What is the evidence for use of clinical prediction models to predict cardiotoxicity related to chemotherapy treatment in women with breast cancer?This systematic review identifies 7 studies and 6 outcome prediction models for cardiotoxicity in different populations of patients with breast cancer. Most prognostic models report good to excellent discrimination but fall short in addressing bias due to methodological weaknesses, particularly for sample size, lack of overfitting consideration, handling of missing data, and insufficient model performance assessment.These results suggest that there is insufficient evidence to provide personalized risk prediction of cardiotoxicity related to breast cancer treatment in clinical practice; thus, researchers should adhere to best practices in the process of developing and validating predictive models. This systematic review evaluates risk prediction models of cardiotoxicity for patients with breast cancer. Cardiotoxicity is a serious adverse effect that can occur in women undergoing treatment for breast cancer. Identifying patients who will develop cardiotoxicity remains challenging.To identify, describe, and evaluate all prognostic models developed to predict cardiotoxicity following treatment in women with breast cancer.This systematic review searched the Medline, Embase, and Cochrane databases up to September 22, 2021, to include studies developing or validating a prediction model for cardiotoxicity in women with breast cancer. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess both the risk of bias and the applicability of the prediction modeling studies. Transparency reporting was assessed with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) tool.C statistic, ranged from 0.70  to 0.87 . The most common predictors identified in final prediction models included age, baseline left ventricular ejection fraction, hypertension, and diabetes. Four of the developed models were deemed to be at high risk of bias due to analysis concerns, particularly for sample size, handling of missing data, and not presenting appropriate performance statistics. None of the included studies examined the clinical utility of the developed model. All studies met more than 80% of the items in TRIPOD checklist.After screening 590 publications, we identified 7 prognostic model studies for this review. Six were model development studies and 1 was an external validation study. Outcomes included occurrence of cardiac dysfunction (echocardiographic parameters), heart failure, and composite clinical outcomes. Model discrimination, measured by the area under receiver operating curves or In this systematic review of the 6 predictive models identified, only 1 had undergone external validation. Most of the studies were assessed as being at high overall risk of bias. Application of the reporting guidelines may help future research and improve the reproducibility and applicability of prediction models for cardiotoxicity following breast cancer treatment. Breast cancer survival 5 years after diagnosis now exceeds 80% in most high-income countries.2 Currently, there are more than 7.7 million breast cancer survivors around the world; this reflects improvements in diagnostics and treatment strategies including radiation therapy, chemotherapy, and biologic agents.4Breast cancer is now the world’s most commonly diagnosed cancer, and it is responsible for 1 in 6 cancer deaths among women.5 In the literature, the incidence of cardiotoxicity was reported to be as high as 34% when anthracycline and targeted agents are combined.6 Moreover, breast cancer survivors have a significantly increased risk of death due to cardiovascular disease (CVD).7Unfortunately, these therapeutics can cause potentially life-threatening adverse effects. Myocardial dysfunction and heart failure (HF), frequently described as cardiotoxicity, are serious adverse effects that women undergoing breast cancer treatment can experience.11 Several scores that can assist health care professionals and patients with breast cancer in assessing prognostic indicators and other decisions made in primary care have been developed in this setting. With the increasing number of breast cancer survivors and adverse cardiovascular events, it appears important to systematically assess the available prediction models and their ability to predict cardiotoxicity. The purpose of this study was to identify, describe, and appraise all prognostic models developed to predict cardiotoxicity related to chemotherapy treatment in women with breast cancer.Given that cardiotoxicity is a significant factor in the prognosis for patients with breast cancer, many studies have aimed to identify predictors of cardiotoxicity. Age, obesity, hypertension, diabetes, and previous anthracycline treatment have been identified as major predictors of cardiac toxicity.PRISMA) reporting guideline. The study protocol was registered with the Prospero International Prospective Register of Systematic Reviews (CRD42021290441). An ethics statement was not required because this study was based exclusively on published research.A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis , involving women diagnosed with breast cancer at any stage (population) who subsequently had cardiotoxicity, defined as changes in LVEF or all symptomatic cardiac toxic effects after breast cancer therapy (outcome), at any time during follow-up (timing), with no predefined comparator (comparator) or setting (setting). We excluded review articles, conference abstract and studies with only patients with metastatic breast cancer. Two reviewers (E.G.K. and A.K.) independently performed the first screening by title and abstract. The 2 investigators independently undertook all study processes including online database search, study selection, data extraction, and critical appraisal. Any disagreements were discussed to reach a consensus. When a consensus was not obtained, third-party experts were invited to research, discuss, and finally reach a decision.The PICOTS  strategy was adopted for the search.On September 22, 2021, we searched the electronic bibliographic databases Embase, Medline (PubMed), and Cochrane with no restriction on publication date. We developed a search strategy from a combination of free text and Medical Subject Heading terms for BC, cardiotoxicity, and HF  checklist.14 Risk of bias (ROB) was assessed according to the 4 PROBAST domains  and applicability according to 3 domains . The ROB was assessed to be either high, low, or unclear based on signaling questions (eAppendix 3 in We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess the risk of bias of the individual prognostic models investigated.TRIPOD) reporting guideline to assess good reporting of studies developing or validating multivariable prediction models  at 1, 3, and 7 years20; and 5-year risk of cardiac death or congestive HF.16Follow-up ranged from 1 to 7 years. Two studies assessed the 1-year risk of cardiotoxicity.20; 2 of them used administrative data with International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes to define HF,18 and 1 of them used MACE as the outcome, which was a composite of HF, myocardial infarction, stroke, cardiovascular deaths.20 In 3 studies, clinical criteria were used in conjunction with left ventricular parameters.21The definition of cardiac events was not uniform. Three of the included studies used clinical criteria21 and comorbidities. Comorbidities included: diabetes20; hypertension21; body mass index21; atrial fibrillation17; prior coronary artery disease20; kidney failure20; and prior stroke.20 Some studies explored other predictors such as: disease characteristics 18; therapeutic methods used such as dose of chemotherapy agents 20 or dose of irradiation to left breast20; biochemical predictors such as lipid profile20; and echocardiographic parameters such as baseline LVEF21 and LVEF change.19 The models and their calculations are described in eTables 4 and 5 in The included models used between 2 and 20 predictors . Most of20 One study did not report discrimination.17 Of the 5 prognostic models for which the C index was reported, 2 models20 had excellent discrimination (0.80-0.89) and 321 had acceptable discrimination (0.70-0.79), irrespective of cardiac event definition , reasonable number of participants with the outcome (item 4.1: fewer than 10 events per variable20), and participants lost to follow-up or with missing data handled inappropriately  were identified as bias related to analysis. We rated as low ROB the analysis domain for the prediction model of Fogarassy et al18 because the development study was based on a large data set and a high number of events per variable (EPV) stratum. In Upshaw and al,21 the development study was rated as unclear ROB due to insufficient model performance assessment . We rated concern for applicability of all the development studies as low across all domains , introduction (item 3), and source of data (item 4) for all studies. Lack of reporting on participant selection (item 13a) or models’ predictive performance (item 10d) were noted in 4 studies.21 Only 1 study19 reported unadjusted association between each candidate predictor and outcome (item 14b). No study reported masked assessment of outcome (item 6b) and predictors (item 7b). Missing data handling (item 9) was not reported in 1 study.18TRIPOD scores varied from 25 to 28 (of 31) in the 6 development studies . All stu22) externally validating the prediction model by Ezaz et al.17 It was derived from data from a retrospective, monocentric cohort study. The recruitment