High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer.
Ontology highlight
ABSTRACT: Introduction
Mammographic density (MD) is a strong, independent risk factor for breast cancer, but measuring MD is time consuming and reader dependent. Objective MD measurement in a high-throughput fashion would enable its wider use as a biomarker for breast cancer. We use a public domain image-processing software for the fully automated analysis of MD and penalized regression to construct a measure that mimics a well-established semiautomated measure (Cumulus). We also describe measures that incorporate additional features of mammographic images for improving the risk associations of MD and breast cancer risk.Methods
We randomly partitioned our dataset into a training set for model building (733 cases, 748 controls) and a test set for model assessment (765 cases, 747 controls). The Pearson product-moment correlation coefficient (r) was used to compare the MD measurements by Cumulus and our automated measure, which mimics Cumulus. The likelihood ratio test was used to validate the performance of logistic regression models for breast cancer risk, which included our measure capturing additional information in mammographic images.Results
We observed a high correlation between the Cumulus measure and our measure mimicking Cumulus (r = 0.884; 95% CI, 0.872 to 0.894) in an external test set. Adding a variable, which includes extra information to percentage density, significantly improved the fit of the logistic regression model of breast cancer risk (P = 0.0002).Conclusions
Our results demonstrate the potential to facilitate the integration of mammographic density measurements into large-scale research studies and subsequently into clinical practice.
SUBMITTER: Li J
PROVIDER: S-EPMC3680940 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BACKGROUND:Mammographic density is a strong risk factor for breast cancer. METHODS:We present a novel approach to enhance area density measures that takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesized that the grey scale of film mammograms is normalized to volume breast density but not pectoral density and thus pectoral density becomes an independent marker of volumetric density. RESULTS:From analysis of data from a Swedish case-control study (1,286 breast cancer cases and 1,391 control subjects, ages 50-75 years), we found that the mean intensity of the pectoral muscle (MIP) was highly associated with breast cancer risk [per SD: OR = 0.82; 95% confidence interval (CI), 0.75-0.88; P = 6 × 10(-7)] after adjusting for a validated computer-assisted measure of percent density (PD), Cumulus. The area under curve (AUC) changed from 0.600 to 0.618 due to using PD with the pectoral muscle as reference instead of a standard area-based PD measure. We showed that MIP is associated with a genetic variant known to be associated with mammographic density and breast cancer risk, rs10995190, in a subset of women with genetic data. We further replicated the association between MIP and rs10995190 in an additional cohort of 2,655 breast cancer cases (combined P = 0.0002). CONCLUSIONS:MIP is a marker of volumetric density that can be used to complement area PD in mammographic density studies and breast cancer risk assessment. IMPACT:Inclusion of MIP in risk models should be considered for studies using area PD from analog films.
| S-EPMC5085828 | biostudies-literature
Project description:IntroductionKnown 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.MethodsWe 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.ResultsDuring 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.ConclusionMammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.
| S-EPMC4581713 | biostudies-literature
Project description:Elevated mammographic density (MD) is an established breast cancer risk factor. Studies examining relationships between MD and breast cancer risk factors are limited in China, where established breast cancer risk factors are less prevalent but dense breasts are more prevalent than Western countries. This study included 11,478 women (45-69 years; 36% premenopausal) participating in an ongoing national cancer screening program in 11 urban provinces in China and predicted as having high-risk for breast cancer. Polytomous logistic regression was performed to assess associations between MD and risk factors by comparing each higher Breast Imaging Reporting and Data System (BI-RADS) category (2, 3, or 4) to the lowest category (BI-RADS, 1). We found associations of increasing age, body mass index, weight, postmenopausal status, and parity with lower MD. Higher levels of education, increasing height, and later first birth were associated with higher MD. These associations did not vary by menopausal status. Additionally, the association between longer period of breastfeeding and lower MD was seen among postmenopausal women only (Pinteraction = 0.003). Having first-degree relatives with breast cancer diagnosed before 50 years was associated with lower MD only among premenopausal women (Pinteraction = 0.061). We found effects of established breast cancer risk factors on MD showed similar directions in Chinese and Western women, supporting the hypothesis that MD represents cumulative exposure to breast cancer risk factors over the life course. Our findings help to understand the biological basis of the association of MD with breast cancer risk and have implications for breast cancer prevention research in China.
| S-EPMC5802809 | biostudies-literature
Project description:Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD (r = 0.77-0.84) and Volpara VPD (r = 0.85-0.90) (P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.
