectorate. All individual participants included in the study gave written informed consent.