period for the study was from 2010 to 2014, and follow-up was not reported. Patients with all stage breast cancer treated by doxorubicin alone or in association with trastuzumab were included. A total of 183 patients were included with 33 events. The reported discrimination was excellent (AUC = 0.86). Calibration was not reported (item 4.7 rated “no information”). We rated the ROB as high due to analysis concerns, including item 4.1 (the number of participants with the outcome was under 100) and item 4.2 , and a lack of specified exclusion criteria  and the clinical risk score from Ezaz et al.17 The combined model had superior receiver-operating characteristics (C = 0.9629).In their study, Milks et al18 was the only included study that had a low overall risk of bias. However, neither study was externally validated. None of the included studies explored net benefit analysis with respect to the developed models.Fogarassy et alERBB2 (formerly HER2)-negative patients, ERBB2-positive patients and patients eligible for trastuzumab. One of the models17 underwent external validation in a separate external validation study.22 The performance of these models and their applicability were assessed. Although most prognostic models reported good to excellent discrimination, of the 6 models, only 1 had low ROB and 1 other unclear ROB; the other 4 models had high ROB due to statistical analysis concerns, particularly for sample size, handling of missing data and not presenting appropriate performance statistics.We conducted a systematic literature search that identified 7 studies and six outcome prediction models for cardiotoxicity in different breast cancer populations, including patients with breast cancer at any stage, 23 Ezaz et al17 used the SEER-Medicare registry to identify 1664 American women diagnosed with early stage breast cancer, and Fogarassy et al18 used the Hungarian National Cancer Registry to include 8068 patients. Nevertheless, we should be cautious because the amount of detail in data collection and whether outcome assessment was conducted by protocol may be a limitation for prognostic analyses in patient registries.24Two studies included in this review used registry data to develop their model. Registry data are commonly used in clinical prediction models because they provide large sample sizes and good representativeness while at the same time being relatively inexpensive and easy to obtain.20 whereas other models included early breast cancer stage. As expected, in the study by Fogarassy et al,18 advanced cancer with distant metastases was confirmed as a significant predictor associated with heart failure. This likely reflects the choice of chemotherapy in addition to its potential impact on cardiotoxicity progression. Pinder et al25 published a large analysis of the Medicare database for patients with breast cancer older than 65 years, and identified advanced cancer stage as significant predictor for heart failure. In addition, Cho et al26 found that metastasis increased doxorubicin-induced cardiotoxicity by 2.66-fold and concomitant trastuzumab increased it by 4.08-fold. In the metastatic setting, ERBB2-targeted therapies are used until disease progression or toxicity.27 These findings suggest that cancer stage may be relatively important predictor in cardiotoxicity prediction models.Two models included patients with advanced cancer stage ,18 utilized a 4-point MACE outcome. Although MACE is an increasingly common and standardized primary outcome of interest in randomized controlled trials, there are potential limitations in studies using administrative databases. A systematic review reported substantial heterogeneity for the MACE composite endpoints used in studies based on administrative databases.28 “Acute myocardial infarction and stroke” and “acute myocardial infarction, stroke, and all-cause death” were the 2 most common composite MACE definitions. This diversity made it challenging to compare findings across studies or to aggregate multiple study results for meta-analyses or systematic reviews when considering different treatment or research questions.28There was significant heterogeneity in the definition of cardiac events and follow-up. One study used a MACE outcome to develop a model to estimate the risk of MACE in women with breast cancer. Kim et al29 while the cutoff is 50% according to the European Society of Cardiology.5 In the past, the definition of cardiotoxicity related to chemotherapy was restricted to ventricular dysfunction. However, the introduction of new treatments in the last few years has widened the concept of cardiotoxicity to a broader definition of cardiovascular toxicity, which incorporates arterial hypertension, ischemia, cardiomyopathy, myocarditis, arrhythmic complications, long QT, and arterial and venous thrombosis.30 Future studies should therefore include a uniform and established definition of cardiotoxicity and a longer follow-up period to determine the effects of cancer treatments on long-term cardiotoxicity risk.The diagnosis of cardiotoxicity is currently defined as a greater than 10% reduction in the ejection fraction (EF) and an absolute value of less than 53% according to the American Society of Echocardiography (ASE) and the European Society of Cardiovascular Imaging (EACVI),31 Moreover, because in different patients, the same biomarker level could have a different meaning depending on whether it is stable or rising, repeated measurements of multiple biomarkers reflecting different pathophysiological pathways may convey incremental prognostic value to single or even repeated biomarker measurements of established biomarkers.32 The cumulative dose of anthracyclines has been proven to be an important factor associated with cardiotoxicity in patients receiving anthracycline-containing therapy.34 In our study, models that included both the cumulative dose of anthracyclines and several comorbidities showed a better predictive performance than models that did not include these variables, except for the study by Goel et al,19 that had excellent discrimination using only baseline and change in LVEF. This suggests that the cumulative dose of anthracyclines could play a large role in the risk of cardiotoxicity in patients with breast cancer.Most predictors included in the 6 prognostic models of this review were clinical variables. Incorporating a greater number of variables such as biomarkers (cardiac troponin and natriuretic peptides) or genetic information  is another potential way to improve the prediction tool.24 Although 5 studies conducted internal validations, only 2 studies21 applied internal validation properly by using bootstrapping techniques to overcome overfitting. In addition, although the demonstration of the performance of a model in an independent population is a necessary step before recommending its widespread use, only one of these models was externally validated.35 Inappropriate EPV for sample size can cause high risks of overfitting and biased predictions.36 EPV above 20 is recommended as the minimum sample size for model development37 and validation samples should contain at least 100 individuals who develop the outcome of interest.38 In this review, the sample size of 3 development models (EPV below 20) and the external model validation (less than 100 events) was not appropriate.Model overfitting and the resulting optimism are important concerns in prediction models. We can define overfitting specific to prediction models as fitting a statistical model with too many degrees of freedom in the modeling process.While the key messages were consistent, 4 included studies were at high overall ROB. One predictive model was only validated once, and we did not consider there to be enough information to assess its suitability in different situations. This review reveals that there is insufficient evidence to provide personalized risk prediction of cardiotoxicity related to breast cancer treatment in clinical practice.To the best of our knowledge, this is the first systematic review to focus on prediction models of cardiotoxicity in patients with breast cancer, including a thorough evaluation of the ROB and reporting quality. A main strength of this study is the adoption of the PROBAST and TRIPOD guidelines as benchmarks.However, we acknowledge that there were several limitations to this systematic review. We only included studies published in English and did not search gray literature. However, it is unlikely that any missed studies would have significantly changed our main findings. Another weakness was that we were unable to perform our planned meta-analysis due to the lack of eligible studies.This review identified 6 prognostic models developed to predict risk of cardiotoxicity following treatment in women with breast cancer. The prediction models were designed in diverse clinical settings and populations, and with a range of included predictors. We are not yet able to accurately predict cardiotoxicity outcomes for a given patient with breast cancer based on their clinical parameters. Most of the models identified in this review fell short in addressing bias due to methodological weaknesses. To improve the overall quality in future predictive models, researchers should adhere to best practices in the development process. Investigators should also be aware of the potential benefits and weaknesses of various methods for model creation and internal validation. More external validation studies are needed to assess the performance and clinical applicability of existing models before they can successfully be used in clinical practice.