| S-EPMC7325699 | biostudies-literature
Project description:Importance:Single-nucleotide polymorphisms (SNPs) have demonstrated an association with breast cancer susceptibility, but there is limited evidence on how to incorporate them into current breast cancer risk prediction models. Objective:To determine whether a panel of 18 SNPs (SNP18) may be used to predict breast cancer in combination with classic risk factors and mammographic density. Design, Setting, and Participants:This cohort study enrolled a subcohort of 9363 women, aged 46 to 73 years, without a previous breast cancer diagnosis from the larger prospective cohort of the PROCAS study (Predicting Risk of Cancer at Screening) specifically to evaluate breast cancer risk-assessment methods. Enrollment took place from October 2009 through June 2015 from multiple population-based screening centers in Greater Manchester, England. Follow-up continued through January 5, 2017. Exposures:Genotyping of 18 SNPs, visual-assessment percentage mammographic density, and classic risk assessed by the Tyrer-Cuzick risk model from a self-completed questionnaire at cohort entry. Main Outcomes and Measures:The predictive ability of SNP18 for breast cancer diagnosis (invasive and ductal carcinoma in situ) was assessed using logistic regression odds ratios per interquartile range of the predicted risk. Results:A total of 9363 women were enrolled in this study (mean [range] age, 59 [46-73] years). Of these, 466 were found to have breast cancer (271 prevalent; 195 incident). SNP18 was similarly predictive when unadjusted or adjusted for mammographic density and classic factors (odds ratios per interquartile range, respectively, 1.56; 95% CI, 1.38-1.77 and 1.53; 95% CI, 1.35-1.74), with observed risks being very close to expected (adjusted observed-to-expected odds ratio, 0.98; 95% CI, 0.69-1.28). A combined risk assessment indicated 18% of the subcohort to be at 5% or greater 10-year risk, compared with 30% of all cancers, 35% of interval-detected cancers, and 42% of stage 2+ cancers. In contrast, 33% of the subcohort were at less than 2% risk but accounted for only 18%, 17%, and 15% of the total, interval, and stage 2+ breast cancers, respectively. Conclusions and Relevance:SNP18 added substantial information to risk assessment based on the Tyrer-Cuzick model and mammographic density. A combined risk is likely to aid risk-stratified screening and prevention strategies.
| S-EPMC5885189 | biostudies-literature
Project description:BackgroundBenign breast disease and high breast density are prevalent, strong risk factors for breast cancer. Women with both risk factors may be at very high risk.MethodsWe included 42818 women participating in the Breast Cancer Surveillance Consortium who had no prior diagnosis of breast cancer and had undergone at least one benign breast biopsy and mammogram; 1359 women developed incident breast cancer in 6.1 years of follow-up (78.1% invasive, 21.9% ductal carcinoma in situ). We calculated hazard ratios (HRs) using Cox regression analysis. The referent group was women with nonproliferative changes and average density. All P values are two-sided.ResultsBenign breast disease and breast density were independently associated with breast cancer. The combination of atypical hyperplasia and very high density was uncommon (0.6% of biopsies) but was associated with the highest risk for breast cancer (HR = 5.34; 95% confidence interval [CI] = 3.52 to 8.09, P < .001). Proliferative disease without atypia (25.6% of biopsies) was associated with elevated risk that varied little across levels of density: average (HR = 1.37; 95% CI = 1.11 to 1.69, P = .003), high (HR = 2.02; 95% CI = 1.68 to 2.44, P < .001), or very high (HR = 2.05; 95% CI = 1.54 to 2.72, P < .001). Low breast density (4.5% of biopsies) was associated with low risk (HRs <1) for all benign pathology diagnoses.ConclusionsWomen with high breast density and proliferative benign breast disease are at very high risk for future breast cancer. Women with low breast density are at low risk, regardless of their benign pathologic diagnosis.
| S-EPMC3714021 | biostudies-literature
Project description:Mammographic density is one of the strongest predictors of breast cancer risk. Recently, it has been suggested that reactive oxygen species may influence breast cancer risk through its influence on mammographic density. In the current study, we addressed this hypothesis and also assessed if the association between carotenoids and breast cancer risk varies by mammographic density. We conducted a nested case-control study consisting of 604 breast cancer cases and 626 controls with prospectively measured circulating carotenoid levels and mammographic density in the Nurses' Health Study. Circulating levels of alpha-carotene, beta-carotene, beta-cryptoxanthin, lycopene, and lutein/zeaxanthin were measured. We used a computer-assisted thresholding method to measure percent mammographic density. We found no evidence that circulating carotenoids are inversely associated with mammographic density. However, mammographic density significantly modified the association between total circulating carotenoids and breast cancer (P heterogeneity = 0.008). Overall, circulating total carotenoids were inversely associated with breast cancer risk (P trend = 0.01). Among women in the highest tertile of mammographic density, total carotenoids were associated with a 50% reduction in breast cancer risk (odds ratio, 0.5; 95% confidence interval, 0.3-0.8). In contrast, there was no inverse association between carotenoids and breast cancer risk among women with low mammographic density. Similarly, among women in the highest tertile of mammographic density, high levels of circulating alpha-carotene, beta-cryptoxanthin, lycopene, and lutein/zeaxanthin were associated with a significant 40% to 50% reduction in breast cancer risk (P trend < 0.05). Our results suggest that plasma levels of carotenoids may play a role in reducing breast cancer risk, particularly among women with high mammographic density.
