We aimed to identify potential novel predictors for breast cancer among post-menopausal women, with pre-specified interest in the role of polygenic risk scores (PRS) for risk prediction. We utilised an analysis pipeline where machine learning was used for feature selection, prior to risk prediction by classical statistical models. An “extreme gradient boosting” (XGBoost) machine with Shapley feature-importance measures were used for feature selection among  Decades of efforts have established multiple predictors2 for the disease, including reproductive factors5, lifestyle7, and inherited genetic factors10. Despite the identification of multiple modifiable predictors, breast cancer remains a leading cause of death, with 685,000 deaths in 2020 worldwide. Pre- and post-menopausal breast cancers are usually regarded as etiologically different15.Breast cancer is the most common cancer among women, with 2.3 million women diagnosed with breast cancer in 202017. Previous ML studies have primarily focused on how ML approaches compare to conventional models for breast cancer risk prediction cancer22, but there are a lack of studies on utilising ML for predictor identification. The increasing availability of large and detailed cohorts, such as the UK Biobank (UKB), offer the opportunity to utilise hypothesis-free approaches for the identification of potentially novel predictors.Traditionally, predictor discovery for diseases such as breast cancer is hypothesis-driven. While it is reasonable to use classical statistical models (e.g. logistic regression) to assess these predictors, some novel predictors may be overlooked in the discovery stage in information-rich data prior to constructing a classical prediction model. Machine learning (ML) methods are able to handle both a large number of predictors and complex non-linear relationships, hence may provide assistance in the discovery of predictors23. For example, PRS added benefits in identifying populations that would most benefit from statin prescription26; PRS added accuracy to existing coronary artery disease risk predictors (e.g. Framingham risk score)27; and breast cancer PRS have been incorporated into existing risk prediction models such as the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA)28 and Tyrer-Cuzick model29.Recent years have witnessed the rapid development of polygenic risk scores (PRS) which aggregate the effect of a large number (e.g. hundreds or thousands) of genetic variants associated with a specific disease or trait, identified using genome-wide association (GWAS) studies. PRS have been proposed in a variety of clinical practice and research, including providing better future disease risk and identifying people at high risk for targeted treatment or screening strategies30, family history31, hormonal birth control use, menopausal status32, and use of corticosteroids33. However, the overall evidence is inconsistent.Interactions between PRS and phenotypic features (e.g. gene-environment interactions) have been suggested with breast cancer and its subtypes, including alcohol consumption, height, hormone therapyOur analysis pipeline consists of ML methods for feature selection, followed by classical Cox models for risk prediction. Our intention was neither to seek superiority among different approaches, nor to build competing prediction models for breast cancer. Our goal was to demonstrate that ML methods can be used for feature selection to complement classical statistical methods. Furthermore, we used SHapley Additive exPlanation (SHAP) feature dependence plots to explore potential interactions between PRS and phenotypic features. We also provided necessary statistical considerations before constructing classical Cox models to further investigate the potentially novel features selected by ML methods.34. The baseline data were collected in person via questionnaires, verbal interview with a trained nurse, physical examinations and biological samples. Follow-up information was obtained through linkage to electronic medical records of death and cancer registries and hospital inpatient records. The UKB study received ethical approval from the North West Multi-center Research Ethics Committee (REC reference: 11/NW/03820). All participants gave written informed consent before enrolment in the study, which was conducted in accordance with the principles of the Declaration of Helsinki.The UKB is a large-scale population-based prospective cohort with detailed phenotypic and genetic data from over half a million participants recruited between 2006 and 2010 across 22 assessment centres in England, Wales and Scotland15. We restricted our study population to a sub-cohort of UKB female participants who were post-menopausal with age 40–69 at baseline, met UKB internal genetic quality control (UKB field 22020), were of genetic White ancestry (UKB field 22006), and had no history of breast cancer, breast carcinoma in situ or mastectomy at baseline  and test (20%) sets (i.e. training-test split) for subsequent ML analyses.Prevalent breast cancer cases were identified using International Classification of Diseases codes (ICD-9: 174.X and ICD-10: C50.X) from the linked cancer registry data, with the date of breast cancer diagnosis preceding or on the date of baseline assessment.Incident cases were ascertained longitudinally using cancer registry data, supplemented by record-level hospital inpatient data due to the reporting delay in registries. The follow-up time for each participant was calculated as the number of years from the date of baseline assessment until the earliest of the following: breast cancer diagnosis, date of death from other causes, date of loss to follow-up, date of mastectomy or last date of medical record availability in UKB: 28th February 2021 in England, 28th February 2021 in Scotland, and 28th February 2018 in Wales.35, we included two breast cancer PRS as potential genetic features: PRS3139 and PRS120k36. Neither PRS used UKB data in its derivation stage, hence both are suitable for calculation within the UKB population without the concern of inflated effect estimates due to sample overlap. PRS313 consisted of 313  independent  variants, developed using the lassosum method37 from the same BCAC data.Due to the substantial discordance in individual-level risk categorisation between different PRS for the same disease38 and Supplementary Materials. We performed further variant Quality control (QC) checks across the whole cohort using a published pipeline39, excluding variants that were not available in UKB, variants poorly imputed in UKB .We used the imputed genetic data from UKB . Full details of genotyping, imputation, genetic array, and principal components (PCs) are described elsewhere40.We then performed sample QC, excluding participants who were related (third degree or higher), sex discordant, or identified as outliers for genotype missingness or heterozygosity , using sample QC data provided centrally by UKB (UKB field 22020) that retained a maximal set of unrelated individuals. Finally, we calculated the PRS as the weighted sum of effect allele dosages, and divided by the number of alleles using PLINK2In a phenome-wide scan of predictors for breast cancer, we considered 2,315 cross-sectional features from the UKB (Supplementary Table 2), reflecting socio-demographics, lifestyle, family history, early life and reproductive factors, blood and urine assays, physical measures, cognitive function, medication use and health conditions at baseline.41. For example, if participants self-reported taking “kliofem tablet” or “kliovance 1 mg/0.5 mg tablet”, they would be categorised into the “G03FA” ATC group (i.e. “contraceptive and hormone replacement therapy (HRT) related medication” group). Since our study population is post-menopausal women only, any G03FA medication is assumed to be HRT, and is referred to as such throughout this paper.We mapped the 6,745 unique self-reported medications (UKB field 20003) to 411 distinct codes at level 4 of the Anatomical Therapeutic Chemical (ATC) classification systemWe identified prevalent cancer diagnoses  from cancer registry data, and prevalent non-cancer diagnoses  from hospital inpatient data.We did not include administrative variables, inapplicable pilot fields, male-specific factors, family history of family members of adoptees, history of surgical operations , and fields collected exclusively during follow-up (e.g. imaging data).For biomarker assays, UKB has performed internal quality checks and excluded values where no data or an error was returned from the analyser, the values were outside the reportable range of the assay at the time of measurement or there was an aliquot problem. Full details of quality control were described in UKB Resource 5636. Among biomarkers, we excluded oestradiol and rheumatoid factor due to their high proportions of values below the lower reportable range.The main pre-processing we performed on training data prior to ML analysis was assigning the following three categories as missing: “Prefer not to answer”, “Do not know”, and empty entries. We then removed features with missing rate 16 for combining ML and statistical approaches  machine learning algorithmFeature selection is necessary prior to constructing an analytical model for risk prediction. This task can be undertaken in many ways . We used model-based feature selection (via a tree-based XGBoost machine) due to its ability to rank large number of features, prior to risk prediction by classical Cox models. XGBoost machine is also able to handle missing data, and reveal non-linear relationship between features and the outcome. The intention was to obtain the “best of both worlds”, by using non-linear methods to identify candidate associated inputs and then using conventional medical statistical models for maximum interpretability of the results.Tree-based XGBoost is an ensemble learning method in which decision trees are built in a sequential manner. After initialising the model prediction by minimising a regularised loss function, the algorithm builds each tree by minimising the residuals from prior ones. The final model prediction is the weighted sum of the predictions from these sequential trees. It is capable of revealing non-linear relationships among correlated features from large datasets in a memory-efficient manner.Our outcome (i.e. response variable) was the time-to-event outcome of incident breast cancer which consists of a binary indicator (present/absent) and the follow-up time. The XGBoost model was trained with negative partial log-likelihood of Cox proportional