This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with the risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. A reduction in the number of features chosen for subsequent analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these features to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. Following these recommendations could enhance the applicability of models, helping improve risk classification and risk prediction for women to tailor screening and prevention strategies to the level of risk.The online version contains supplementary material available at 10.1186/s13058-022-01600-5. Evolving technology from film mammograms to digital images has changed the sources of data and ease of access to study a range of summary measures from breast mammograms and the risk of breast cancer , 2. ThesThe leading measure for long-term risk categorization extracted from mammograms is breast density , 12. ThiIn 2016, Gastounioti and colleagues summarized the literature at that time to classify approaches to parenchymal texture classification: (1) gray-level features—skewness; kurtosis; entropy; and sum intensity, (2) co-occurrence features—entropy; inertia; difference moment; and coarseness, (3) run-length measures gray-level non-uniformity and run-length non-uniformity, (4) structural patterns measures lacunarity, fractal dimension, and (5) multiresolution spectral features . They coIn current medical practice, risk prediction is viewed as an objective way to assess the risk of a patient developing a disease such as a 10-year risk of cardiovascular disease . Risk prWe aim to summarize the methods used to classify mammographic breast parenchymal features in relation to the prediction of future breast cancer, the time from mammogram to the diagnosis of breast cancer, the analysis of data from either one or both breasts , the study design, and the statistical methods for estimating the association between features and risk. We then identify gaps in evidence to prioritize future studies and speed us to better support the precision prevention of breast cancer.We considered all studies of adult women (at least 18 years old) involving original data. Abstract-only papers, review articles, and conference papers were excluded.We included studies measuring at least one non-density mammographic feature. A study had to explicitly define the mammographic features included. Studies that did not do this, such as those which used a deep model, were excluded because we would be unable to determine the characteristics of features used and compare these to other studies.We compared models using parenchymal texture features to those that did not.Our primary outcome of interest was the risk of breast cancer, including both invasive and in situ cancers. The risk of breast cancer was required to be dichotomized (yes/no). Analysis of other risks  and studies examining the association between mammographic features and other risk factors were excluded to narrow the scope of our paper.Only studies available in English were included. Additionally, only studies published from 2016 onward were included to avoid overlap with previous reviews.The published literature was searched using strategies designed by a medical librarian for the concepts of breast density, mammography, and related synonyms. These strategies were created using a combination of controlled vocabulary terms and keywords, and were executed in Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov. Results were limited to English using database-supplied filters. Letters, comments, notes, and editorials were also excluded from the results using publication type filters and limits.('breast density'/exp OR ((breast NEAR/3 densit*):ti,ab,kw OR (mammary NEAR/3 densit*):ti,ab,kw OR (mammographic NEAR/3 densit*):ti,ab,kw)) AND NOT AND [english]/limAn example search is provided below (for Embase).The search was completed for the first time on September 9, 2020, and was run again on October 14, 2021, to retrieve citations that were published since the original search. The second search was dated limited to 2020-present (October 2021). Full search strategies are provided in the Additional file Two reviewers  worked independently to review the titles and abstracts of the records. Next, the two reviewers independently screened the full text of the articles that they did not reject to determine which were eligible for inclusion. Any disagreements about which articles to include were resolved by consensus. Two reviewers  then went through this subset independently and excluded the ones without explicitly defined features.We created a data extraction sheet that two reviewers  used to independently extract data from the included studies. Disagreements were resolved by a third reviewer. If included studies were missing any desired information, any additional papers from the works cited, such as previous reports, methods papers, or protocols, were reviewed for this information.p value. No restrictions on follow-up time were placed. For studies that reported multiple risk estimates, we prioritized the area under the curve with the most non-mammogram covariates included from the validation study if applicable. If the study did not report an area under the curve, we listed the primary models which were discussed in the Results section of the paper.Any estimate of the risk of breast cancer was eligible to be included. Risk models could combine multiple texture features or examine texture features individually. The predictive ability could be evaluated using an area under the curve, odds ratio, matched concordance index, hazard ratio, or the report: author, publication yearthe study: location/institution, number of cases, number of controls, study designthe research design and features: lapsed time from mammogram to diagnosisthe mammogram: machine type, mammogram view(s), breast(s) used for analysisthe model: how density was measured, number of texture features extracted, types of texture features extracted, whether feature extraction was machine or human, whether all features were used in the analysis, how features for analysis were chosen, non-mammogram covariates included, established confounders for density, prediction horizon, statistical methods for assessing risk associationWe collected data on:The quality of the included studies was assessed using the Quality in Prognostic Studies (QUIPS) tool . Risk ofThis study did not involve human subjects, and therefore oversight from an Institutional Review Board was not required.This review was not registered and a protocol was not prepared.The search and study selection process is shown in Fig. Of the 5329 unique citations, 5124 were excluded based on the review of the title and abstract. Two hundred and five full-text reports were retrieved and assessed for eligibility by two readers. Of these, 177 were excluded for reasons such as not measuring a non-density feature, not reporting risk of breast cancer as an outcome, being an abstract or a duplicate paper, or not being published in English.We identified 28 studies published since 2016 that met eligibility criteria as set out in the selection flowchart –52.Of the 28 studies, only 7 were based on digitized analog film images , 47–49, Tables The approach to the assessment of breast parenchymal texture and side of body  varied across studies. Almost half of the studies (13 out of 28) used BIRADs as the baseline measure of density , 48–50. We next assessed the value added from the addition of texture features to prediction models for breast cancer. As noted in Tables Reporting on established confounders for mammographic density varied across studies, with some accounting for age, body mass index, hormone therapy, and menopausal status while others considered fewer or even none in analyses. The prediction horizon, or how far ahead a model predicts breast cancer, was only defined for 3 of the studies , 30, 32 Not all studies reported concordance statistics for their analysis, a metric to evaluate the predictions made by an algorithm defined as the proportion of concordant pairs divided by the total number of possible evaluation pairs. We summarized six studies in Table Results from the assessment of the risk of bias are shown in Additional file Study reporting impacted our ability to rate the risk of bias. None of the studies reported information to judge study attrition risk of bias, leaving all with an unclear risk of bias. Likewise, for the study population, most studies did not report on the characteristics of the source population making it difficult to determine whether the population of interest was adequately represented.This review examined studies that evaluated the value of adding texture features to predict future breast cancer incidence or reported the association between these features and risk, of which we identified 28. Our findings suggest that risk prediction model performance increases when breast texture measures are added to mammographic breast density. The majority of studies evaluating future risk were limited to less than 3 years. Evidence using a longer interval from mammogram to diagnosis is necessary to be sufficient to guide breast cancer risk management or prevention , 53–56. The use of machine learning approaches to overcome variability in the assessment of BD has been reviewed recently . The chaThere have been some efforts on validating the texture features included in the studies in the literature. For example, the Tabár classification of parenchymal patterns has been shown to be highly reproducible  and seveThere are several limitations to the studies included in this review. There was a large amount of variability in reporting established confounders of density, raising the issue of study confounding among studies that did not account for these established risk factors for density measures. Methods used may be nongeneralizable due to study specificities such as using different types of mammograms and different racial/ethnic populations. With short time periods between mammography and diagnosis of cancer, it is difficult to know whether studies using the ipsilateral breast are predicting breast cancer or merely detecting it. Some studies only provided evidence of an association rather than assessing the prediction performance of future risk, limiting their clinical translation. Large sample sizes are needed to validate associations, and a few studies had a small number of cases. Variation in the risk of bias observed in these studies reflects the variation in methods particularly in consideration of confounding. Level of reporting impacted our ability to fully assess the risk of bias in these studies.We observed 5 studies using measures of texture that were measured or defined at the time of cancer diagnosis and from the contralateral breast , 45, 52.To more effectively improve risk classification and prediction, we note the need for a harmonized strategy for assessing features across studies. The measurement challenge is a resource providing common mammogram images to identify the best method to measure mammographic density and predict breast cancer risk . A similIn the 2016 review, Gastounioti et al. reviewed features of automated parenchymal texture analysis in relation to breast cancer risk . This inRegarding our methods for this updated systematic review, we used the QUIPS tool  to assesFor clinical use to guide precision prevention, we must identify both high-risk women for a range of risk reduction strategies  and low-The addition of parenchymal texture features to risk prediction models generally resulted in improved performance beyond mammographic density. Despite current limitations in the literature, the more widespread use of digital mammography and availability of digital images including parenchymal features offer a growing opportunity to more uniformly assess image texture features. Incorporating these features into risk prediction models can improve risk classification and risk prediction, leading to improved breast cancer risk management such as tailoring screening interval and prevention strategies to the level of risk.Additional file 1. Risk of bias assessments for 28 included studies using QUIPS (sorted by year). Complete search strategy. PRISMA checklist.