| S-EPMC2820729 | biostudies-literature
Project description:Aim: Nodal status remains one of the most important prognostic factors in breast cancer. The cellular and molecular reasons for the spread of tumor cells to the lymph nodes are not well understood and there are only few predictors in addition to tumor size and multifocality that give an insight into additional mechanisms of lymphatic spread. Aim of our study was therefore to investigate whether breast characteristics such as mammographic density (MD) add to the predictive value of the presence of lymph node metastases in patients with primary breast cancer. Methods: In this retrospective study we analyzed primary, metastasis-free breast cancer patients from one breast center for whom data on MD and staging information were available. A total of 1831 patients were included into this study. MD was assessed as percentage MD (PMD) using a semiautomated method and two readers for every patient. Multiple logistic regression analyses with nodal status as outcome were used to investigate the predictive value of PMD in addition to age, tumor size, Ki-67, estrogen receptor (ER), progesterone receptor (PR), grading, histology, and multi-focality. Results: Multifocality, tumor size, Ki-67 and grading were relevant predictors for nodal status. Adding PMD to a prediction model which included these factors did not significantly improve the prediction of nodal status (p = 0.24, likelihood ratio test). Conclusion: Nodal status could be predicted quite well with the factors multifocality, tumor size, Ki-67 and grading. PMD does not seem to play a role in the lymphatic spread of tumor cells. It could be concluded that the amount of extracellular matrix and stromal cell content of the breast which is reflected by MD does not influence the probability of malignant breast cells spreading from the primary tumor to the lymph nodes.
| S-EPMC3858989 | biostudies-other
Project description:Breast density and microcalcifications are strongly associated with the risk of breast cancer. However, few studies have evaluated the combined association between these two factors and breast cancer risk. We investigated the association between breast density, microcalcifications, and risk of breast cancer. This cohort study included 3,910,815 women aged 40-74 years who were screened for breast cancer between 2009 and 2010 and followed up until 2020. The National Health Insurance Service database includes national health-screening results from the national breast cancer screening program, which is an organized screening program provided every 2 years for all women aged 40 years or older. Breast density was assessed based on the Breast Imaging Reporting and Data System (BI-RADS) 4th edition, mostly through visual assessment by radiologists. The presence or absence of microcalcifications was obtained from the mammographic results. Cox proportional hazard regression for breast cancer risk was used to estimate hazard ratios (aHRs) adjusted for breast cancer risk factors. A total of 58,315 women developed breast cancer during a median follow-up of 10.8 years. Women with breast cancer had a higher proportion of microcalcifications than women without breast cancer (0.9% vs. 0.3%). After adjusting for breast density, women with microcalcification had a 3.07-fold (95% confidence interval [CI] 2.82-3.35) increased risk of breast cancer compared to women without microcalcification. The combined association between microcalcification and breast density dramatically increased the risk of breast cancer, corresponding to a higher level of breast density. Among postmenopausal women, the highest risk group was women with BI-RADS 4 and microcalcification. These women had more than a sevenfold higher risk than women with BI-RADS 1 and non-microcalcification (aHR, 7.26; 95% CI 5.01-10.53). Microcalcification is an independent risk factor for breast cancer, and its risk is elevated when combined with breast density.
| S-EPMC9773568 | biostudies-literature
Project description:BackgroundClinical use of breast cancer risk prediction requires simplified models. We evaluate a simplified version of the validated Rosner-Colditz model and add percent mammographic density (MD) and polygenic risk score (PRS), to assess performance from ages 45-74. We validate using the Mayo Mammography Health Study (MMHS).MethodsWe derived the model in the Nurses' Health Study (NHS) based on: MD, 77 SNP PRS and a questionnaire score (QS; lifestyle and reproductive factors). A total of 2,799 invasive breast cancer cases were diagnosed from 1990-2000. MD (using Cumulus software) and PRS were assessed in a nested case-control study. We assess model performance using this case-control dataset and evaluate 10-year absolute breast cancer risk. The prospective MMHS validation dataset includes 21.8% of women age <50, and 434 incident cases identified over 10 years of follow-up.ResultsIn the NHS, MD has the highest odds ratio (OR) for 10-year risk prediction: ORper SD = 1.48 [95% confidence interval (CI): 1.31-1.68], followed by PRS, ORper SD = 1.37 (95% CI: 1.21-1.55) and QS, ORper SD = 1.25 (95% CI: 1.11-1.41). In MMHS, the AUC adjusted for age + MD + QS 0.650; for age + MD + QS + PRS 0.687, and the NRI was 6% in cases and 16% in controls.ConclusionA simplified assessment of QS, MD, and PRS performs consistently to discriminate those at high 10-year breast cancer risk.ImpactThis simplified model provides accurate estimation of 10-year risk of invasive breast cancer that can be used in a clinical setting to identify women who may benefit from chemopreventive intervention.See related commentary by Tehranifar et al., p. 587.
| S-EPMC8026588 | biostudies-literature