Influential factors and prediction model of mammographic density among Chinese women.
Ontology highlight
ABSTRACT: Abstract
To evaluate the characteristics and influential factors of breast density and establish a new model for predicting breast density in Chinese women, so as to provide a basis for breast cancer screening techniques and duration.A total of 9412 women who were selected from screening and intervention techniques for Breast and Cervical Cancer Project between April 2018 and June 2019 were enrolled in this study. Selected women were randomly assigned to training and validation sets in a ratio of 1:1. Univariable and multivariable analyzes were performed by Logistic regression model. Nomogram was generated according to the results of multivariate analysis. Calibration, area under curve (AUC) and akaike information criterion (AIC) were used for measuring accuracy of prediction model.There were 377 (4.0%) women in breast imaging reporting and data system (BI-RADS) A category, 2164 (23.0%) in B category, 5749 (61.1%) in C category and 1122 (11.9%) in D category. Age duration, educational attainment, history of benign diseases, breastfeeding history, menopausal status, and body mass index (BMI) were imputed as independent influential factors for breast density in multivariable analysis. The AUC and AIC of training and validation set were 0.7158, 0.7139, and 4915.378, 4998.665, respectively.This study indicated that age, educational attainment, history of benign breast disease, breastfeeding history, menopausal status and BMI were independent influential factors of breast density. Nomogram generated on the basis of these factors could relatively predict breast density, which in turn could be used for recommendations of breast cancer screening techniques.
SUBMITTER: Shang MY
PROVIDER: S-EPMC8284716 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundMammographic density (MD) has not been systematically investigated among Chinese women. Breast cancer screening programmes provided detailed information on MD in a large number of asymptomatic women.MethodsIn the Multi-modality Independent Screening Trial (MIST), we estimated the association between MD and its influential factors using logistic regression, adjusting for age, body mass index (BMI) and study area. Differences between Chinese and other ethnic groups with respect to MD were also explored with adjustment for age and BMI.ResultsA total of 28 388 women aged 45 to 65 years, who had been screened by mammography, were enrolled in the study. Of these, 49.2% were categorized as having dense breasts (BI-RADS density 3 and 4) and 50.8% as fatty breasts (BI-RADS density 1 and 2). Postmenopausal status [odds ratio (OR) = 0.66; 95% confidence interval (CI): 0.62-0.70] and higher number of live births (OR = 0.56; 95% CI: 0.46-0.68) were inversely associated with MD, whereas prior benign breast disease (OR = 1.48; 95% CI: 1.40-1.56) and later age at first birth (OR = 1.17; 95% CI: 1.08-1.27) were positively associated with MD. In comparison with the data from the Breast Cancer Surveillance Consortium, we found that women in MIST were more likely to have fatty breasts than Americans (from the Breast Cancer Surveillance Consortium) in the older age group (?50 years) but more likely to have dense breasts in the younger age group (<50 years).ConclusionsThis study suggests that several risk factors for breast cancer were associated with breast density in Chinese women. Information on the determinants of mammographic density may provide valuable insights into breast cancer aetiology.
| S-EPMC4121553 | biostudies-literature
Project description:BACKGROUND:Female steroid hormone levels and exogenous hormone use influence breast cancer risk. We investigated the association between genetic variation in the hormone metabolism and signaling pathway and mammographic density, a strong predictor of breast cancer risk. METHODS:We genotyped 161 SNPs in 15 hormone metabolism pathway gene regions and evaluated mammographic density in 2,038 Singapore Chinese women. Linear regression analysis was used to investigate single-nucleotide polymorphism (SNP) and mammographic density association. An overall pathway summary was obtained using the adaptive ranked truncated product test. RESULTS:We did not find any of the individually tested SNPs to be associated with mammographic density after a multiple testing correction. There was no evidence of an overall effect on mammographic density of genetic variation in the hormone metabolism pathway. CONCLUSIONS:In this cross-sectional study, genetic variation in hormone metabolism pathway was not associated with mammographic density in Singapore Chinese women. IMPACT:Consistent with existing data from Caucasian populations, polymorphisms in hormone pathway genes are not likely to be strong predictors of mammographic density in Asian women.
| S-EPMC4197055 | biostudies-literature
Project description:BackgroundCharacteristics of mammographic density for Chinese women are understudied. This study aims to identify factors associated with mammographic density in China using a quantitative method.MethodsMammographic density was measured for a total of 1071 (84 with and 987 without breast cancer) women using an automatic algorithm AutoDensity. Pearson tests examined relationships between density and continuous variables and t-tests compared differences of mean density values between groupings of categorical variables. Linear models were built using multiple regression.ResultsPercentage density and dense area were positively associated with each other for cancer-free (r=0.487, p<0.001) and cancer groups (r=0.446, p<0.001), respectively. For women without breast cancer, weight and BMI (p<0.001) were found to be negatively associated (r=-0.237, r=-0.272) with percentage density whereas they were found to be positively associated (r=0.110, r=0.099) with dense area; age at mammography was found to be associated with percentage density (r=-0.202, p<0.001) and dense area (r=-0.086, p<0.001) but did not add any prediction within multivariate models; lower percentage density was found within women with secondary education background or below compared to women with tertiary education. For women with breast cancer, percentage density demonstrated similar relationships with that of cancer-free women whilst breast area was the only factor associated with dense area (r=0.739, p<0.001).ConclusionThis is the first time that mammographic density was measured by a quantitative method for women in China and identified associations should be useful to health policy makers who are responsible for introducing effective models of breast cancer prevention and diagnosis.