In contrast, TG , total cholesterol (TC) , apolipoprotein A (ApoA)  and LDL-C  were not associated with breast cancer development. In studies adjusting for hormone use and physical activity, breast cancer risk was positively correlated with TC . Similarly, TG was significantly related to breast cancer development after adjustment for baseline lipids  and race (any races mentioned in each study) . In the present meta-analysis, HDL-C was inversely related to breast cancer risk. Overall, data on the links between lipids and breast cancer are conflicting. However, there is increasing evidence that low HDL-C is related to an increased risk for this type of malignancy.Dyslipidemia has been linked to breast cancer incidence. The aim of the present meta-analysis was to further investigate the relationship between the serum lipid profile and breast cancer risk. Databases such as PubMed, EMBASE, and Web of Sciences were searched up to the end of January 2021 using certain MeSH and non-MeSH keywords and combinations to extract related published articles. Twenty-six prospective studies involving 1,628,871 women, of whom 36,590 were diagnosed with breast cancer during the follow-up period met the inclusion criteria. A negative and significant association was found between the HDL-C level and the risk of breast cancer (relative risk (RR): 0.85, 95% CI: 0.72–0.99, I Breast cancer is the most frequently diagnosed tumor in women according to the World Health Organization (WHO) . AccordiA link between dyslipidemia and breast cancer has been reported. For example, increased levels of triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C) and very low-density lipoprotein cholesterol (VLDL) were observed in breast cancer patients compared with normal controls ,4. HowevThe relationship between high-density lipoprotein cholesterol (HDL-C) and the risk for breast cancer remains unclear with some studies reporting an inverse association and others reporting the opposite or no association at all ,8,9,10. The aim of the present systematic review and meta-analysis was to further investigate the relationship between the serum lipid profile and breast cancer development.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to conduct the present systematic review and meta-analysis. This meta-analysis is registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42021281278.We searched for papers published up to the end of January 2021 in databases such as PubMed, EMBASE, and Web of Sciences (ISI). The search strategy used MeSH and non-MeSH keywords and combinations to extract related published articles. The keywords used were “TC”, “HDL-C”, “LDL-C”, “TG”, “Apolipoprotein”, “lipoproteins”, “cholesterol”, “triglyceride”, “dyslipidemias”, “lipid profile”, “lipid component”, “blood lipid”, “plasma lipid”, “serum lipid”, “plasma lipoprotein, “dyslipoproteinemia”, “hypercholesterolemia”, “hypertriglyceridemia”, “hyperlipidemia”, “lipemia”, “ApoA”, “Apolipoproteins A”, “ApoB”, “Apolipoproteins B”, and “Metabolic syndrome”. Breast cancer was defined using the terms “breast neoplasm”, “breast cancer”, “breast tumor”, “breast tumour”, “breast malignancy”, and “breast carcinoma”. Furthermore, “Cohort”, “Prospective”, “Longitudinal”, “Follow-up”, “Nested”, and “Population-based” terms were used to limit the findings to cohort studies. No automatic filtering of databases was used during the database search. The references of papers were also checked. No time or language limitations were applied. The reference list of eligible articles was further searched, and authors were contacted by email for additional data, if needed.Original articles that fulfilled the following criteria were included in the present systematic review and meta-analysis: (1) cohorts with a prospective design (the exposure takes place before the outcome), (2) participants free of cancer at baseline, and, (3) investigations of the relationship between the lipid profile and the risk of developing breast cancer.After excluding duplicates and based on titles and abstracts, we excluded animal studies and those involving humans aged ≤ 18 years. In addition, supplementary hand searching of the reference lists of previous reviews or meta-analyses was conducted. Of 93 eligible full articles, 26 articles met the inclusion criteria .p < 0.001). Disagreements were resolved at a meeting between reviewers prior to selected articles being retrieved  blinded to the names, qualifications and institutional affiliations of the study authors. Agreement between the reviewers was excellent . A study quality assessment was performed according to the Newcastle-Ottawa Scale . In contrast, TG  and physical activity . Similarly, TG was significantly related to breast cancer development after adjustment for baseline lipids  and race (any race mentioned in each study)  added in .p = 0.542) and Begg’s rank correlation test  indicated the absence of a publication bias. After adjustment of the effect size for the potential publication bias using the ‘trim and fill’ correction, no potentially missing studies were added to the funnel plot for HDL–C, TG, TC or LDL–C.Visual inspection of the funnel plot symmetry suggested no potential publication bias for the comparisons of HDL–C (Egger = 0.237), TG (Egger = 0.069), TC (Egger = 0.480) and LDL–C with the risk of breast cancer . Further2: 52%) [2: 65%) [The present systematic review and meta-analysis evaluated the associations between lipid parameters and the risk of developing breast cancer in women. Only HDL-C was found to be significantly related to breast cancer development with an RR value of 0.85, thus highlighting the potential preventive effect of elevated HDL-C levels. Similarly, low HDL-C levels (<77 mg/dL) were correlated with an increased risk of breast cancer in the Copenhagen General Population Study [2: 52%) , and the[2: 65%) . In the [2: 65%) . Therefop = 0.02) and HDL-C  correlated with an increased breast cancer risk [In the present meta-analysis, TC, TG and LDL-C were not found to be related to breast cancer risk. Similar results were reported in the abovementioned previous meta-analyses conducted by Touvier et al.  and Ni ecer risk .As already mentioned, in the present meta-analysis, after adjustments for hormone use and physical activity, TC was found to be positively associated with breast cancer risk, as was TG when adjusted for baseline lipids and race. Overall, there are conflicting data regarding the links between lipids  and the risk for breast cancer . For exaApoA was also not significantly related to breast cancer risk in the present meta-analysis. Similarly, no association has previously been observed between ApoA1 and breast cancer incidence , whereasThis study had some limitations. First, studies used several designs to define associations. Secondly, adjusted covariates differed between the included studies and this might have increased the risk of a confounding bias. Third, cutoff points for the first and last categories varied, and this might have led to study variation.In conclusion, the present meta-analysis found a significant inverse association between HDL-C and the risk of breast cancer development. TC, TG, LDL-C and ApoA were not found to be significantly correlated with breast cancer development. Hormone use and physical activity affected the relationship of TC with this malignancy, as did race and baseline lipid values for TG. Data on the role of lipids on breast cancer risk are generally conflicting, with low HDL-C usually being related to an increased risk. Further basic and clinical research is required to elucidate the associations between specific cholesterol components and breast cancer risk in certain populations as well as the exact mechanisms underlying the lipid-related signaling pathway involved in the development of this malignancy. Such data will provide evidence on the potential clinical use of lipid-modifying drugs in relation to breast cancer prevention.