hazard model (i.e. “cox-loss”) as the loss function. Missing data was regarded as containing information . During the training of the model, samples with missing values were assigned a default direction in each branch to either the left or the right child node, based on the gain.For hyper-parameter tuning, we performed grid search (Supplementary Table 3) with five-fold cross validation (CV) on training data using cox-loss as the evaluation metric. The optimal set of hyper-parameters for XGBoost were found to be: maximum depth 43, permutation based feature importance44, and SHAP values [arXiv:1705.07874]. Existing literature16 [arXiv:1802.03888] suggests that SHAP values are the most consistent and stable among the above methods. These properties are vital aspects of feature selection as they provide assurance that the selected features are robust to the perturbation of input data45. SHAP values also have the advantages of faster computation and better visualisation compared to permutation-based methods . Despite those advantages, SHAP values have its limitations  and could potentially generate misleading interpretations, which could hide biases47.A variety of measures exist for obtaining features of high importance, such as the XGBoost built-in methodsma) across all samples to summarise the global attribution of this feature. In addition to the global SHAP values shown in a summary bar plot  and permutation-based methods for comparison. The SHAP value of each feature was first computed using one sample at a time to reflect the local effect on the sample, and then aggregated by taking the mean of absolute SHAP values . In contrast, the XGBoost machine had assigned a missing category to missing data, effectively assuming MNAR.The missingness within each feature was carefully assessed at this stage, as the number of features had now been sufficiently reduced (e.g. from over 1k to under 30) to permit such close inspection. For example, the variables “Age at first birth” and “Number of live births” needed to be considered together to ensure the imputed data were reasonable for women who have not had children. We performed multiple imputation using the We note that the above statistical procedure is essential preparation before constructing classical statistical models and must not be overlooked. The analytical power of their elegant equations comes from careful attention to model specifications and thorough examination of the underlying assumptions.Following the necessary preparation above, we constructed a Cox proportional hazard model (i.e. the augmented Cox model) using the training data to assess the associations between novel features and incident breast cancer, adjusting for established risk factors. Since PRS were present in the model, we also adjusted for genetic array (UKB field 22000) and the first 10 genetic PCs (UKB field 22009) to account for the underlying population structure.50 using the training and test data, respectively. Both the augmented and baseline Cox models were pre-specified in our statistical analysis plan.To determine whether the novel features identified by ML improve model performance, we built a separate Cox model using the training data with only the two PRS and known risk factors (i.e. the baseline Cox model). We computed Brier score at 10-year for assessing overall model performance and Harrell’s C-index for assessing risk discrimination. We used the two sets of baseline hazard at 10-year and model coefficients  yielded from these two Cox models to compute the 10-year risks and prognostic index ; values less than 10 were considered acceptable. Statistical tests were two-tailed, and performed using a 5% significance level.51 .As sensitivity analyses, we built additional Cox models using the training data to investigate the potential “PRS XGBoost version 1.5.0 and SHAP version 0.40.0 were implemented in Python version 3.8.8 and Cox model analyses were conducted using R version 4.0.2.Baseline characteristics of the study population are presented in Table Figure ma) are shown in Fig. ma in Additional Supplementary Table).The top 20 features ranked by the highest mean absolute SHAP values ) appeared in the top 20 features. The four risk factors not present/ranked in the top 20 are use of HRT  Blood count (UKB Category 100081) (red blood cell count)Blood biochemistry (UKB Category 17518) Urine assays (UKB Category 100083) Among the top 20 ranked features, our XGBoost machine discovered novel predictors of breast cancer from the following categories:The anthropometric measure, basal metabolic rate , showed expected correlation with BMI  that had higher values (i.e. red points) with negative SHAP values (i.e. decrease in relative hazard). This suggests potential protective effect of these features on developing breast cancer .The symmetry in the range of the positive and negative SHAP values in the SHAP beeswarm plot suggests a linear relationship between the feature and the outcome. If the range of the positive SHAP values are longer than that of the negative SHAP values (or vice versa), this may suggest a non-linear relationship.For example, the SHAP values of alkaline phosphatase have similar lengths in the ranges of the positive and negative SHAP values, indicating a linear relationship with the outcome. We observed similar patterns for the two PRS. In contrast, age at recruitment occupies a longer range in the negative SHAP values than the positive, suggesting that younger age may have had stronger impact on breast cancer than older age (i.e. non-linear effect). To further investigate the non-linear effect of age, we performed post-hoc analyses by fitting two Cox models: (1) an unadjusted Cox model that included age only; and (2) a Cox model that further added a spline term of age with 3 inner knots at 25th, 50th, and 75th quantiles. We then performed likelihood-ratio test on the spline terms . Besides the slight change in the feature importance ranking, monocytes count and whole body fat mass were ranked on the borderline by cox-loss at 21 and 30, respectively. In comparison, they were ranked at 17 and 18 by log-loss.Our final augmented multivariable Cox model consists of the 14 established risk factors available in the UKB (including the two PRS), the 10 potentially novel features identified by SHAP value rankings, genetic array, and the first 10 genetic PCs. Among these 10 novel features, the following five had a statistically significant association with breast cancer in post-menopausal women: basal metabolic rate, red blood cell count, plasma urea, plasma phosphate, and creatinine in urine. Among these five features, blood biochemistry features (i.e. plasma urea and plasma phosphate) were inversely associated with risk of developing breast cancer, whereas other novel features  were positively associated. The direction of effect was consistent with the trend observed in SHAP beeswarm plot.The remaining five novel features that did not reach the 5% significance level are: sodium in urine, aspartate aminotransferase, gamma glutamyltransferase, alkaline phosphatase, and C-reactive protein.Figure The Brier scores of baseline and augmented Cox models using the training data were both 0.032, and those using the test data were both 0.031. In the training data, Harrell’s C-index increased from 0.667 (the baseline Cox model) to 0.673 (the augmented Cox model) when adding the additional novel features selected by ML. In the test data, the corresponding Harrell’s C-index increased from 0.664 to 0.665, computed using the model coefficients obtained from the training data.As mentioned in Sect. 3.6, the Harrell’s C-index of the XGBoost model was 0.717 from the training data (i.e. 0.044 increase from the augmented Cox model) and 0.667 (i.e. 0.002 increase from the augmented Cox model) from the test data. This increase in C-index could be due to the higher complexity of XGBoost (e.g. incorporates non-linearity) compared to classical Cox models. To interrogate the possibility of reverse causation, we conducted another sensitivity analysis to exclude the first two years of follow-up for blood and urine biomarkers , as an extra sensitivity analysis. We observed attenuations in associations for novel features, but the overall direction of results remained consistent with the main analyses. This could be due to both reduced sample size and possibility of chance findings.The aim of this study was to demonstrate the utility of combining machine learning with traditional statistical approaches in the domain of cancer epidemiology. We identified several known risk factors  and five potentially novel predictors . These latter novel features remained robustly associated with incident post-menopausal breast cancer after incorporating known risk factors in classical Cox models. Further research is necessary to confirm whether these novel predictors are potential modifiable risk factors for breast cancer, particularly the biological mechanisms of these associations. We intended to identify novel predictors for further exploration, rather than commenting on their potential causal nature.52. The unexpected observation was that the XGBoost model selected a more detailed body composition measures, basal metabolic rate instead of BMI, indicating that more precise body composition measures could provide important information above and beyond BMI for predicting breast cancer. Previous studies55 have investigated anthropometric factors beyond BMI  and found positive associations with post-menopausal breast cancer. However more detailed anthropometric factors are worthy of further investigation. Our findings showed that basal metabolic rate is a significant predictor for breast cancer, contradicting previous studies that found no such association57. Our positive finding could be due to the statistical power conferred by the large sample size of UKB.Here we delineate the existing literature on the novel factors. Body composition measures were expected to be associated with breast cancer as obesity is a well-known risk factor for post-menopausal breast cancer58, but our study suggests it may be associated with breast cancer. Aspartate aminotransferase and alkaline phosphatase are both blood biomarkers related to liver function and were not previously associated with non-metastatic breast cancer59. In contrast, gamma glutamyltransferase was observed to have positive association with subtypes of breast cancer in the Apolipoprotein Mortality Risk (AMORIS) cohort60, and C-reactive protein, a marker of systemic inflammation, was reported to be associated with breast cancer via