| S-EPMC6444251 | biostudies-literature
Project description:PURPOSE:Mammographic density (MD) is a strong risk factor for breast cancer, yet its relationship with tumor characteristics is not well established, particularly in Asian populations. METHODS:MD was assessed from a total of 2001 Chinese breast cancer patients using Breast Imaging Reporting and Data System (BI-RADS) categories. Molecular subtypes were defined using immunohistochemical status on ER, PR, HER2, and Ki-67, as well as tumor grade. Multinomial logistic regression was used to test associations between MD and molecular subtype (luminal A?=?reference) adjusting for age, body mass index (BMI), menopausal status, parity, and nodal status. RESULTS:The mean age at diagnosis was 51.7 years (SD?=?10.7) and the average BMI was 24.7 kg/m2 (SD?=?3.8). The distribution of BI-RADS categories was 7.4% A?=?almost entirely fat, 24.2% B?=?scattered fibroglandular dense, 49.4% C?=?heterogeneously dense, and 19.0% D?=?extremely dense. Compared to women with BI-RADS?=?A/B, women with BI-RADS?=?D were more likely to have HER2-enriched tumors (OR?=?1.81, 95% CI 1.08-3.06, p?=?0.03), regardless of menopausal status. The association was only observed in women with normal (<?25 kg/m2) BMI (OR?=?2.43, 95% CI 1.24-4.76, p?<?0.01), but not among overweight/obese women (OR: 0.98, 95% CI 0.38-2.52, p?=?0.96). CONCLUSIONS:Among Chinese women with normal BMI, higher breast density was associated with HER2-enriched tumors. The results may partially explain the higher proportion of HER2+?tumors previously reported in Asian women.
| S-EPMC7304859 | biostudies-literature
Project description:Studies investigating associations between mammographic density (MD) and breast cancer subtypes have generated mixed results. We previously showed that having extremely dense breasts was associated with the human epidermal growth factor receptor-2 (HER2)-enriched subtype in Chinese breast cancer patients. In this study, we reevaluated the MD-subtype association in 1549 Chinese breast cancer patients, using VolparaDensity software to obtain quantitative MD measures. All statistical tests were 2-sided. Compared with women with luminal A tumors, women with luminal B/HER2- (odds ratio [OR] = 1.20, 95% confidence interval [CI] = 1.04 to 1.38; P = .01), luminal B/HER2+ (OR = 1.22, 95% CI = 1.03 to 1.46; P = .03), and HER2-enriched tumors (OR = 1.30, 95% CI = 1.06 to 1.59; P = .01) had higher fibroglandular dense volume. These associations were stronger in patients with smaller tumors (<2 cm). In contrast, the triple-negative subtype was associated with lower nondense volume (OR = 0.82, 95% CI = 0.68 to 0.99; P = .04), and the association was only seen among older women (age 50 years or older). Although biological mechanisms remain to be investigated, the associations for the HER2-enriched and luminal B subtypes with increasing MD may partially explain the higher prevalence of luminal B and HER2+ breast cancers previously reported in Asian women.
| S-EPMC7791622 | biostudies-literature
Project description:BackgroundWhile mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal women. Then, we aimed to identify potential correlates of these biomarkers to guide future research on potential modifiable determinants of mammographic density.MethodsA total of 132 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, hexose) were measured by tandem liquid chromatography/mass spectrometry in plasma samples from 573 premenopausal participants in the Mexican Teachers' Cohort. Associations between metabolites and percent mammographic density were assessed using linear regression models, adjusting for breast cancer risk factors and accounting for multiple tests. Mean concentrations of metabolites associated with percent mammographic density were estimated across levels of several lifestyle and metabolic factors.ResultsSphingomyelin (SM) C16:1 and phosphatidylcholine (PC) ae C30:2 were inversely associated with percent mammographic density after correction for multiple tests. Linear trends with percent mammographic density were observed for SM C16:1 only in women with body mass index (BMI) below the median (27.4) and for PC ae C30:2 in women with a BMI over the median. SM C16:1 and PC ae C30:2 concentrations were positively associated with cholesterol (total and HDL) and inversely associated with number of metabolic syndrome components.ConclusionsWe identified new biomarkers associated with mammographic density in young women. The association of these biomarkers with mammographic density and metabolic parameters may provide new perspectives to support future preventive actions for breast cancer.