BRCA1 or BRCA2 mutation carriers consider RRM, but that carriers of moderate penetrance mutations  should be managed based on family history. We aimed to investigate determinants of decision for RRM, and hypothesized that mutation status, age, family history, partner status, and breast cancer would impact RRM decision making.Women harboring mutations in breast cancer susceptibility genes are at increased lifetime risk of developing breast cancer and are faced with decisions about risk management, including whether to undergo high‐risk screening or risk‐reducing mastectomy (RRM). National guidelines recommend We performed a retrospective study assessing RRM decisions for 279 women.BRCA and moderate penetrance gene mutations, a personal history of breast cancer, or a first degree relative with a history of breast cancer were more likely to undergo RRM. Breast cancer status and age showed an interaction effect such that women with breast cancer were less likely to undergo RRM with increasing age.Women with BRCA mutation carriers. Further insights are needed to better support RRM decision‐making in this population.Although national guidelines do not recommend RRM for moderate penetrance carriers, the rates of RRM for this population approached those for  Surgical decisions varied between women with and without breast cancer and by mutation status. Our study showed higher rates of RRM among breast cancer patients than among unaffected patients and higher rates of RRM by mutation risk level in both affected and unaffected patients. The genes BRCA1 (OMIM accession number: 113705) and BRCA2 (OMIM accession number: 600185) were discovered in the mid‐1990s and are considered “high penetrance” because mutations confer, by some estimates, a greater than 80% lifetime risk for female breast cancer , and are advised to undergo risk‐reducing oophorectomy after completion of childbearing  carriers, the NCCN Guidelines® (v.3.2019) recommended annual mammogram and consideration of breast MRI starting at age 40 but cautioned that evidence was insufficient for recommending RRM which should be “based on family history”    Li, . However Honold, . Uptake  Honold, . Further Honold, . Decisio Honold, .BRCA mutation carriers. Country of origin, timing of genetic testing, screening discomfort, cancer worry, family history, and the “Angelina Effect” observed after the public announcement of bilateral RRM by the actress Angelina Jolie, are suggested to impact surgical decision for unaffected BRCA mutation carriers , CHEK2 (NM_001005735.2), NBN (NM_001024688.3), or PALB2 (NM_024675.4)), high penetrance gene (BRCA1 (NM_007294.4) or BRCA2 (NM_000059.4)), or having no known mutation identified on hereditary cancer panel testing.Gene mutation group status was defined as carrying a mutation (pathogenic or likely pathogenic variant) in a moderate penetrance gene  was somewhat lower than the proportion of affected, eligible moderate penetrance gene carriers (17%). The “no mutation” status arm group was populated using a list of 813 patients who tested negative on genetic testing. A random integer generator was used to select a comparator group in a systematic random selection until 30 eligible subjects with breast cancer and 62 eligible women without breast cancer were accrued.In order to populate our moderate penetrance group a list of genetic test results in the defined time period was filtered to include mutations in 2.3We performed a retrospective review of electronic medical records. Demographics, cancer history, mutation status, breast cancer risk, detailed personal history, three‐generation family history, surgical decision, and previous genetic testing were abstracted from each patient. Data on each patient was collected at the time of her genetic test result disclosure. Age at surgical decision was defined as the age at genetic test results.2.4Following descriptive analysis of the study population, we performed univariate analysis to examine the relationship between the surgical decision and each risk factor considered in this study, using Pearson's chi‐square test for categorical risk factors and Wilcoxon rank sum test for continuous risk factors.Multivariable logistic regression modeling was performed to examine the joint effects of patient variables on the surgical decisions. Model selection by likelihood ratio test showed that a model including interaction effect between age and breast cancer was optimal to study the joint effect of age, family history (whether a first degree relative was affected with breast cancer), mutation status, partnered status and breast cancer status on RRM decision. After applying bootstrap cross validation, the bias‐corrected rank correlation between the predicted probabilities and the observed responses is 0.8025; the bias‐corrected c index (AUC) is 0.9013 Table .Log‐likelihood test was used for final model selection. The main and interaction effects were reported by odds ratios (ORs) and 95% confident intervals (95% CIs). Interaction plot was also provided to assist a better understanding of the effects. The predictive performance of the final model was assessed by the bias‐corrected c‐index (AUC).33.1ATM, 54 CHEK2, 3 NBN, 9 PALB2, 51 BRCA1, and 46 BRCA2 carriers in addition to 92 patients who did not harbor a mutation. Results showed that 40.2% (39/97) of BRCA mutation carriers, 30% (27/90) of moderate penetrance gene carriers, and 8.6% (8/92) of women with no mutation underwent RRM. Patient demographics by RRM decision are listed in Table p < .001), the younger they were diagnosed (p < .001), if they carried a high or moderate risk mutation (p < .001), or if they had a partner (p = .02)  had a family history of breast cancer in a first, second, or third degree relative. In fact, 13 (48.1%) of the 27 women with a moderate risk mutation who underwent RRM had a first degree relative with a history of breast cancer. Each of the 7 women with moderate risk mutations with no personal history of breast cancer who underwent RRM had a first degree relative (FDR) with breast cancer Table .3.2A multivariable model was developed to determine whether age, breast cancer diagnosis, first degree relative with breast cancer, mutation, and partnered status impacted RRM decision.Odds ratios for this model are demonstrated in Table BRCA carriers and 7.967  times higher for carriers of moderate penetrance genes. Women with BRCA mutations were 2.623 times as likely to undergo RRM as women with mutations in moderate penetrance genes . Age was only a predictor of RRM among women with breast cancer. Among women with a diagnosis of breast cancer, the odds of undergoing RRM for older patients (75% quantile) compared to younger patients (25% quantile) was OR = 0.097   of PALB2 and 58% (7/12) of ATM/CHEK2 carriers with breast cancer underwent RRM compared to 57% (4/7) and 29% (2/7) of those without breast cancer, respectively  and the rate of RRM also differs by country (Metcalfe, NBN and PALB2 as moderate penetrance genes. Two large population‐based, case–control studies published in early 2021 did not find an association between germline NBN mutations and breast cancer risk, in contrast, the odds ratios reported for PALB2 mutations ranged from 3.83 to 8.04, depending on family history of breast cancer (Breast Cancer Association Consortium, PALB2 carriers have shifted from “RRM: Evidence insufficient, manage based on family history” in 2019 to “RRM: discuss option of risk‐reducing mastectomy” in 2020. The categorization of these genes in the moderate‐penetrance gene category aligns with the guidelines for surgical management for carriers of pathogenic variants in these genes during the study period, which was based on the understanding of the risk conferred by these genes at that time. When the study patients were tested and counseled, evidence for RRM in PALB2 and NBN was considered insufficient, and NCCN guidelines stated that providers should manage carriers of mutations in these genes based on family history. Given that the surgical recommendations for PALB2 and NBN carriers matched those of ATM and CHEK2 carriers, patients carrying pathogenic variants in these four genes populated the “moderate penetrance” group.Recent publications have challenged the classification of both 5This retrospective study evaluated several potential factors associated with a patient's decision to undergo RRM including mutation status, first degree relative with breast cancer, partnered status, age, and personal breast cancer diagnosis. The multivariable model revealed that these factors impact a woman's odds of electing RRM, although partnered status was not statistically significant. This study was unique in that it compared rates of RRM for moderate risk mutation carriers to “no mutation” and high‐risk mutation carriers, and the results suggest that carriers of moderate risk mutations commonly elect for RRM even without clear guidelines to support this approach. Surgical decisions are multifactorial, but providers should aim to identify the root causes of a decision for RRM in a moderate risk carrier by identifying and addressing any knowledge gaps. This study underscores the importance of educating care providers and patients on moderate risk mutations and providing access to genetic counseling wherever feasible, so that patients can be educated about genetic risk and empowered to make informed decisions about RRM.This study would not have been possible without all the authors listed. Each author provided support with study concept, data abstraction, analysis, and write‐up.The Neuroscience‐based Interventions for Cancer Risk Behavior Change NIH Grant R35CA197461 and NCI award P30CA014089 provided support for this research.The authors declare that there are no competing interests.This study was granted exemption by the Samuel Oschin Comprehensive Cancer Institute Protocol Review and Monitoring Committee on 5/30/2019. It was granted IRB approval by the Cedars‐Sinai Office of Research Compliance and Quality Improvement on 6/3/2019 under title name STUDY00000014: IIT2019_MCARTHUR_CHEK. Patients were not consented to this study given that it was a retrospective review study. Documentation of approval is available by request.Appendix S1 Supporting informationClick here for additional data file.