meta-analysis62. To our knowledge, no previous studies have reported the association of plasma phosphate, sodium in urine or creatinine in urine with breast cancer.The SHAP feature importance ranking supported the associations of novel biomarkers with post-menopausal breast cancer, but little literature exists on this topic. Plasma urea, a blood biomarker related to kidney function, was reported to have null causal relationship with breast cancerOur findings of novel predictors should be treated with caution, and further examined in independent datasets. These novel features could be surrogates for other processes that are not modelled in our analyses. The role of ML in this study is model-based feature selection that ranks the approximately 1.7k input features. The classical Cox model was our pre-specified model for risk prediction and statistical inference. Although we have pre-specified our analytical models, there is still the possibility of chance findings, particularly when the same training data were used in both the feature selection (i.e. the ML model) and the risk prediction . We did not observe evidence of reverse causation, but such absence of evidence should not be regarded as evidence of absence.13, hence were regarded as missing values and could not be included in the analysis.Four well-established risk factors are unavailable  or unusable (plasma oestradiol) in the UKB. Plasma oestradiol was measured in UKB, but the measured concentrations of nearly all post-menopausal women were below the reportable rangeWe identified 18 established risk factors for post-menopausal breast cancer from the literature, 14 of which are available in UKB. Out of these 14 risk factors, 4 were not ranked in the top 20 by the SHAP values: BMI was ranked at 79, probably because its related factor  were already ranked among the top 20; HRT use was on the borderline of inclusion in top 20 (ranked 26), whereas age at menarche and parity were ranked further behind (ranked 74 and 72 respectively).63, while others advocated a cut-off value of 0.05 for the SHAP feature importance measure16. Different approaches, such as Boruta64, exist, but all contain an element of randomness and there is no gold standard threshold. We pre-specified the top 20 features due to the practical need for keeping the number of features in a manageable range, in the absence of established criteria on this empirical choice. Apart from the SHAP values, importance can be similarly quantified in terms of gain in prediction power or even beyond the context of the model itself (e.g. cost-effectiveness of the model).This highlights the need for establishing a criterion to decide which features should be regarded as important. Some suggested considering 3% of total number of features as importantWe had an a priori interest in PRS, and intended to explore the relationship between the two PRS for breast cancer based on the existing understanding of their correlation. We discovered that both PRS were ranked as the strongest risk factors by the agnostic ML methods, which is surprising given that both PRS were developed using largely the same GWAS data for the same disease. We then conducted in-depth Cox regression simultaneously fitting both PRS as predictors, and concluded that both PRS are significant predictors for post-menopausal breast cancer. This raises the general question of whether multiple PRS should be used to improve risk prediction obtained from a single PRS.SHAP dependence plots are useful for (i) visualising non-linear relationships between features and outcome, and (ii) revealing potential pairwise interactions between features. However, in this study, we noticed unexpected patterns arising from these plots, indicating that careful investigations are required before drawing firm conclusions.ML methods are well suited for model-based large-scale (e.g. thousands) feature selection, and our SHAP values utilise the impact in model predictions with and without a particular feature to aid interpretability. In contrast, classical (e.g. backwards) feature selection methods are more suitable for selecting from a small number (e.g. 10–20) of features. Multivariable Cox models are beneficial in the subsequent risk prediction step, because (i) they are able to quantify the strength of association of each feature with the outcome via hazard ratio with 95% confidence interval; (ii) model coefficients (i.e. betas) and confidence intervals are typically used to infer feature importance in classical statistical models, providing a straightforward interpretation of the model output; and (iii) different choices of loss-function or feature ranking method in ML may result in different features being selected. It is therefore desirable to further examine the selected features using classical models for risk prediction.When the search scope includes highly correlated features, one needs to carefully choose ML (e.g. tree-based) models that are capable of handling correlations, and then perform correlation and collinearity checks before including the selected features in classical statistical models. To reduce collinearity, we eliminate one feature among the highly correlated pairs, followed by checking the VIF. Other approaches for handling collinearity include Ridge and Lasso regression. It is possible that the eliminated feature could be a causal factor behind the retained feature. Furthermore, the focus of this paper is association (not causation), and therefore factors appearing to be strongly associated with the outcome do not necessarily imply causal relationships.We did not expect the results obtained from a ML method to be fully compatible with those from a classical statistical model, because each approach has its own strengths and limitations. Instead, we aimed to complement Cox models with the insight from the XGBoost machine in this study. There are several possible explanations on the observed differences between the ML method and classical Cox models in this study.First, our XGBoost machine characteristically makes binary splits of the input features among thousands of trees assuming non-linear relationship among features, whereas our Cox models are essentially linear. Although it is possible to incorporate non-linearity in Cox models , it would result in an overly complex model for interpretation. It is neither necessary nor appropriate to anticipate full agreement between ML and classical statistical models. Second, the criteria for inferring feature importance are different between ML and classical models, as described above.Finally, we emphasise that while agreement between ML and classical models raises the confidence of discovery, differences do not necessarily imply superiority of either approach or compel us to choose one over the other. The differences could serve as a signal for further investigations where critical thinking should be exercised.66 has compared various imputation strategies  and suggested utilising the method that yields the best prediction accuracy depending on the dataset. However, the potential biases arising from missing data must not be overlooked, particularly in the context of statistical modelling.It is worth highlighting the challenges we encountered when implementing our analysis pipeline, and providing potential solutions here. The main challenge is how to handle missing data when combining ML and classical statistics for interpretation. Existing literature67. However, it is not feasible to train a ML model on each imputed dataset due to the extensive computation time this would require. One alternative is to perform a single imputation, but this was not compatible with our complex variables where some are missing at random and some are not. For example, “Age at first birth” is not missing at random and if one blindly performs a single imputation, the imputed data would not make sense for women who have not had children. Although manual inspections can be performed on small datasets, it is practically impossible to carefully assess the missing mechanisms of the high , fits the model using each dataset, and then pools model estimates using Rubin’s ruleOur solution is as follows: For the ML analysis, we treated missing data as a separate category and used the default setting for XGBoost . Once we had reduced the number of features to a workable size  and detailed family history data were either unavailable or unusable in UKB, hence could not be investigated in our study. Cohort-wide exposure data was captured at a single time point (date of enrolment), therefore we were unable to account for within-person variability for the input features. This means that our analysis pipeline was not suitable to incorporate longitudinal data of input features. We did not investigate subtypes (Estrogen-receptor [ER]-positive or negative) of breast cancer, due to incomplete data on tumour type in UKB. We did not incorporate exome data that are necessary for identifying BRCA1/BRCA2 carriers, or other high penetrance variants. Our study population consists only of genetically White individuals, and therefore should be not generalised to other ethnicities without further research. Moreover, our findings were solely based on UKB. Such recruitment-based biobanks are subject to potential biases, including “healthy volunteer” selection biasIn conclusion, using ML for feature selection prior to risk prediction by Cox models, we identified five statistically significant novel associations with post-menopausal breast cancer for blood counts, blood biochemistry and urine biomarkers. The discrimination performance was maintained when adding these five novel features to the baseline Cox model that only contains two PRS and established risk factors. Adding such factors to a model does not substantively increase risk prediction, but, when included, appears to be relied upon when making subsequent risk predictions . We discovered that both of our pre-specified PRS were ranked as the most important features by SHAP value, and can be simultaneously included in our final Cox model. Our findings support further investigation on using more precise anthropometry measures for improved breast cancer prediction. The important next step would be to externally validate our findings, which is necessary before any subsequent implementation in clinical practice. Specific directions for future research include the utility of multiple PRS for better risk prediction, and validating the association between blood/urine biomarkers and risk of breast cancer using data external to UKB.Supplementary Information 1.Supplementary Information 2.


