Assessing risk of breast cancer in an ethnically South-East Asia population (results of a multiple ethnic groups study)

The impact of BMI on breast cancer - an updated systematic review and meta-analysis

BACKGROUND

Breast cancer is the most frequent form of cancer in women all over the world. It is the main cause of cancer death and the most often diagnosed cancer in women in 140 of the world's 184 countries. The link between breast cancer risk and body mass index (BMI) has gotten increasing attention in recent years, although the results are still debatable. Therefore, the current systematic review and meta-analysis evaluate the impact of BMI on breast cancer.

METHODS

The current study was carried out as a systematic review and meta-analysis, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We systematically searched Cochrane, Google Scholar, PubMed, EMBASE and Scopus databases to identify eligible articles impact of BMI on breast cancer with the appropriate Medical Subject Headings (MeSH). The Newcastle-Ottawa checklist was used for the risk of assessment for the included studies. Meta-analysis was performed using Review Manager 5.3 software.

RESULTS

Forty-six studies were included in the current review, which met the selection criteria of the current review. Among included 46 studies in this review, 50% (n = 23) of the studies found the HER2 type of breast cancer followed by triple-negative and HR-positive. The obesity was significantly higher in the case group compared with the control group (P < .001). Heterogeneity between the 14 studies is medium (I2 = 72%). In this review, there was no significant relation between overweight and breast cancer in women (P > .05). Heterogenecity between the 14 studies is medium (I2 = 89%). However, after removing the publication bias a significant relation between overweightness and breast cancer in women (P = .0005) was observed.

CONCLUSION

Obese breast cancer patients are a specific type of patient. They are more likely to develop cancer. Their need to surgery and radiation may cause greater difficulties. Obesity and overweight in women greatly increase the risk of breast cancer, according to the findings of the current meta-analysis. To confirm these findings and understand the pathogenic pathways, more research is required.

Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population?

Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580-0.628). Calibration was better for longer-term prediction horizons (E/O_{long-term ranges}: 0.86-1.71; E/O_{short-term ranges}:1.24-3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval.

Development and evaluation of the screening performance of a low-cost high-risk screening strategy for breast cancer

OBJECTIVE

To develop and evaluate the screening performance of a low-cost high-risk screening strategy for breast cancer in low resource areas.

METHODS

Based on the Multi-modality Independent Screening Trial, 6 questionnaire-based risk factors of breast cancer (age at menarche, age at menopause, age at first live birth, oral contraceptive, obesity, family history of breast cancer) were used to determine the women with high risk of breast cancer. The screening performance of clinical breast examination (CBE), breast ultrasonography (BUS), and mammography (MAM) were calculated and compared to determine the optimal screening method for these high risk women.

RESULTS

A total of 94 breast cancers were detected among 31,720 asymptomatic Chinese women aged 45-65 years. Due to significantly higher detection rates (DRs) and suitable coverage of the population, high risk women were defined as those with any of 6 risk factors. Among high risk women, the DR for BUS [3.09/1,000 (33/10,694)] was similar to that for MAM [3.18/1,000 (34/10,696)], while it was significantly higher than that for the CBE [1.73/1,000 (19/10,959), P = 0.002]. Compared with MAM, BUS showed significantly higher specificity [98.64% (10,501/10,646) vs. 98.06% (10,443/10,650), P = 0.001], but no significant differences in sensitivity [68.75% (33/48) vs. 73.91% (34/46)], positive prediction values [18.54% (33/178) vs. 14.11% (34/241)], and negative prediction values [99.86% (10,501/10,516) vs. 99.89% (10,443/10,455)]. Further analyses showed no significant difference in the percentages of early stage breast cancer [53.57% (15/28) vs. 50.00% (15/30)], lymph node involvement [22.73% (5/22) vs. 28.00% (7/25)], and tumor size ≥ 2 cm [37.04% (10/27) vs. 29.03% (9/31)] between BUS and MAM. Subgroup analyses stratified by breast densities or age at enrollment showed similar results.

CONCLUSIONS

The low-cost high-risk screening strategy based on 6 questionnaire-based risk factors was an easy-to-use method to identify women with high risk of breast cancer. Moreover, BUS and MAM had comparable screening performances among high risk women.

Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study

Purpose: To develop and to validate a risk-predicted nomogram for downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4a breast lesions. Patients and Methods: We enrolled 680 patients with breast lesions that were diagnosed as BI-RADS category 4a by conventional ultrasound from December 2018 to June 2019. All 4a lesions were randomly divided into development and validation groups at the ratio of 3:1. In the development group consisting of 499 cases, the multiple clinical and ultrasound predicted factors were extracted, and dual-predicted nomograms were constructed by multivariable logistic regression analysis, named clinical nomogram and ultrasound nomogram, respectively. Patients were twice classified as either "high risk" or "low risk" in the two nomograms. The performance of these dual nomograms was assessed by an independent validation group of 181 cases. Receiver Operating Characteristic (ROC) curve and diagnostic value were calculated to evaluate the applicability of the new model. Results: After multiple logistic regression analysis, the clinical nomogram included 2 predictors: age and the first-degree family members with breast cancer. The area under the curve (AUC) value for the clinical nomogram was 0.661 and 0.712 for the development and validation groups, respectively. The ultrasound nomogram included 3 independent predictors (margins, calcification and strain ratio), and the AUC value in this nomogram was 0.782 and 0.747 in the development and validation groups, respectively. In the development group of 499 patients, approximately 50.90% (254/499) of patients were twice classified "low risk", with a malignancy rate of 1.18%. In the validation group of 181 patients, approximately 47.51% (86/181) of patients had been twice classified as "low risk", with a malignancy rate of 1.16%. Conclusions: A dual-predicted nomogram incorporating clinical factors and imaging characteristics is an applicable model for downgrading the low-risk lesions in BI-RADS category 4a and shows good stability and accuracy, which is useful for decreasing the rate of invasive examinations and surgery.

Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review

Introduction

Currently, the Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model (GM) has been widely recognized and adapted for to study disparity in racial and ethnic groups in America including Asian and Pacific Islander American females. However, its applicability outside America remains uncertain due to diversity in epidemiology and risk factors of breast cancer in populations especially in Asian females. We sought to evaluate the performance of the GM to predict breast cancer risk in Asian countries.

Material and methods

This study identified articles published from 2010 by searching PubMed, MEDLINE, Scopus, Web of Science, Google Scholar and gray literature. The initial search terms were breast cancer, mammary, carcinoma, tumor, neoplasm, risk assessment tool, BCRAT, breast cancer prediction, Gail model, Asia, and Asian.

Results

The search yielded 20 articles, with 7 articles addressing the AUC and/or the expected (E) to observed (O) ratio of predicted breast cancer risk, representing the accuracy of the GM in the Asian population. One publication reported the sensitivity and specificity but no AUC. None of the studies were accepted as the standard for reporting prognostic models. Several studies reported good prognostic testing and likely developed a new model modifying the items in the instrument.

Conclusion

The results are not strong enough to develop breast cancer risk in the setting of Asian countries. Involving the breast cancer risk of the Asian population in developing a prognostic model with good statistical understanding is particularly important and can reduce flawed or biased models. Identifying the best methods to achieve well-suited prognostic models in the Asian population should be a priority.

Gail Model Underestimates Breast Cancer Risk in Thai Population

Background: The Gail model is the most widely used method for breast cancer risk estimation. This model has been studied and verified for its validity in many groups but there has yet to be a study to validate the Gail model in a Thai population. This study aims to evaluate whether the Gail model can accurately calculate the risk of breast cancer among Thai women. Methods: The subjects were recruited from the Division of Head, Neck, and Breast Surgery, Department of Surgery, Siriraj Hospital. The patients attending the division were asked to enroll in the study and complete questionnaires. Gail model scores were then calculated. Relationships between parameters were examined using the Pearson’s chi-square test, Fisher’s exact test, and independent-samples t-test. Results: There were 514 women recruited. Age, parity, age at first-live birth, and history of atypical ductal hyperplasia (ADH) were significant risk factors for breast cancer. The 5-year and lifetime risk score for breast cancer calculated by the Gail model were not significantly different between the patient and the control subjects. The proportions of the subjects with lifetime risk ≥20% were significantly higher in breast cancer patients (p=0.049). Conclusion: The Gail model underestimated the risk of breast cancer in Thai women. Calibration of the model is still required before adoption in Thai population.

Long-term excess risk of breast cancer after a single breast density measurement

AIM

Breast density is a risk factor for breast cancer. As density changes across a woman's life span, we studied for how long a single density measurement taken in (post-)menopausal women remains informative.

METHODS

We used data from Singaporean women who underwent a single mammography screen at age 50-64 years. For each case with breast cancer diagnosed at screening or in the subsequent 10 years, whether screen detected or diagnosed following symptoms, two age-matched controls were selected. We studied the excess risk of breast cancer, calculated as an odds ratio (OR) with conditional logistic regression and adjusted for body mass index, associated with 26-50% and with 51-100% density compared with ≤25% density by time since screening.