Purpose: Participants’ engagement in a project requires intrinsic motivations, which may evolve during the intervention thanks to lifestyle changes and positive challenges. Over the years, tailored programs based on physical activity and psychological sessions have been promoted to support the quality of life and well-being of breast cancer survivors. Personal expectations and needs are essential to predict participant adherence to the intervention as well as their possibility to reach positive outcomes. This study presents a preliminary understanding of the differences between motives and outcomes obtained after an integrated physical and psychological intervention conducted by professional trainers and psychologists. Methods: Forty-five women with a history of breast cancer answered some questions before and after the program, and the results were analyzed in accordance with the procedure of the thematic analysis. Results: Physical and psychological well-being are the two main themes that emerged from the participants. Interestingly, some differences emerged between the two data collections. Themes such as aesthetic evaluation interest and the need to learn psychological strategies disappeared at the end of the intervention; on the other hand, the need to make a distance from the illness experiences emerged as an obtained outcome. Conclusions: The discussion explains these differences and highlights the importance of considering breast cancer patients’ needs and motives to take part in interventions to promote quality of life. Receiving a diagnosis of breast cancer exposes people to physical and psychological consequences that persist over time and affect their quality of life. A patient’s path does not end with successful physical care but continues even after treatment. In this regard, people have to deal with negative emotions related to the diagnosis , changes in lifestyle and social relationships, and possible long-term side effects of oncological care on survivors’ health  ,2. In adThus, it is of paramount importance to support breast cancer patients, helping them to overcome the emotional trauma related to the diagnosis, manage their emotions, and re-define their life after cancer ,6. In thIn Italy, some integrated interventions based on physical activities and psychological support to improve quality of life in cancer survivors were promoted . In the The psychological literature highlights that the involvement of cancer survivors in interventions often requires intrinsic motivation, which often appears to have a dynamic nature ,15. MotiOn this basis, the present study aims to qualitatively explore the initial motivations and perceived outcomes in female cancer survivors’ participation in two different programs that combine sports activities and group psychological support to promote quality of life. In the context of the above-mentioned integrated intervention, a qualitative thematic analysis was carried out to assess the motivations and outcomes of participants who took part in the intervention. The analyses highlighted the main themes (and sub-themes) of internal motivation that participants showed at the beginning of the intervention and the perceived outcomes that participants reported at the end of the project.ageM = 50.24; ageSD = 6.75). All the participants included in this study met the following inclusion criteria: (1) adult participants (2) women who received a diagnosis of breast cancer in the past few years, and (3) participants that were not actually under oncological treatment. Women who showed cognitive impairment, physical or psychological impairments that prohibited their participation in the study, or an inability to understand the study or to sign the informed consent were excluded from this research.A total of 45 women with an oncological history were invited to take part in this study (The majority of participants had attained a high school education (59.9%), lived in the North of Italy (74.5%), and worked as white-collar employees (92.7%). Additionally, more than half of the sample participates in physical activities (60.5%) and is not involved in individual psychotherapy (63.6%) see .Participants voluntarily took part in an intervention based on physical exercises and a psychological program that aims to support well-being and quality of life through physical and psychological sessions with an expert psychologist.ageM = 50.13; ageSD = 8.00) participated in a group intervention that combined physical/postural exercises with psychological group interventions for a total of 13 sessions ; 19 women  participated in a group intervention coupling sailing boat courses with psychological group interventions for a total of eight sessions .The sample was constituted as follows: 26 women  were tailored on the basis of participants’ needs and abilities. In particular, physical activities lead to the promotion of a positive body image , emotionThe invitation to participate in the programs was posted on social networks and sent via a commercial mailing list. Breast cancer survivors who accepted to participate received the information sheet and informed consent form. At the beginning and end of the intervention, the participants were invited to answer some open questions. Specifically, at the beginning of the program, participants described their initial motivations to participate in the program with the help of some stimulus questions . At the end of the program, the same participants described the main outcomes they obtained thanks to the participation in the project . All the textual answers were collected through Qualtrics software. Participants responded to the questions individually; no time or word-limit constraints were given.All the text answers provided by participants were downloaded from Qualtrics as a .xls file. All the reports were in Italian, and all the references to people, locations, and dates were anonymized. The length of the answers was heterogeneous, ranging from a few-word statement to a paragraph of 280 words for motives and 223 words for outcomes.In the first phase, two authors (V.S. and D.M.) read each text many times, familiarizing themselves with the content.In the second phase, authors conducted an initial coding of the data; codes were used to identify segments of the textual reports (semantic content). If one answer reported a single main motive or outcome, it was considered a single segment. This initial coding procedure was run with Microsoft Excel.In the third phase, the different codes were grouped into potential main themes (and sub-themes). Discrepancies between the raters were resolved through a discussion between the authors.In the fourth phase, we developed and reviewed the themes. Specifically, the thematic map was reviewed by two authors (V.S. and D.M.).In the final phase, themes were labeled, providing an explicit definition of their themes. To further validate the thematic map, one author who did not take part in the previous phases of analysis (I.D.) reviewed the entire process and the identified themes.The stages of the analytical process followed the main phases of qualitative thematic analysis, as described by Braun and Clarke . Three rThe authors had several meetings to discuss and define the themes. Five main themes were identified, with 12 sub-themes for motivations and 10 sub-themes for outcomes.The results of the analytical process are presented in this section. Five main themes, 12 sub-themes for motivations, and 10 sub-themes for outcomes were identified . AccordiThe first main theme is physical well-being. Women included in this study highlighted the need to promote physical well-being through their participation in the integrated intervention sessions.promote their physical well-being and their relaxation. For example, a woman who received, in the past, a breast cancer diagnosis stated that she participated in this intervention in order to: “improve my well-being and do physical activities in order to avoid incorrect body postures (ID11)”. Similarly, other participants declared that their aims were to: “lengthen and stretch the body to reduce physical pains and lymphedema (ID12)” and “decrease the bodily tensions (ID25)”. Additionally, another sub-theme was the promotion of body awareness. Women reported their desire to stay in contact with their body and related inner sensations in order to improve their self-awareness ” and “Tone my muscles and be aware of my body. I need to know my bodily sensations (ID6)”). Lastly, some women included in the project reported that their initial motivation to participate was to increase their aesthetic evaluation in order to improve their self-acceptance ”).Specifically, before starting the intervention, some breast cancer survivors reported their need to do physical activities to body sensations and perceptions. For example, a woman stated that: “I feel well, the intervention sessions were a support and an effective method of relaxation (ID1)”. Moreover, several women perceived lower body tension and more relaxation ” and “I perceive more relax related to neck; I will continue with physical exercises twice a week before sleeping (ID8)”. At the same time, participants evidenced improvements in their body awareness, in terms of available physical movements, as reported: “I became more aware of the possibilities of making some movements, also trying to make physical movements that I had not done in a long time. Moreover, the comparison with the other women of the group helped me to reflect on aspects of myself that I have not ever explored before (ID6)” and “Participating in this project, I learned to be aware of body parts that I felt in tension (ID14)”. We also noticed that few breast cancer survivors demonstrated other physical benefits, which are not associated with the intervention directly, for example: “Thanks to this intervention, I learned to relax some sore parts of my body, improving my quality of sleep (ID14)”. This highlights the possibility of reaching other goals that were not previously set at the beginning of the intervention. Moreover, related to the differences between the initial motivations to participate in the program and the final outcomes, we noticed that the aesthetic aspect theme disappeared.After engaging in the intervention, breast cancer survivors reported improvements in their physical well-being. From the words of some participants, it emerged that the integrated intervention helped women to promote their self-awareness of their inner world in order to improve their understanding of emotions, thoughts, and sensations. For instance, women with a history of breast cancer reported their desires to promote psychological abilities to “try to give a meaning and put a name to my feelings (ID14)”. In line with this, a women reported: “I chose to participate in this program because I would like to have more confidence in myself. Additionally, I want to be aware and recognize my fragility, have the opportunity to share my thoughts and experiences, and to promote healthy habits to appreciate myself and my life (ID14)”. These motives were strictly associated with participants’ desires of promoting psychological abilities to cope with negative emotions, as