scholarly journals Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort

2017 ◽  
Vol 23 (15) ◽  
pp. 4181-4189 ◽  
Author(s):  
Anika Hüsing ◽  
Renée T. Fortner ◽  
Tilman Kühn ◽  
Kim Overvad ◽  
Anne Tjønneland ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Added Value ◽  
Risk Prediction Models ◽  
Exogenous Hormones ◽  
Serum Hormone

A Validated Risk Prediction Model for Breast Cancer in US Black Women

2021 ◽  
Author(s):  
Julie R. Palmer ◽  
Gary Zirpoli ◽  
Kimberly A. Bertrand ◽  
Tracy Battaglia ◽  
Leslie Bernstein ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Black Women ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Model ◽  
Risk Prediction Models ◽  
Relative Risks ◽  
Discriminatory Accuracy

PURPOSE Breast cancer risk prediction models are used to identify high-risk women for early detection, targeted interventions, and enrollment into prevention trials. We sought to develop and evaluate a risk prediction model for breast cancer in US Black women, suitable for use in primary care settings. METHODS Breast cancer relative risks and attributable risks were estimated using data from Black women in three US population-based case-control studies (3,468 breast cancer cases; 3,578 controls age 30-69 years) and combined with SEER age- and race-specific incidence rates, with incorporation of competing mortality, to develop an absolute risk model. The model was validated in prospective data among 51,798 participants of the Black Women's Health Study, including 1,515 who developed invasive breast cancer. A second risk prediction model was developed on the basis of estrogen receptor (ER)–specific relative risks and attributable risks. Model performance was assessed by calibration (expected/observed cases) and discriminatory accuracy (C-statistic). RESULTS The expected/observed ratio was 1.01 (95% CI, 0.95 to 1.07). Age-adjusted C-statistics were 0.58 (95% CI, 0.56 to 0.59) overall and 0.63 (95% CI, 0.58 to 0.68) among women younger than 40 years. These measures were almost identical in the model based on estrogen receptor–specific relative risks and attributable risks. CONCLUSION Discriminatory accuracy of the new model was similar to that of the most frequently used questionnaire-based breast cancer risk prediction models in White women, suggesting that effective risk stratification for Black women is now possible. This model may be especially valuable for risk stratification of young Black women, who are below the ages at which breast cancer screening is typically begun.

P1547Circulating lipoprotein subfractions as new non-invasive biomarkers of coronary atherosclerosis

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J Saether ◽  
E Madssen ◽  
E Vesterbekkmo ◽  
G Giskeodegaard ◽  
S Gjaere ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Coronary Atherosclerosis ◽  
Prediction Models ◽  
Statistical Tests ◽  
Added Value ◽  
Gensini Score ◽  
Lipoprotein Subfractions ◽  
Risk Prediction Models ◽  
Blood Samples ◽  
Ldl Particles

Abstract Objectives Coronary artery disease (CAD) is the most common cause of death globally. In the next decade, the number of people at risk is expected to increase, due to obesity, inactivity and diabetes. Therefore, precise risk-prediction models will be increasingly important for the healthcare system, to be able to initiate cost-efficient prevention strategies. One of the first steps in CAD-development is sub-clinical atherosclerosis. Biomarkers that could reflect the presence of coronary atherosclerosis would be extremely valuable for risk prediction of myocardial infarction (MI). Serum cholesterol levels are key variables in risk prediction; however, there is growing interest for exploring the potential of other lipid subclasses. The aim of this study is to identify specific lipoprotein subfractions that are associated with the extent of coronary atherosclerosis. Methods 60 patients with suspected CAD were enrolled. Blood samples were collected before the partiens underwent coronary angiography. The extent of coronary atherosclerosis were quantified using the Gensini score. The partients were classified into three groups based on their Gensini score (<20.5: normal, 20.6–30: non-significant CAD and >30.1: significant CAD). The blood samples were analyzed by nucelar magnetic resonance (NMR) lipidomics. Univariate and multivariate statistical tests were used to determine whether lipoprotein subfractions were associated with the extent of coronary atherosclerosis. Results and discussion Of the 117 lipoprotein subfractions quantified, 10 were different in patients with significant CAD compared to patients with normal vessels in non-statin users (p=0.005). Despite no difference in total cholesterol, LDL and HDL cholesterol between the three Gensini groups, NMR lipidomics revealed that patients with significant CAD had twice as many circulating LDL-5 and LDL-6 particles as patients with normal vessels. Furthermore, three types of small LDL-subfractions, called LDL-5-TG, LDL-5-ApoB and LDL-6-ApoB, were significantly increased in patients with significant CAD. Interestingly, previous studies have suggested that small LDL particles are more atherogenic than larger particles. In addition, patients with significant CAD had low levels of ApoA1 containing HDL particles, and high levels of two different small VLDL particles. Previous studies have indicated that small VLDLs are more atherogenic than larger VLDLs, and does to a greater extent penetrate the vessel intima. Conclusions This study reveals strong associations between serum lipoprotein subfractions and the degree of coronary atherosclerosis quantified by Gensini score. Especially, the high levels of certain types of small LDL-particles in patients with CAD, indicates that measuring lipoprotein subfractions may provide added value to risk prediction models for MI. However, these findings needs to be further explored and validated in large cohort studies. Acknowledgement/Funding Norwegian Health Association, the Liaison Committee between the Central Norway Regional Health Authority (RHA) and NTNU

scholarly journals Breast Cancer Risks and Risk Prediction Models

Breast Care ◽  
2015 ◽  
Vol 10 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Christoph Engel ◽  
Christine Fischer
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
At Risk ◽  
Risk Prediction ◽  
Clinical Management ◽  
Prediction Models ◽  
Cancer Risks ◽  
Large Variability ◽  
Risk Prediction Models ◽  
Mutation Carriers

BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

scholarly journals A Review on Automatic Mammographic Density and Parenchymal Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-31 ◽  
Author(s):  
Wenda He ◽  
Arne Juette ◽  
Erika R. E. Denton ◽  
Arnau Oliver ◽  
Robert Martí ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Risk Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Prevention Measures ◽  
Clinical Environment ◽  
Tissue Segmentation ◽  
Risk Prediction Models ◽  
The Impact

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

Risk prediction models for familial breast cancer

2006 ◽  
Vol 2 (2) ◽  
pp. 257-274 ◽  
Author(s):  
Antonis C Antoniou ◽  
Douglas F Easton
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Familial Breast Cancer ◽  
Prediction Models ◽  
Risk Prediction Models
Sign in / Sign up

Export Citation Format

Share Document