RESULTS

In total, 490 women had breast cancer, of which 361 were diagnosed because of symptoms after screening. Women with 51-100% breast density had an excess risk of breast cancer that did not seem to attenuate with time. In 1-3 years after screening, the OR was 2.22 (95% confidence interval [CI]: 1.07-4.61); in 4-6 years after screening, the OR was 4.09 (95% CI: 2.21-7.58), and in 7-10 years after screening, the OR was 5.35 (95% CI: 2.57-11.15). Excess risk with a stable OR of about 2 was also observed for women with 26-50% breast density. These patterns were robust when the analyses were limited to post-menopausal women, non-users of hormonal replacement therapy and after stratification by age at density measurement.

CONCLUSION

A single breast density measurement identifies women with an excess risk of breast cancer during at least the subsequent 10 years.

Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model

Background

Considering the lack of efficient breast cancer prediction models suitable for general population screening in China. We aimed to develop a risk prediction model to identify high-risk populations, to help with primary prevention of breast cancer among Han Chinese women.

Methods

A cause-specific competing risk model was used to develop the Han Chinese Breast Cancer Prediction model. Data from the Shandong Case-Control Study (328 cases and 656 controls) and Taixing Prospective Cohort Study (13,176 participants) were used to develop and validate the model. The expected/observed (E/O) ratio and C-statistic were calculated to evaluate calibration and discriminative accuracy of the model, respectively.

Results

Compared with the reference level, the relative risks (RRs) for highest level of number of abortions, age at first live birth, history of benign breast disease, body mass index (BMI), family history of breast cancer, and life satisfaction scores were 6.3, 3.6, 4.3, 1.9, 3.3, 2.4, respectively. The model showed good calibration and discriminatory accuracy with an E/O ratio of 1.03 and C-statistic of 0.64.

Conclusions

We developed a risk prediction model including fertility status and relevant disease history, as well as other modifiable risk factors. The model demonstrated good calibration and discrimination ability.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5321-1) contains supplementary material, which is available to authorized users.

Breast Cancer Risk From Modifiable and Non-Modifiable Risk Factors among Women in Southeast Asia: A Meta-Analysis

Objective:

The aim of this study was to determine breast cancer risk from modifiable and non-modifiable factors among women in Southeast Asia.

Methods:

This meta-analysis was performed on research articles on breast cancer risk factors in PubMed, ProQuest and EBSCO databases published between 1997 and October 2017. Pooled odds ratios (OR) are calculated using fixed and random-effect models. Data were processed using Review Manager 5.3 (RevMan 5.3).

Results:

From a total of 1,211 articles, 15 studies (1 cohort and 14 case control studies) met the criteria for systematic review. Meta-analysis results showed that of the known modifiable risk factors for breast cancer, parity (nulipara) had the highest odd ratio (OR = 1.85 [95% CI 1.47-2.32]) followed by body mass index (overweight) (OR = 1.61 [95% CI 1.43-1.80]) and use of oral contraceptives (OR = 1.27 [95% CI 1.07-1.51]). Of non-modifiable risk factors, family history of breast cancer had the highest odd ratio (OR = 2.53 [95% CI 1.25-5.09]), followed by age (≥ 40 years) (OR = 1.53 [95% CI 1.34-1.76]) and menopausal status (OR = 1.44 [95% CI 1.26-1.65]).

Conclusion:

This analysis confirmed associations between both modifiable risk factors (parity, body mass index and use of oral contraceptives) and non-modifiable risk factors (family history of breast cancer, age and menopausal status) with breast cancer.

Risk assessment model for invasive breast cancer in Hong Kong women

No risk assessment tool is available for identifying high risk population of breast cancer (BCa) in Hong Kong. A case-control study including 918 BCa cases and 923 controls was used to develop the risk assessment model among Hong Kong Chinese women.Each participant received an in-depth interview to obtain their lifestyle and environmental risk factors. Least absolute shrinkage and selection operator (LASSO) selection model was used to select the optimal risk factors (LASSO-model). A risk score system was constructed to evaluate the cumulative effects of selected factors. Bootstrap simulation was used to test the internal validation of the model. Model performance was evaluated by receiver-operator characteristic curves and the area under the curve (AUC).Age, number of parity, number of BCa cases in 1st-degree relatives, exposure to light at night, and sleep quality were the common risk factors for all women. Alcohol drinking was included for premenopausal women; body mass index, age at menarche, age at 1st give birth, breast feeding, using of oral contraceptive, hormone replacement treatment, and history of benign breast diseases were included for postmenopausal women. The AUCs were 0.640 (95% CI, 0.598-0.681) and 0.655 (95% CI, 0.621-0.653) for pre- and postmenopausal women, respectively. Further subgroup evaluation revealed that the model performance was better for women aged 50 to 70 years or ER-positive.This BCa risk assessment tool in Hong Kong Chinese women based on LASSO selection is promising, which shows a slightly higher discriminative accuracy than those developed in other populations.

Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction

Introduction

Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population.

Methods

We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values.

Results

During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm², 21-30cm², 31-40cm², 41-50cm², 51-60cm², and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively.

Conclusion

Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.

A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort

Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520; 0.646] and 0.621 [0.563; 0.679] using the kNN and 0.565 [0.500; 0.631] and 0.617 [0.561; 0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554; 0.675] and 0.549 [0.495; 0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.

Factors associated with mammography adherence among married Chinese women in Yanbian, China

BACKGROUND

Despite the efficacy of regular mammograms, the incidence and mortality rate of breast cancer have been increasing in China. Insufficient studies on the factors affecting mammography adherence in Chinese married women have been conducted. The purpose of the present study was to explore the factors associated with adherence to guidelines for regular mammography among Chinese married women.

MATERIALS AND METHODS

The participants were recruited conveniently and included Chinese and Korean Chinese women who were married, living at Yanbian City in China. Demographic information, status regarding eight risk factors of breast cancer, health responsibility, and perceived benefits/barriers of mammography were obtained. Descriptive analyses, t-test, and multivariate analysis were performed. Hierarchical logistic regression was conducted to explore the factors associated with regular mammography adherence in Chinese and Korean Chinese subgroups.

RESULTS

About 24% of the sample population was adherent in going for regular mammography. The adherent group was significantly more educated, had more children, and had a lower proportion experiencing early menarche and a greater menopausal proportion than the non-adherent group. The final model using logistic regression analysis showed that being Chinese [OR=2.199 (1.224-3.951)], having no or one child [OR=4.879 (1.835-12.976)], early menarche [OR=3.515 (1.057-11.694)], being menopausal [OR=3.120 (0.965-10.088)], aged 40-49 [OR=2.374 (1.099-5.124)], having low education [OR=0.400 (0.211-0.765)], and perceiving greater benefits in doing mammography [OR=1.080 (1.014-1.151)] were significantly associated with mammography adherence, after controlling for covariates.

CONCLUSIONS

Sociocultural sensitive intervention for minorities should be emphasized when improving the adherence of regular mammography. Intervention tailored for women with lower education should be delivered and the benefits of mammography should be propagated to women in rural areas of China.

Validation of electrical impedance tomography qualitative and quantitative values and comparison of the numeric pain distress score against mammography

Electrical impedance tomography (EIT) is a potential supplement for mammogram screening. This study aimed to evaluate and feasibility of EIT as opposed to mammography and to determine pain perception with both imaging methods. Women undergoing screening mammography at the Radiology Department of National University of Malaysia Medical Centre were randomly selected for EIT imaging. All women were requested to give a pain score after each imaging session. Two independent raters were chosen to define the image findings of EIT. A total of 164 women in the age range from 40 to 65-year-old participated and were divided into two groups; normal and abnormal. EIT sensitivity and specificity for rater 1 were 69.4% and 63.3, whereas for rater 2 they were 55.3% and 57.0% respectively. The reliability for each rater ranged between good to very good (p<0.05). Quantitative values of EIT showed there were significant differences in all values between groups (ANCOVA, p<0.05). Interestingly, EIT scored a median pain score of 1.51±0.75 whereas mammography scored 4.15±0.87 (Mann Whitney U test, p<0.05). From these quantitative values, EIT has the potential as a health discriminating index. Its ability to replace image findings from mammography needs further investigation.

Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women

The optimal approach regarding breast cancer screening for Chinese women is unclear due to the relative low incidence rate. A risk assessment tool may be useful for selection of high-risk subsets of population for mammography screening in low-incidence and resource-limited developing country. The odd ratios for six main risk factors of breast cancer were pooled by review manager after a systematic research of literature. Health risk appraisal (HRA) model was developed to predict an individual's risk of developing breast cancer in the next 5 years from current age. The performance of this HRA model was assessed based on a first-round screening database. Estimated risk of breast cancer increased with age. Increases in the 5-year risk of developing breast cancer were found with the existence of any of included risk factors. When individuals who had risk above median risk (3.3‰) were selected from the validation database, the sensitivity is 60.0% and the specificity is 47.8%. The unweighted area under the curve (AUC) was 0.64 (95% CI = 0.50-0.78). The risk-prediction model reported in this article is based on a combination of risk factors and shows good overall predictive power, but it is still weak at predicting which particular women will develop the disease. It would be very helpful for the improvement of a current model if more population-based prospective follow-up studies were used for the validation.