reported: “Understanding my anxiety and anger that are always present in my life (ID25)” and “Gaining more awareness of myself, of my resources and my limitations; in other words, improving and accepting myself (ID20)” in order to promote “A greater acceptance of myself, the desire to see me with different eyes and react to a difficult period of my life (ID24)”. Additionally, participants highlighted their initial motivation to take part in the intervention to have time to make new experiences, far from their usual everyday life. For example, women reported motivations linked to the desire to “Stay with myself and challenge myself (ID34)” and “Take time for myself. Develop a better awareness of my limits and abilities and do what I like (ID40)”.Psychological well-being is the second main theme reported by participants. One of the main motivations for being engaged in the integrated intervention was focused on achieving self-awareness, reporting to obtain “More awareness and serenity because I have worked on myself by facing some negative thoughts about myself (ID24)” and “Now, I feel myself free from a burden. I have less anxieties and useless thoughts. I feel I have new abilities to deal with some of my difficulties and stay in contact with others. I have learned to better understand myself and my emotions (ID35).” In line with initial motivations, some participants reported that this intervention helped them to both overcome negative emotions ” and take time for themselves (“I have better confidence in myself and my abilities. I learned to see myself from the outside by not affecting my judgment with limiting thoughts. I learned to take time for myself and meditate on my life (ID16)”).Referring to outcomes after interventions, breast cancer survivors demonstrated a general satisfaction about their psychological well-being. Participants reported better overcome the illness experience and improve their ability to turn negative feelings into positive growth. A participant wrote: “After the second diagnosis of cancer, I could not elaborate my illness because I experienced fear and anger. I decided to participate instinctively because I felt the need to do something for myself (ID39)” and “I want to live a unique experience, thinking about my illness in a positive way and comparing myself with other people who received my same diagnosis. Talking about my illness, listen other experiences, and take a break from my daily routine could be useful for me…I think that staying away from my daily routine could help me to understand who I am and what I feel (ID41)”. Similarly, other participants evidenced physical and emotional challenges  because I think that people who received a worse diagnosis than me  have to face strong difficulties. At the same time, I know that the expression of my emotions can help me (ID9)”). In addition, they directly expressed the desire of learning psychological strategies to face everyday life difficulties, as reported in the following quotations: “My goal is to improve my self-esteem and, in particular, acquire useful “tools” to accept my illness and my body changes. I realize that my self-esteem sometimes falters, sometimes I feel discouragement, especially when pain and edema in the arms intensified, or when I wake up tired. These moods affect my well-being (ID9)”.Participants’ motivations were also related to their needs to self-awareness and skills to face physical and psychological issues ”. Another interesting outcome referring to coping with the illness abilities is the new perception of being a woman, and not only a patient. This is relevant in terms of self-representations; in other words, breast cancer survivors evidenced a new focus on themselves as people, with needs, desires, and goals. In particular, they put themselves at the center of their lives ”). Participants also reported the desire to distance themselves from the idea of being an ill person ”).After the intervention, the main outcome reported was related to improvements in the overall illness acceptance, in line with the motives. Breast cancer survivors evidenced improvements in sharing their illness experience ” and “I have the desire to share my experience with other women, gain more awareness of myself, and take time to my emotional and physical growth (ID20)”) and listened to others ”). An additional important initial motivation to take part in the intervention was related to the need to stay in contact with people who lived a similar illness experience ”). Finally, having a previous positive experience in a group intervention was reported as motivation to participate in a new project. A woman indeed wrote: “I decided to participate in this intervention because I would like to experience positive relationships as I lived in other similar projects (ID12)”.Positive social relationships emerged as a relevant point of interest as one of the motives to participate, thanks to the possibility of perceiving group belongingness. More specifically, breast cancer survivors reported the need of sharing experience and emotions with others was one of the main reported outcomes. Breast cancer survivors evidenced a general satisfaction, referring to the possibility of discussing their illness with other cancer survivors to reflect on their experiences. This way, a woman wrote: “The discussion with other women in the group helped me to reflect deeply about myself and my body after illness. Moreover, I had the occasion to discuss the relationship with my partner and our intimacy issues (ID6)” and “I shared a lot of my emotions during this intervention and I understand that I can do it! I did not feel sick. Listening to others and sharing thoughts and emotions enriched my point of view. Feeling myself surrounded by new friendships was very useful and I will carry on forever with me. I was authentic and I was myself during this intervention; and now… I feel free”! (ID39)”. Additionally, another outcome is related to the relevance of the group to improve yourself and personal habits. “During this intervention, the main result is to be out of my daily routine in which I felt myself trapped. The discussion and comparison with women involved in this project lead me to change of habits, improving physical activities especially. My body will never come back like before; however, I can learn to feel myself good in my new body, without forgetting to take care of my inner world. I am the only one responsible for my well-being and body perception (ID9)”.In line with motivations, Interestingly, some breast cancer survivors also reported the need to be useful to improve scientific studies in the psycho-oncological field. In particular, some of them wrote the following sentences: “My goal is to give a little contribution to psycho-oncology research (ID21)”. This is associated with the interest in promoting helpful studies for future breast cancer survivors ” and “Trying to develop studies to help people who will be affected by breast cancer (ID16)”. Accordingly, breast cancer survivors also demonstrated their satisfaction of having supported scientific studies as an outcome ”.The present paper aimed to describe the main motives and perceived outcomes related to the participation in an integrated intervention to promote well-being in a group of breast cancer survivors. On a theoretical level, this contribution started from the close connection between individual motivation, self-monitoring, and self-management . SimilarAs presented in the result section, reaching satisfying physical and psychological well-being was the main focus of breast cancer survivors’ interest in participating in this project. Moreover, increasing social relationships  was particularly evident. Additionally, women reported the need of promoting strategies to cope with their previous illness experience on a third level. Finally, a few participants highlighted their interest in helping with scientific studies and research. Similar themes were identified as outcomes, but differences in terms of content and relevance between motives and outcomes were observed.Firstly, physical well-being is at the center of participants’ motives and outcomes. In particular, breast cancer survivors expressed their need to increase their ability to do physical movements as well as relaxation to promote well-being. This aspect is important, especially for breast cancer survivors who could not practice physical activity after their diagnosis . TherebySecondly, in our study, we found that psychological well-being is almost equally relevant. In particular, participants expressed their interest in improving self-acceptance and self-esteem, which are generally damaged by oncological experience ,40. InteCoping with the illness is the third theme of interest that emerged from participants’ motives and outcomes. In particular, breast cancer survivors highlighted the need to cope with the fear of recurrence and negative emotions . Current studies show that there is a relationship between fear of cancer recurrence, anxiety, and self-efficacy . SimilarMoreover, our findings also confirm the relevance of social relationships. Sharing experiences and emotions with people who have experienced a similar situation could be useful . This leFinally, the interest in supporting scientific research has been reported by some participants. Since there are strong emotional bonds among breast cancer survivors , our hypThe findings of this study add to our understanding of motives and outcomes related to participation in an integrated intervention for breast cancer survivors. The limitations of the present study are presented as follows. Firstly, due to the number of participants, it was not possible to analyze the differences in motives and outcomes between subgroups . Future studies need to cover this limitation in order to prevent possible sampling bias. Additionally, due to the data collection procedure, another limitation concerns the reports’ shortness. More detailed and longer narrations would have allowed for the adoption of alternative phenomenological approaches, such as interpretative phenomenological analysis . HoweverKnowing motives and outcomes related to integrated intervention for breast cancer survivors is relevant to implement useful and tailored programs. Broadening our understanding of the experiences of breast cancer survivors enables healthcare professionals to devise appropriate strategies to provide better care in order to improve their quality of life. In particular, a clear understanding of motives and outcomes would provide a wider range of resources and strategies for coping to tailor interventions. At the same time, professionals with different expertise could work together to tailor an efficacy program. Moreover, a holistic view of physical and psychological needs could be helpful to promote the overall quality of life. Breast cancer survivors should know that cancer does not always exclude the possibility of well-being. Therefore, interventions focused on survivors’ needs could change the way in which cancer survivors deal with their physical and emotional issues. Future research should indeed be designed by taking into account motives and desired outcomes.