For secondary outcomes at 6 months, women using SCOPE significantly reduced BMI  and increased breast cancer-related knowledge  and attitude . No significant findings were found regarding diet self-efficacy, physical self-efficacy, or breast cancer screening barriers. The results suggest the intervention has great potential to promote the health and wellness of women.Breast cancer prevalence has increased globally, with 12.2% of breast cancer cases identified in China. Obesity and unhealthy lifestyles are major risk factors for breast cancer. We conducted a randomized control trial to assess the feasibility and evaluate the preliminary effect of the Smartphone-Based Cancer and Obesity Prevention Education (SCOPE) program among adult biological women with a waist circumference greater than 80 cm. The SCOPE program includes tailored and culturally appropriate educational information for obesity and breast cancer prevention delivered by the research team via WeChat. The control group received non-tailored general health information via WeChat. A total of 102 women  participated, and 87 (85%) completed 6-month follow-up assessments. For the primary study outcome at 6 months, women using SCOPE significantly reduced waist circumference (Cohen’s  Cancer is China’s leading cause of death (about 7500 deaths per day). Cancer not only adversely affects the health of individuals but also imposes an economic burden on families and healthcare systems . The leaIn China, smartphone ownership is about 71%; thus, a smartphone-based intervention could offer an effective and feasible tool to reduce the risk of obesity and breast cancer . SmartphThus, we piloted a smartphone-based intervention designed to meet the needs and lifestyles of Chinese mothers using the popular communication platform WeChat. This study aimed to assess the feasibility and estimate the preliminary efficacy of a Smartphone-Based Cancer and Obesity Prevention Education Program for Chinese Women (SCOPE) using a randomized control trial (RCT) design. To prevent the risk for breast cancer, this study focused on obesity as the primary outcome and healthy lifestyle and knowledge and attitude regarding breast cancer prevention as secondary outcomes. Two major aims of this study include:Aim 1: To assess the feasibility of SCOPE, a tailored smartphone-based lifestyle intervention.Aim 2: To estimate the preliminary efficacy of SCOPE intervention on the primary study outcome (waist circumference) and secondary outcomes  in the SCOPE intervention compared to the control groups at 3 months and 6 months.This pilot study used a randomized control trial design. The social cognitive theory (SCT) ,23 and sWomen who met the inclusion criteria from two community health centers in Changsha, Hunan Province, China, were invited to this study and randomized to either the SCOPE or control programs. Inclusion criteria included (1) being biologically female, (2) being at least 18 years old, (3) having a waist circumference greater than 80 cm, (4) owning a smartphone, (5) being able to read Chinese and speak Mandarin, (6) being premenopausal, and (7) having a child between the ages of 1 and 17 years old. Exclusion criteria included (1) being pregnant, (2) giving birth less than 12 months before the enrollment date, or (3) having an acute or life-threatening disease .Trained research assistants worked with the study sites to recruit eligible women. An invitation letter that explains the study’s purpose and WeChat contact information was sent to all women with dependent children. They were asked to message the study’s WeChat within two weeks of receiving the invitation to indicate their interest in participating. Those who were interested were invited to an in-person screening session during health fairs hosted at the recruitment sites. Several in-person screening sessions were offered at each location. During these sessions, eligibility verification, informed consent, administration of a baseline survey, and measurements were obtained. The committee on human research at the University of California San Francisco and Central South University approved the study (IRB 18-27025).The SCOPE intervention consisted of three components: (1) the use of the Fitbit activity tracker, (2) 12-weekly education modules delivered via WeChat, and (3) bi-weekly tailored messages. The 12-week active intervention included the Fitbit tracker and 12 weekly, culturally appropriate, evidence-based educational modules delivered via WeChat, a popular communication app in China. After the 12 weekly modules, the maintenance phase included six bi-weekly tailored tips and messages based on Fitbit data and personal goals sent via WeChat. The control group received general health information without tailoring via WeChat at the same frequency as the SCOPE program. Data on outcomes were collected at baseline, 3 months, and 6 months.TM tracking device to wear daily. Each participant received in-person, one-on-one training from the research assistants on accessing the app and their tracking data. Fitbit Alta HRTM built by Fitbit ), is a sensitive, 3-D motion sensor that tracks an individual’s activities, sleep, and food intake. The website and apps are currently available in Chinese in China. The Fitbit Alta HRTM has mobile apps (Android and iPhone) that enable real-time automatic wireless syncing to smartphones (via data transfer). Both the Fitbit app and the online program allow tracking of physical activity, sedentary activity, and dietary intake and the ability to set individualized goals, monitor progress toward those goals, and interface with interactive apps related to promoting an active lifestyle, balanced diet, and healthy weight. In the study, participants were asked to wear the Fitbit Alta HRTM daily for at least 10 h while awake and throughout the night while asleep. Those in the intervention group were taught to use the Fitbit and WeChat apps to set up fruit/vegetable intake goals and daily steps. If they did not use the device and app for more than 1 week, a WeChat reminder message was sent to them. Participants in the SCOPE intervention group were asked to share their data with the research team to develop messages tailored to individuals’ behavior patterns.Fitbit tracker. All participants received a Fitbit Alta HRhttps://www.chinacdc.cn/en/ (accessed on 1 July 2019)), the National Health Commission of the People’s Republic of China (http://en.nhc.gov.cn/2019-07/17/c_75509.html (accessed on 1 July 2019)), and Beijing Institute for Cancer Research [Twelve modules. Modules were sent via WeChat. Participants received 12 weekly, culturally appropriate, evidence-based educational modules along with tailored tips and messages via WeChat. Each module includes an educational session/information that lasts less than 20 min. The educational modules include (1) Assess Your Risk, (2) Prevention Through Cancer Screening and Vaccination, (3) Eat Well to Reduce Cancer Risk for You and Your Family, (4) Smart Shopping and Cooking, (5) Abs Exercise, (6) Active Lifestyle with Your Child to Boost Your Energy, (7) Fitness for You and Your Child, (8) Healthy Lifestyle for Healthy You, (9) Happy Motherhood and Stress Management, (10) Effective Coping and Getting Support, (11) Stay Motivated to Prevent Cancer and Obesity, and (12) Healthy Weight and Flat Abs for Long-Term Health. The contents of the modules were based on relevant evidence from the literature as well as professional organizations in China, such as the Chinese Center for Disease Controls  on general health topics important to women in China. The topics included anxiety, depression, sexually transmitted infections, intimate partner violence, HIV, unintended pregnancy, hepatitis B, and general cancer prevention. Participants also received six biweekly, non-tailored text messages on general health topics. The 12 modules are different from the modules in the intervention group. These modules in the control group include general and non-tailored information for general health topics. The weekly educational modules sent to the control group are the same duration as for the intervention group to avoid attention bias.The control group received a Fitbit Alta HRData were collected at baseline, 3 months, and 6 months. The research assistants and a registered nurse conducted all measurements. The trained research assistant and registered nurse measured WC, weight, and height. Study participants completed all questionnaires in a separate room where a research assistant could answer questions. All measurements and survey items have been used in China and demonstrated adequate reliability and validity.Feasibility. The number of eligible participants, percentage of completed assessments, and module access were assessed.Usability and acceptance. The frequency of accessing the 12 modules and satisfaction of reviewing the modules were collected to measure the usability and acceptance of these modules.Demographic survey. The socio-demographic questionnaire assesses participants’ age, education level, parents’ occupation, residence, and household income.Waist circumference (WC). This study followed the waist circumference measurement protocol set by the National Institute of Health. Waist circumference was measured midway between the lowest rib and the superior border of the iliac crest. The circumferences were given as the mean of the 2 measurements to the nearest 0.1 cm. Central obesity is a waist circumference ≥ 80.0 cm for Chinese women ,31,32. T2). Research assistants measured height and weight using instruments calibrated before each measurement. The instruments have an accuracy of 0.1 cm in height and 0.1 kg in weight. This study used the criteria for overweight and obesity in China criteria: underweight as BMI < 18.5 kg/m2, normal weight as 18.5 kg/m2 ≤ BMI < 24.0 kg/m2, overweight as 24.0 kg/m2 ≤ BMI < 28.0 kg/m2, and obese as BMI ≥ 28.0 kg/m2 [Body mass index BMI. BMI is determined by measuring weight and height  and regular physical activity  were used in this study. These two items were from the Canadian Diabetes Risk Questionnaire for the Chinese population survey used in China (CHINARISK). Adequate reliability (test-retested reliability = 0.98) and validity (sensitivity = 0.73 and specificity = 0.63) have been reported .Physical activity/average daily steps. The average daily steps on the week we collected data were used. Previous studies indicated that the Fitbit step count outputs are stable at different speeds and substantial with observer counts (0.97–1.0) . The relSelf-efficacy for diet and physical activity. The 8-item survey was used in the study. Participants were asked about their confidence level in engaging in activities related to a healthy diet and regular physical activities. Scoring was based on five points (0- not confident to 5- very confident), and a higher score indicates a higher level of