| S-EPMC8305592 | biostudies-literature
Project description:TGF-? plays a critical role in normal mammary development and morphogenesis. Decreased TGF-? signaling has been associated with increased mammographic density. Percent mammographic density (PMD) adjusted for age and body mass index (BMI) is a strong risk factor and predictor of breast cancer risk. PMD is highly heritable, but few genetic determinants have been identified. We investigated the association between genetic variation in TGFB1 and PMD using a cross-sectional study of 2,038 women who were members of the population-based Singapore Chinese Health Study cohort. We assessed PMD using a computer-assisted method. We used linear regression to examine the association between nine tagging single-nucleotide polymorphisms (SNP) of TGFB1 and PMD and their interaction with parity, adjusting for age, BMI, and dialect group. We calculated P values adjusted for correlated tests (P(ACT)) to account for multiple testing. The strongest association was observed for rs2241716. Adjusted PMD was higher by 1.5% per minor allele (P(ACT) = 0.04). When stratifying by parity, this association was limited to nulliparous women. For nulliparous women, adjusted PMD was higher by 8.6% per minor allele (P(ACT) = 0.003; P for interaction with parity = 0.002). Three additional TGFB1 tagging SNPs, which were in linkage disequilibrium with rs2241716, were statistically significantly associated with adjusted PMD (P(ACT) < 0.05) for nulliparous women. However, none of these three SNPs showed statistically significant association after adjusting for rs2241716. Our data support that TGFB1 genetic variation may be an important genetic determinant of mammographic density measure that predicts breast cancer risk, particularly in nulliparous women.
| S-EPMC3740538 | biostudies-literature
Project description:To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk.Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r 2 in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk.In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both).These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.
| S-EPMC5568000 | biostudies-other
Project description:Prior research suggests that several endogenous hormones in premenopausal women are associated with breast cancer risk; however, few studies have evaluated associations of endogenous hormones with mammographic density (MD) in premenopausal women. We conducted a cross-sectional study of plasma hormone levels in relation to MD among 634 cancer-free premenopausal women in the Nurses' Health Study II. We measured percent MD from screening mammograms using a computer-assisted method. We assayed estradiol, estrone, and estrone sulfate in blood samples timed in early follicular and mid-luteal phases of the menstrual cycle as well as testosterone, androstenedione, progesterone, dehydroepiandrosterone (DHEA), DHEA sulfate, sex hormone-binding globulin (SHBG), and anti-Müllerian hormone in luteal or untimed samples. We used multivariable linear regression to quantify the association of %MD with quartiles of each hormone, adjusting for age, body mass index, and breast cancer risk factors. Women in the highest quartile of follicular estradiol levels had significantly greater %MD compared to those in the lowest quartile [difference, 6.7 percentage points; 95% confidence interval (CI) 2.2, 11.3; p-trend?<?0.001]. Similar associations were observed for follicular free estradiol but not luteal-phase estradiol. Also, women in the top (vs. bottom) quartile of free testosterone had significantly lower %MD (difference, -?4.7; 95% CI -?8.7, -?0.8; p-trend?=?0.04). Higher SHBG was significantly associated with higher percent MD (difference, 4.8; 95% CI 1.1, 8.6; p-trend?=?0.002). Percent MD was not strongly associated with other measured hormones. Results were similar in analyses that excluded women with anovulatory cycles. Our findings suggest that follicular estradiol and SHBG may play an important role in premenopausal percent MD.
| S-EPMC5864534 | biostudies-literature
Project description:Metabolic syndrome has been associated with an increased risk of breast cancer; however, little is known about the association between metabolic syndrome and percent mammographic density, a strong predictor of breast cancer. We analyzed cross-sectional data from 789 premenopausal and 322 postmenopausal women in the Mexican Teacher's Cohort (ESMaestras). Metabolic syndrome was defined according to the harmonized definition. We measured percent density on mammograms using a computer-assisted thresholding method. Multivariable linear regression was used to estimate the association between density and metabolic syndrome, as well as its components by state (Jalisco, Veracruz) and menopausal status (premenopausal, postmenopausal). Among premenopausal women in Jalisco, women with metabolic syndrome had higher percent density than those without after adjusting for potential confounders including BMI [difference = 4.76; 95% confidence interval (CI), 1.72-7.81]. Among the metabolic syndrome components, only low high-density lipoprotein levels (<50 mg/dL) were associated with significantly higher percent density among premenopausal women in Jalisco (difference = 4.62; 95% CI, 1.73-7.52). Metabolic syndrome was not associated with percent density among premenopausal women in Veracruz (difference = -2.91; 95% CI, -7.19 to 1.38), nor among postmenopausal women in either state. Metabolic syndrome was associated with higher percent density among premenopausal women in Jalisco, Mexico, but was not associated with percent density among premenopausal women in Veracruz, Mexico, or among postmenopausal women in either Jalisco or Veracruz. These findings provide some support for a possible role of metabolic syndrome in mammographic density among premenopausal women; however, results were inconsistent across states and require further confirmation in larger studies.
| S-EPMC4227611 | biostudies-literature