Higher circulating prolactin has been associated with increased breast cancer risk. Prolactin binding to the prolactin receptor (PRLR) can activate the transcription factor STAT5, thus, we examined the association between plasma prolactin and breast cancer risk by tumor expression of PRLR, STAT5, and the upstream kinase JAK2.n = 168 cases, 765 controls) and postmenopausal women .Using data from 745 cases and 2454 matched controls in the Nurses’ Health Study, we conducted polytomous logistic regression to examine the association between prolactin (> 11 ng/mL vs. ≤ 11 ng/mL) measured within 10 years of diagnosis and breast cancer risk by PRLR , phosphorylated STAT5 , and phosphorylated JAK2  tumor expression. Analyses were conducted separately in premenopausal  and pSTAT5-C , but not tumors that were negative for these markers . This was stronger when tumors were positive for both pSTAT5-N and pSTAT5-C . No association was observed for PRLR or pJAK2 (positive or negative) and breast cancer risk among premenopausal women. Among postmenopausal women, plasma prolactin levels were positively associated with breast cancer risk irrespective of PRLR, pSTAT5, or pJAK2 expression .We did not observe clear differences in the association between plasma prolactin and breast cancer risk by tumor expression of PRLR or pJAK2, although associations for premenopausal women were observed for pSTAT5 positive tumors only. While additional studies are needed, this suggests that prolactin may act on human breast tumor development through alternative pathways.The online version contains supplementary material available at 10.1186/s13058-023-01618-3. Prolactin, a multifunctional pituitary hormone, is important in the proliferation and differentiation of normal mammary epithelium  and is nPreclinical models suggest that prolactin may impact breast tumorigenesis through the JAK-STAT pathway. Prolactin, when bound to the prolactin receptor (PRLR), activates JAK kinases via trans-phosphorylation leading to STAT5 tyrosine phosphorylation (pSTAT5) and localization to the nucleus , 10. pSTn = 18,743) provided a second blood sample [The NHS began in 1976 when 121,700 female registered nurses aged 30–55 years of age from 11 US states completed a baseline questionnaire. NHS participants are followed by biennially mailed questionnaires to collect information on multiple exposures, including putative breast cancer risk factors, and incident diseases, including breast cancer. In 1989–1990, 32,826 participants, ages 43–70, provided blood samples, and in 2000–2002 a subset of these women ages 53–80 n = ,743 provThe NHS study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. Participants provided implied consent by returning the self-administered mailed study questionnaires and blood sample; individuals in this study signed a medical record and tissue release form. This study was approved by the Mass General Brigham IRB (Protocol Number: 1999P010982).As described previously, prolactin was assayed by microparticle enzyme immunoassay in 12 batches at the Reproductive Endocrinology Unit Laboratory at the Massachusetts General Hospital using the AxSYM immunoassay system  or by Christopher Longcope, MD , using the IMx System  . The cor2. For covariates with repeated measures throughout follow-up, we used data from the questionnaire cycle closest in time prior to blood collection.Participants were queried on age at menarche and height on the 1976 baseline questionnaire. Current weight and personal history of benign breast disease (BBD) were assessed at baseline and on all biennial questionnaires. Hormone therapy (HT) use and fasting status at the time of blood collection were assessed using a questionnaire administered to participants at blood collection. Body mass index (BMI) was calculated as weight (kg)/height (m)Freshly cut sections of the TMA blocks were assayed by immunohistochemistry (IHC) at the laboratory of Charles Clevenger at Northwestern University for the following markers: (1) prolactin receptor (PRLR) , (2) tyrosine phosphorylated STAT5 (pSTAT5)  and (3) pJAK2 . Study pathologists , blinded to participant identity and characteristics, scored each TMA core, and determined tumor area morphologically as tumor cells are readily differentiated from cells in the tumor microenvironment, either based on visual differential or using the H&E stained slide. For PRLR and pSTAT5, cores were scored from 0 to 3  for cytoplasmic and nuclear staining intensity separately. A case was considered positive if one or more cores were scored 2 or 3, except for cytoplasmic pSTAT5, which was scored positive if any core was scored 1 or higher as few cores were scored 2 or 3. pJAK2 was scored from 0 to 2  in the cytoplasm. The intraclass correlations across cores ranged from 0.78 to 0.88. A case was considered positive for pJAK2 if any core was scored 1 or 2. For 3.0–12.6% of cases, depending on the stain, intensity was unable to be assessed  and are excluded from the analysis.P values were 2-sided and considered statistically significant if less than 0.05. All analyses were conducted using SAS, version 9.4 .Polytomous unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) as well as to test for heterogeneity by tumor expression (positive versus negative as described above). Based on our prior research [In total, 745 cases and 2454 controls were included Table ; 168 casAmong all premenopausal women, a nonsignificant positive association was observed comparing prolactin levels of > 11 ng/mL vs. ≤ 11 ng/mL. When we evaluated associations by tumor receptor status, we observed a significant increase in risk for prolactin levels > 11 vs. ≤ 11 ng/mL for pSTAT5 nuclear positive  but not pSTAT5 nuclear negative tumors . Similar results were observed for cytoplasmic pSTAT5  tumors  compared to the other three subtypes  had an increased risk of tumors with either nuclear or cytoplasmic pSTAT5 expression (or both), but not of tumors with limited pSTAT5 staining. Consistent with our prior work, which included the women in this study, the overall associations were stronger in postmenopausal compared to premenopausal women [Despite an overall lack of association between circulating prolactin levels and risk of breast cancer in premenopausal women , we obseConversely, among postmenopausal women, prolactin was similarly associated with breast cancer risk regardless of pSTAT5 expression or expression of PRLR or pJAK2, suggesting that this pathway may not be the sole or even primary mechanism of action in this population. One potential alternative mechanism may be through the JAK2-nuclear factor 1-C2 (NF1-C2) pathway –30. PRLRIn addition to STAT5, the other STAT family member activating commonly in breast cancer is STAT3 . There iThis study has several strengths, including a large sample size, pre-diagnosis measures of prolactin, and associated tumor tissue. Further, we were able to evaluate nuclear and cytoplasmic tumor cell compartments for staining of PRLR and pSTAT5 to evaluate differences by cellular location of marker expression. However, we were unable to evaluate pSTAT5a and pSTAT5b expression separately. Both are stimulated by prolactin and play a similar role in mammary gland development; however they differ in their downstream effects in the immune response, such that pSTAT5a is important for T-helper cells, and pSTAT5b plays a role in proliferation of natural killer cells , 37. TheOverall, we found that pSTAT5-positive breast cancer risk was increased for premenopausal women with high prolactin levels, suggesting that prolactin may be causally related to breast cancer risk in a subset of women through the traditional prolactin-related JAK-STAT pathway. However, no differences in risk were observed by expression of pSTAT5, PRLR, or pJAK2 in postmenopausal women, for whom prolactin was consistently positively associated with risk. This suggests that prolactin may be related to another factor that alters breast cancer risk or that it acts through other biologic pathways, such as NF1-C2, which have yet to be fully elucidated. Given the large variation in the hormonal milieu between premenopausal and postmenopausal women, better understanding of the differential actions of prolactin under low and high estrogen and progesterone conditions may provide further insight into these relationships.Additional file 1. Supplemental tables and figure.