self-efficacy. Reliabilities have been documented in the exercise domain  and the diet domain  .Social support for diet and physical activity. The social support scale consists of six items measuring social support for diet and physical activity from family members and friends. The survey includes three items to evaluate the support of diet and exercise from family members and three from friends. The diet social support scale is based on a 4-point scale: 0 (never), 1 (sometimes), 2 (frequently), or 3 , while the physical activity social support scale is rated on a 5-point scale: 0 (never) to 4 . Higher scores indicate more social support for the participant. The survey has adequate reliability, with Cronbach’s alpha ranging from 0.80 to 0.87 for the diet domain and 0.84 to 0.91 for the physical activity domain .Perceived stress scale (PSS). This 14-items survey measures the degree of perceived stress the mothers perceived in the past month. The survey asked participants to respond to 14 questions on how often they have felt  over the past month. Participants report the frequency of these questions on a five-point Likert scale (0  =  never to 4  =  very often), with the higher the score reflecting the higher level of the perceived stress. This survey has been used in China and has good psychometric properties in the Chinese population .Breast cancer risk knowledge, attitude, and barriers questionnaire (CBCSBQ). A 12-item self-report questionnaire measures knowledge (4 items), attitude (4 items), and barriers to mammographic screening (4 items) ,42. Partd, which has been described as a small effect for 0.2–0.49, a medium effect for 0.5–0.79, and a large effect for ≥0.8 [Because this is a pilot study aimed at testing the feasibility, acceptance, and preliminary efficacy, a power analysis was not conducted. Since this pilot RCT has a limited sample size, analyses were intended to be exploratory, and we set α = 0.05 (two-sided). Descriptive statistics  were reported for quantitative demographic and outcome variables. Frequencies and percentages were reported for categorical variables. T, chi-square, or analysis of variance (ANOVA) tests were used to compare differences between the intervention and control group at baseline. We used mixed effects models to estimate the effect of intervention vs. control from before/after treatment . Because the sample size was small, we used the nonparametric bootstrap with 5000 draws to obtain bias-corrected confidence intervals and evaluated significance ,44,45. Afor ≥0.8 . Data anp > 0.05) in the demographic data and outcome variables at the baseline between the SCOPE and control groups  participated in this study see . The meal groups .d = 0.08, p = 0.008) and BMI , and improved daily steps (d = 0.63 (a medium effect), p = 0.012), positive breast cancer attitude (d = 0.81 (large), p < 0.001), and breast cancer knowledge , p = 0.03; Results from the mixed-model analysis found that at the 3-month follow-up, compared to baseline, participants in the SCOPE program compared to control significantly reduced WC , p < 0.001) and BMI , and increased breast cancer knowledge , p = 0.001) and attitude (d = 1.39 (large), p < 0.01). WC, BMI, and BC attitudes and knowledge were estimated slightly stronger at 6 months than at 3 months, but the step count was slightly less .At 6 months, compared to baseline, women in the SCOPE group compared to control significantly reduced WC  indicated they learned a great deal or a lot from these modules.As breast cancer is a significant health issue, and obesity increases the risk for breast cancer, especially for women with children, we developed a smartphone-based intervention using a popular communication app tailored to Chinese mothers. Our intervention aimed at reducing obesity, increasing knowledge and attitude, and reducing barriers to breast cancer. In our pilot study, less than 50% of participants who have abdominal obesity reported eating five servings of fruit and vegetables and exercising daily at baseline. We observed a significant reduction in WC and BMI in the intervention group compared to the control group and small to large effect sizes of the intervention on improving PA steps, breast cancer knowledge, and breast cancer attitude at three months follow-up. Smaller effects on behavior and psychosocial outcomes were found at the six-month follow-up. Our pilot study revealed good feasibility and high satisfaction in accessing the modules.p = 0.048) [The lower level of engagement in healthy eating and active lifestyle found in women with abdominal obesity in China is problematic and even worse during the COVID pandemic. The results of our study are consistent with other studies. Previous work examining the effects of the COVID pandemic in China found that more than half of people gained weight, with 16.3% of women gaining more than 2.5 kg . They al= 0.048) . Another= 0.048) . Women wThe results of our study revealed great potential in reducing obesity and the risk of breast cancer. Although studies focusing on Chinese women are limited, current evidence demonstrates that premenopausal Asian women with abdominal obesity have a higher risk of breast cancer compared to non-Hispanic white women ,54. The Our study also revealed small to large effects on the behaviors and psychosocial factors, such as increasing fruit/vegetable intake, PA steps, and social support regarding diet and PA. As studies have supported the impact of a poorer health lifestyle, including a sedentary lifestyle and inadequate fruit and vegetable intake, on an increased risk for abdominal obesity, it is important to build a healthy lifestyle and a supportive environment for maintaining a healthy lifestyle to ensure the sustainability of health behavior changes ,62. AlthIn addition, this intervention also improved women’s knowledge and attitudes regarding breast cancer. Our study is consistent with other studies suggesting that adequate educational programs can increase women’s knowledge and attitudes about breast cancer ,66,67. SOur pilot study revealed good feasibility and high satisfaction in accessing the modules. Traditional in-person weight management and breast cancer prevention programs are time-consuming and costly, especially during a pandemic. The promising effect of our intervention can contribute to the use of communication apps and theoretical-based intervention. Our intervention integrates the SCT and SDH, the use of everyday technology (WeChat), and an adaptation of an evidence-based program tailored to the behavior patterns, preferences, and cultures of women in China. WeChat is a popular multifunctional social media mobile app, and connectivity is widely available without restrictions . Using WThere are several limitations in this pilot study. The study’s results need to be interpreted with caution. Because this pilot study was conducted with a small sample size and a homogeneous population, we have limited power to detect small differences. The results may also only be generalized to similar populations. Moreover, this study did not include other potential risk factors such as alcohol drinking and smoking. Other limitations included convenience sampling, self-reported measures, and only 6 months of follow-up. Future studies would also need to investigate whether specific subgroups of women can benefit more from this program and include other potential risk factors  in the study.This pilot study demonstrates a promising way to prevent obesity and breast cancer. The SCOPE intervention delivered via a communication app is feasible and may positively reduce women’s risk for obesity and improve breast cancer knowledge and attitude. This intervention has great potential to promote the health and wellness of women and the community.


The study aimed to develop and validate a prediction model that can be used to classify women for tailored breast cancer screening based on their individual risk. The model included data on age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease for 57,411 women screened in BreastScreen Norway 2007–2019. The 4-year predicted risk of breast cancer ranged between 0.2% and 7.3%, with 95% of the population having a risk of 0.6–2.3%. The differences in the predicted risk favor personalized screening for breast cancer.Background: We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. Method: This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). Results: The 4-year predicted risk of breast cancer ranged from 0.22–7.33%, while 95% of the population had a risk of 0.55–2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09–1.11) and the AUC was 62.6% (95% CI: 60.5–65.0%). Conclusions: This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50–69 years. In recent decades, the risks of mammographic screening, such as false positive screening results and overdiagnosis, have been debated ,2,3,4. CSeveral risk prediction models ,11,12 haTo meet this need, we aimed to develop and validate a breast cancer risk prediction model targeting women participating in population-based screening, using data from BreastScreen Norway.This study has a legal basis in accordance with Articles 6  (e) and BreastScreen Norway started in 1996 and is part of the Norwegian public health care service . The pron = 16,300) [We conducted a retrospective cohort study including information about all women screened at four breast centers in BreastScreen Norway, at least once between 1 January 2007, and 31 December 2019. The women were followed for interval cancer two years after her last screening examination, until 1 December 2021. Data from four centers  were chosen as these centers had collected automated mammographic density within the study period. Women included in the study were free from breast cancer before invitation, had at least one automated measurement of mammographic density, and completed the self-reported health indicator questionnaire used in the study . Women p 16,300) . FurtherData from BreastScreen Norway was extracted from the Cancer Registry of Norway which administers the program. Questionnaire data were collected as a part of BreastScreen Norway and are described in detail elsewhere .The self-reported questionnaire provided information on known risk factors for breast cancer , includiMammographic density was recorded using automated software  ,40. EachAge, BMI, age at menarche, mammographic density, first and second-degree family history of breast cancer, benign breast disease, exercise, physical activity, alcohol consumption, smoking, pregnancy, and use of HT, oral contraception, and hormone spiral were tested by backward selection for inclusion in the model. By using backward selection, we ensured including the minimal reasonable number of risk factors that maximized model performance. We tested interactions between the risk factors included in the model. Because none of the interaction terms were significant, they were not included in the model. The proportional hazards assumption was assessed by testing the independence between Schoenfeld residuals and time ,43.2), age at menarche  and other qualitative variables  in order to present the estimates in a more common way for studies on breast cancer risk factors [The means with standard deviations (SD) for quantitative variables  and numbers with percentages for qualitative variables  were compared for women with and without breast cancer. We used partly conditional Cox proportional hazards regression to estimate the adjusted hazard ratios (aHR) of breast cancer incidence for the aforementioned risk factors ,44. The  factors ,12,34,45Following the method described by Zheng and Heagerty , we predTo present the effect of the study variables in the risk estimation, we plotted the difference in the 4-year predicted risk estimated by the model with and without each variable for each woman in the population.p-values <0.05 were considered statistically significant. All analyses were performed using the statistical software R version 4.1.2 .Statistical tests were two-sided and all The study cohort included 73,945 women screened during the study period. After exclusions, the study cohort comprised 57,411 women who underwent 182,812 examinations during the study period. A total of 1230 breast cancers were diagnosed . The meap = 0.037) (p = 0.154). Women diagnosed with breast cancer had a higher proportion of mammographically dense breasts at baseline, a higher proportion of second- and first-degree family history of breast cancer and benign breast disease, higher alcohol consumption, and a higher proportion of HT use compared with women without breast cancer. We found a higher proportion of women reporting high levels of exercise and the number of pregnancies among those without a breast cancer diagnosis.Mean age at menarche was 13.16 years for women with breast cancer and 13.24 years for women without breast cancer (= 0.037) . No diffp = 0.76). After transformation into a qualitative variable, the effect of age at menarche was no longer significant   had a higher risk of breast cancer  compared with those classified with VDG2. A higher risk was also observed for women with a first-degree family history of breast cancer  compared with those without, and for those with versus without a prior benign breast disease . Women who reported more than 4 h exercise per week had an aHR of 0.65 (95% CI: 0.51–0.83) using those who reported no exercise as reference. Finally, breast cancer risk was higher in women who reported the use of HT versus those who did not  . The proference) .The 4-year predicted risk of breast cancer ranged between 0.22% and 7.33% . The thrWe found the highest overall effect in the estimated risks of mammographic density, modifying the estimated 4-year risk of women between a 1.61% reduction and a 3.83% increase depending on her mammographic density . The purWe used data from a longitudinal cohort of 57,411 women to develop and validate a 4-year risk prediction model for breast cancer among women participating in BreastScreen Norway. The model included age, family history, mammographic density, BMI, previous benign breast disease, exercise, alcohol consumption, every use of hormone therapy (HT), age at menarche, and pregnancy. The wide variation in the absolute predicted risks, from 0.22% to 7.33%, contributes to substantiating the need to use such models to personalize breast cancer screening.Similarly to other studies, we found a higher risk of breast cancer among women with mammographically dense versus non-dense breasts, also after adjusting for BMI , suggestThe E/O ratio showed that the model slightly overestimated the risk in the population, (E/O ratio = 1.1), thus, a calibration before implementation is required. The discrimination of the model was modest in our study, (AUC = 62.6%), and comparable to other studies in breast cancer risk prediction . AUC couWhen plotting the 4-year risk, we found a broad range of values in the risk estimates, reflecting the differences in risk among women targeted for breast cancer screening. These differences highlight the ability of risk prediction models to identify women at higher and lower risk compared with the average, and their need for implementing personalized screening strategies. Offering risk-based strategies to women with broad differences in the risk of breast cancer could improve the efficiency of screening , diminisTwo clinical trials are currently underway to demonstrate the increased efficiency of personalized screening, one in Europe  and one To our knowledge, most models on breast cancer risk prediction have not included lifestyle variables, such as exercise or alcohol habits, despite being already proven risk factors for breast cancer ,32. ThesResults from our model add knowledge about risk prediction for breast cancer among asymptomatic women participating in BreastScreen Norway and can be used to classify women into low, average, and high-risk groups which could benefit from being offered personalized screening intervals or modalities. Despite previous studies that have shown that risk-based screening strategies for breast cancer could be cost-effective, future research is needed to find the most cost-effective criteria for classifying women into different risk groups ,6. PreviWe used individual data from women participating in a population-based screening program for breast cancer and several risk factors, and variables related to previous screening examinations were available. This is a strength of the study. Mammographic density was automatically measured by software, reducing the risk of measurement bias. This study has several limitations. Most of the risk factors were self-reported, which could lead to information bias. However, self-reporting is a common source of information in risk prediction models ,61. FurtThe risk prediction model developed and validated showed differences in the risk of breast cancer, supporting personalized screening, which is expected to improve the risk-benefit balance of organized screening.


Breast cancer is the commonest cancer among women in India, yet the uptake of early detection programs is poor. This leads to late presentation, advanced stage at the time of diagnosis, and high mortality. Poor accessibility and affordability are the most commonly cited barriers to screening: we analyse socio-cultural factors influencing the uptake of early detection programmes in a Universal Health Coverage (UHC) setting in India, where geographical and financial barriers were mitigated.Two hundred seventy-two women engaging in an awareness-based early detection program were recruited by randomization as the participant (P) group. A further 272 women who did not participate in the early detection programme were recruited as non-participants (NP). None of the groups were previously screened for breast cancer. Interviews were conducted using a 19-point questionnaire, consisting of closed-ended questions regarding demographics and social, cultural, spiritual and trust-related barriers.p = 0.02) and belonged to nuclear families . Although they reported more fear of isolation due to stigma , they had greater knowledge about breast cancer and trust in the health system compared to non-participants.The overall awareness about breast cancer was high among both groups. None of the groups reported accessibility-related barriers. Participants were more educated (58.09% vs 47.43%, The major socio-cultural barriers identified were joint family setups, lower education and awareness, and lack of trust in healthcare professionals. As more countries progress towards UHC, recognising socio-cultural barriers to seeking breast health services is essential in order to formulate context-specific solutions to increase the uptake of early detection and screening services.The online version contains supplementary material available at 10.1186/s12885-023-11359-3. Breast cancer (BC) is the leading cancer amongst women in India . AlthougIn 2010, the Government of India, through its National Health Mission, first launched a comprehensive non-communicable disease control program . HoweverUniversal Health Coverage (UHC) provides financial protection to patients and their families from out-of-pocket expenditure where healthcare provision is concerned . Indian This study was conducted in an urban community cohort in Mumbai, India. This urban community of 100,000 people is covered under an employees’ contributory health service scheme. Under this scheme, healthcare services are provided at a minimum flat contribution of one percent of the basic pay of the employee. Most families in this cohort were of middle socio-economic status and earned a fixed salary in the Department of Atomic Energy employee cohort. The health system is structured in the form of a two-tiered system within the eastern suburbs of Mumbai, India. It consists of 14 PHCs spread across the city. The PHCs refer patients to a central referral hospital when needed. The 14 PHCs served a population of 100,000, and 4 of these 14 centres were transformed into breast clinics for this study . In JuneGadgil et al. have published further details about this healthcare system, cohort, and organisation, in a separate manuscript dedicated to methodology of establishing an early detection program . The womThis prospective nested case–control study, conducted from June 2015 to June 2017, assessed the breast health-seeking behaviour of women who did not participate in the early cancer detection program, hereafter referred to as non-participants (NP). These non-participant women were compared to women who availed the program, henceforth referred to as participants (P), or the control group.n = 272 in each group was reached as the sample size.For the study, 80% power, 95% confidence intervals, and 12% participation rate in the program were attributed. Allowing for 10% attrition, Z = Confidence level at 95% p = Estimated prevalence or proportionse = range of CI (5% or 10%)The study was approved by the Ethics Committee of Bhabha Atomic Research Centre Hospital before starting the recruitment process . All methods were carried out in accordance with relevant guidelines and regulations.All women who were detected with abnormalities on clinical breast examinations were referred to the hospital situated in the same residential area for further investigation and treatment. Diagnostic mammography and cytology were carried out as a part of triple assessment, but not as a screening modality. CBE remained the sole screening modality for all women .We recruited women who visited PHCs for complaints other than breast-related symptoms and those who were accompanying their relatives to health centres as ‘non-participants’ (NP). All women who had participated in the early detection program were identified for the control group (P). We generated a randomised list of unique identification numbers (UIDs) from the list of participants, and recruited these women for the study. Recruitment was conducted in keeping with a 1:1 allocation ratio between participants and non-participants. We contacted the recruited individuals over the phone and arranged a convenient time and date for in-person interviews. Informed consent was obtained from all women during their hospital visit prior to interview. When recruited women were illiterate, informed consent was obtained from their respective legal guardians.Investigators were medical professionals with graduate and above level qualifications, with an experience of greater than 10 years at the community healthcare level. They attended a two-day training programme at the Department of Surgery at the central referral hospital where the early detection program was being implemented.Trained investigators then conducted in-person, face-to-face interviews with all participants. If an individual in the P or NP groups refused to be interviewed or could not be contacted, the next individual in the initial randomised list was contacted, and so on.n = 272) was not reached due to low response rates in both P and NP groups. More UIDs were generated and randomised again to recruit participants, and non-participants were recruited from PHCs, as explained above. This additional set allowed us to reach the target sample size in the second round.Recruitment and interviews were completed over two rounds. In the first round of six months, the target sample size number . Joint family set ups refer to two or more married couples of a single generation, or three or more married couples from multiple generations living in the same household. On the other hand, nuclear families refer to a single married couple living with or without their unmarried children . Questiop < 0.05 was set as the significance threshold.SPSS Version 24.0  and Microsoft Office Excel 2013 for Windows were used to perform statistical analysis. Socio-demographic characteristics of women were represented across both groups. Responses were presented in absolute numbers as well as percentages. Chi-square test of significance was used, and The total number of women recruited was 544: 272 participants and 272 non-participants. Figure p < 0.01). Additionally, women from nuclear families had significantly more participation in the early detection programme . Both groups were comparable in age, partner status, and occupation status.Table p = 0.009). Awareness regarding breast lumps as a symptom of BC was comparable across groups. However, participant women were significantly more aware that cancer could also present as blood-stained nipple discharge , skin changes over the breast  or a lump in the armpit . Furthermore, those who participated in the early detection program were significantly more aware of the need for breast examination in asymptomatic women .Women in both groups had high breast cancer awareness, but awareness was higher among participants .Both groups of women agreed that their health was important and that their family and/or spouses would support their attendance at breast clinics for routine check-ups. Significantly more women in the NP group felt they did not have time to attend routine check-ups . Fatalistic attitudes and spiritual beliefs regarding BC were similar in participants and non-participants, with no significant differences noted. None of the women perceived the ‘distance to clinics’ to be prohibitive or ‘accessibility’ as a barrier to attending the early detection program.A significant cultural barrier among participants was the social stigma of being labelled a “cancer patient” in the eyes of friends and neighbours, if seen attending breast clinics . There was a statistically significant difference in patients’ trust in the doctors’ ability to treat BC if found, between participants and non-participants . Furthermore, there was a significant difference in the reported rudeness and indifference of staff experienced by non-participants . Table Though there was no perceived risk of overdiagnosis in either of the groups, participants were more worried about losing the entire breast if detected with a lump or cancer (48% vs 39%, This study revealed that participants had higher levels of education, were more aware of the signs and symptoms of breast cancer, and the need for participation in early detection programmes even in the absence of symptoms. Non-participants belonged to joint families, and stated lack of time as a reason for not attending the programme. Despite higher education and awareness, participants feared social stigma and ‘losing the entire breast if detected with cancer’ but had significantly more faith in the health system, in that their doctors would be able to treat them if diagnosed with BC.Education and awareness have repeatedly been cited as facilitators of early health-seeking behaviour –25. SimiA recent systematic review on barriers to breast and cervical cancer screening  found thWomen are often expected to be primary caregivers for dependent children and the elderly due to set societal gender roles, especially in the Indian subcontinent. This could lead to self-neglect and not viewing their own health as a priority. Studies from Brazil and Chile have highlighted that self-neglect as a result of limited time, has led to a decrease in women performing breast self-examinations . FurtherDespite engaging in the early detection programme, and having higher levels of education and awareness, women who participated in the early detection program were noted to be more fearful of the social stigma of attending ‘cancer clinics’. The fear of being diagnosed with cancer and being socially isolated after diagnosis has been documented in Indian literature on screening for breast cancers. A qualitative study from South India, quoted women’s responses, “[I’m] scared, the family members may not mingle with us casually. If the society comes to know that is all, [I] fear [that], they will isolate us”, or “If they say, we have the disease after the screening, our life is gone after that, the life is over, that kind of negative thoughts [prevent going]” . This thParticipants demonstrated more fear than non-participants, especially of losing their entire breast if cancer were to be detected. This could perhaps be attributed to the fact that participants attended awareness talks where they learned about mastectomies and surgical management of the disease. However, it is important to note here that participant women overcame that stigma and fear and presented themselves for examination. Information providers hence must take care to adopt a comprehensive approach to awareness building, emphasising early operative benefits, availability of breast conservation surgery and addressing concerns of patients when they present themselves for consultations. A comprehensive approach aided by empathic communication skills helps prevent fear and foster trust. Yet, further interviews of these participant women to understand mechanisms of coping with fear and stigma to present themselves for screening would be deeply insightful.The defining parameter of the participant group remained that they had trust in their healthcare providers’ ability to treat them, if they were to be diagnosed with cancer. It is widely recognised that trust in physicians positively influences health-seeking behaviours . FurtherIn contrast, the non-participant group had expressed that the healthcare facility might not be able to adequately treat their BC if it were to be diagnosed and that healthcare workers were rude and indifferent. Given that this was a UHC setting and all patients are life-long members, it is possible that these perceptions among non-participants arose from their previous experiences at other outpatient departments or clinics prior to the study. Hence, they likely did not visit the breast clinics when they were invited to participate. The participants of the program on the other hand perhaps felt more cared for, when they responded to the invites by presenting themselves for breast examination. When they presented, receiving detailed information about cancer and careful handling of their questions and fears probably made them feel confident and reassured about the treating team. The clinics dedicated to breast examination also may have provided a less crowded environment, and the non-rushed, sensitive approach of the caregivers may have further contributed to their trust and confidence in the program. Haworth, Margalit  note thaThis study has a unique strength in studying a population covered by UHC. As such, this paper was able to analyse socio-cultural barriers outside of other commonly described barriers such as affordability and accessibility. This is further emphasised by our study’s findings that neither participants nor non-participants believed distance to or accessibility of the clinics to be prohibitive to their attendance, making a strong case for UHC. This is in stark contrast to most other publications on the subject. Pal et al.  noted fiThis study also has certain limitations. It is crucial to note that this study was based in Mumbai, a metropolitan city in India, where access to transport is markedly better than in most rural settings. Hence the generalizability of our findings to rural populations may be limited. A lack of open-ended questions in the methodology also limited the scope of the discussions in this paper. In-depth interviews enabling qualitative analysis would reveal a far more nuanced understanding of socio-cultural barriers, and hence are needed going forward. Future work could also benefit from a more targeted approach and explore the barriers to the uptake of early detection programmes in first-degree relatives of patients with breast cancer, who have been identified as a high-risk cohort .Higher education, trust, and nuclear families were protective factors and thus facilitators to engaging in the early detection programme. The major socio-cultural barriers identified in the study were joint family setups, lower education and awareness, and a lack of trust in healthcare professionals.Identifying these socio-cultural barriers to seeking breast health services is essential in order to formulate context-specific solutions to mitigate them. Future work is needed to study these socio-cultural barriers more in-depth. Our study has identified certain implications for changing practice: namely greater trust and awareness building. “Inter-sectorial co-ordination” has previously proven valuable in mobilising different stakeholders for organised cancer detection in low-resource settings . In addiAdditional file 1.Additional file 2: Supplementary Table 1. Regression analysis of factors associated with breast cancer screening.


Is change in mammographic breast density associated with the development of breast cancer, and does this change diverge from the expected decrease in density with age?In this nested case-control cohort study of 947 women attending breast screening during up to 10 years, a decrease in breast density was observed in all women regardless of subsequent breast cancer development. The rate of density change was significantly slower in the breast in which cancer was later diagnosed.This study found that evaluating longitudinal changes in breast density from digital mammograms may offer an additional tool for assessing risk of breast cancer and subsequent risk reduction strategies. Although breast density is an established risk factor for breast cancer, longitudinal changes in breast density have not been extensively studied to determine whether this factor is associated with breast cancer risk.To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer.This nested case-control cohort study was sampled from the Joanne Knight Breast Health Cohort of 10 481 women free from cancer at entry and observed from November 3, 2008, to October 31, 2020, with routine screening mammograms every 1 to 2 years, providing a measure of breast density. Breast cancer screening was provided for a diverse population of women in the St Louis region. A total of 289 case patients with pathology-confirmed breast cancer were identified, and approximately 2 control participants were sampled for each case according to age at entry and year of enrollment, yielding 658 controls with a total number of 8710 craniocaudal-view mammograms for analysis.Exposures included screening mammograms with volumetric percentage of density, change in volumetric breast density over time, and breast biopsy pathology-confirmed cancer. Breast cancer risk factors were collected via questionnaire at enrollment.Longitudinal changes over time in each woman’s volumetric breast density by case and control status.P = .04).The mean (SD) age of the 947 participants was 56.67 (8.71) years at entry; 141 were Black (14.9%), 763 were White (80.6%), 20 were of other race or ethnicity (2.1%), and 23 did not report this information (2.4%). The mean (SD) interval was 2.0 (1.5) years from last mammogram to date of subsequent breast cancer diagnosis . Breast density decreased over time in both cases and controls. However, there was a significantly slower decrease in rate of decline in density in the breast that developed breast cancer compared with the decline in controls (estimate = 0.027; 95% CI, 0.001-0.053; This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management. This nested case-control cohort study evaluates the association between change in mammographic density in each breast over time and risk of subsequent breast cancer. The association of breast density with breast cancer risk is consistent across a range of methods used to estimate density as the percentage of density .5 Volumetric density is an established risk factor for breast cancer and is incorporated into existing risk assessment models, including Tyrer-Cuzick model, version 8.6 However, to our knowledge, there are limited data on the association between change in density over time and risk of subsequent breast cancer diagnosis. Retrospective data from Korea show that change in the category of Breast Imaging Reporting and Data System (BI-RADS) breast density is associated with change in risk across 3 mammograms,7 consistent with an earlier meta-analysis.8 However, this study did not use volumetric analysis of density.Mammographic breast density is a well-established and strong risk factor for breast cancer.We hypothesized that there is a difference in the rate of change in breast density in women who subsequently develop breast cancer compared with those who do not. Using longitudinal, quantitative data captured for up to 10 years, we evaluated the association between rate of change in mammographic density and risk of breast cancer development.STROBE) reporting guideline.Ethical approval for this prospective nested case-control cohort study was obtained from the Washington University in St Louis institutional review board. Participants provided informed written consent. We followed the Strengthening the Reporting of Observational Studies in Epidemiology  or a diagnosis of breast cancer within 6 months of registration for the study, for a total of 10 481 women , with an SD of 2.4.Follow-up was conducted by linking to electronic health records, a tumor registry, and a death register. Routine screening mammograms were obtained every 1 to 2 years. A total of 8908 of the 10 481 women in our cohort (85%) had either a mammogram or clinic visit within the last 3 years of follow-up .We sampled 2 control participants for each case patient according to age at cohort entry and year of enrollment. Incident breast cancers were identified through record linkage to pathology and tumor registries. We identified 347 cases and 694 controls. After linkage to screening mammogram files, we excluded women with breast implants and those without screening mammograms retrieved, and we retained 289 cases and 658 controls with mammograms taken through October 31, 2020, resulting in a total number of 8710 craniocaudal-view mammograms for analysis. All analyses performed in this study used the nested case-control cohort for efficiency of image data processing.10 The questionnaire at entry assessed height, current weight, parity, age at first birth, cessation of menses (yes or no), age at menopause , age at hysterectomy, family history of breast cancer , history of biopsy-confirmed benign breast disease, current alcohol intake, and race and ethnicity.Women self-reported breast cancer risk factors on entry to the cohort. These risk factors were drawn from established and validated measures.12 The correlation between volumetric density generated from our automated algorithm with Volpara, version 1.5.0  was 0.81 according to an out-of-sample study with 375 women recruited from the mammography service at Washington University with a mean (SD) age of 47 (4.8) years  between cases and the controls. With this model, we accounted for correlations within a woman between both her breasts and separately for density measured on the same breast over time. We used Box-Cox transformation to normalize the distribution of breast density and performed model checking and evaluation of residuals , 763 were White (80.6%), and 20 were of other race or ethnicity (2.1%); 23 patients did not report this information (2.4%). Race and ethnicity were self-reported. The mean (SD) age of the patients was 56.67 (8.71) years at entry. The mean (SD) interval between screening mammograms was 1.3 (0.7) years . For the cases, the mean (SD) time from last mammogram to subsequent breast cancer diagnosis was 2.0 (1.5) years .The breast cancer risk factors assessed at entry for the women in this study, stratified by case and control status, are presented in P = .01) (P < .001), and women with a history of biopsy-confirmed benign breast disease had higher breast density .When the mean volumetric density of the 2 breasts was used, breast density at entry was significantly higher for cases compared with controls  . Breast P = .11). However, when density change in each breast was analyzed separately, there was a significant difference in the rate of density change over time in the breast that developed cancer compared with that in controls   . MammogrTo illustrate this significant change in the rate of decrease in density during the follow-up interval, in In this prospective cohort study designed to evaluate breast cancer risk over time, we used case-control sampling for efficient processing of 8710 prospectively ascertained digital mammographic images. We observed that breast density was higher at entry to the study for women who would later develop breast cancer compared with controls who remained cancer free. Volumetric breast density decreased significantly over time in both groups. To reflect the underlying biology of breast cancer development, we modeled each breast independently to allow for different rates of change for the breast that would develop breast cancer and the contralateral breast in a case patient that remained free from breast cancer. The rate of decrease in density for the breast that developed breast cancer was significantly slower than for controls who did not develop breast cancer. This observation, using longitudinal repeated measures, reflects the dynamic changes in breast tissue that differ significantly between women who develop breast cancer and those who do not. These data suggest that longitudinal changes in breast density may be used to refine the assessment of risk of breast cancer and to inform precision prevention strategies.16 Although density is an accepted intermediate marker of breast cancer risk,2 studies of change in density are limited. Previous studies have used reader-dependent, subjective measures of density and categorical density data .21 Previous studies have also been based on analysis of digitized radiographic images,23 which are of lower image quality than standard digital mammograms.25 Furthermore, changes in breast density in these studies were often assessed from comparison of only 2 time points. Despite these limitations, a recent meta-analysis of 4 studies reported that a change in BI-RADS breast density category is associated with an increased risk of breast cancer.8Breast density remains one of the most readily available summary measures from screening mammograms that may be used to assess future breast cancer risk.9 The mean (SD) time from most recent mammogram to breast cancer diagnosis was 2.0 (1.5) years, and we excluded mammograms within 6 months of diagnosis to avoid bias that can be induced if density is spuriously increased owing to undetected cancer.26 We used craniocaudal views because epidemiologic studies show that associations of density with breast cancer are stronger for craniocaudal views than for mediolateral oblique views.27Here, we moved beyond these limitations to draw on the full data of digital mammograms to capture changes within each breast over time. Analysis of mammographic images of each breast independently allows for the study of the specific breast in which cancer later develops. An additional strength of this study is the use of a median of 4  or more digital mammograms per participant during a follow-up period of 10 years. Follow-up mammograms from 2008 to 2020 reflect current clinical practice. The cohort was generated from a population undergoing routine screening and reflects the broader community.29 Finally, refining the trade-off of number of images and interval between images to assess the change over time will also inform translation to clinical practice.30Once mammography screening is begun from the age of 45 years (American Cancer Society) or 50 years (US Preventive Services Task Force), repeated either annually or biannually, women accumulate a series of mammograms. Therefore, the available data are naturally composed of longitudinal images for each breast. Traditional models focus on fixed prediction for some future interval conditional on information gathered at baseline. However, given the available longitudinal data, dynamic prediction that can be updated immediately after mammography conditional with the woman’s most up-to-date individualized density trajectory in each breast could improve overall breast cancer risk classification over time. These repeated measures can be further refined for routine practice and incorporated into a dynamic risk classification and hence guide personalized prevention services according to the woman’s level of risk. Further research is needed to define the clinical association between incorporation of density change over time and any subsequent risk reduction strategies that may be recommended as a result.15 we advanced one-time assessment of breast density, which has already been established as a key component for risk stratification.31 A systematic review shows that, in models predicting women’s risk of breast cancer that were published from 2007 to 2019, the addition of a onetime measure of breast density significantly increased discriminatory accuracy in 7 of 11 studies. The increase in the area under the curve ranged from 0.03 to 0.14.31 The current findings open the potential for dynamic prediction modeling to account for breast-specific density changes over time.In this prospective cohort study using repeated images and established statistical approaches to repeated-measures analysis,32 The potential to use the data embedded in mammographic images and make full use of longitudinal measures will facilitate optimizing risk stratification to guide more personalized risk reduction.33Much ongoing research focuses on mammography analysis to allow for an earlier diagnosis of breast cancer, when it is the most treatable.26The limitations of the study include a population that was predominantly White  and analysis of digital images from only 1 manufacturer (Hologic). However, other studies have shown breast density is a robust risk factor across race and ethnicity and mammography vendors.Using longitudinal digital mammograms of each breast during up to 10 years, this prospective cohort case-control study found that women with a slower decrease in breast density had a higher risk of developing breast cancer. These dynamic changes in density over time may be used to refine risk stratification and guide more individualized screening and prevention approaches.