scholarly journals Breast cancer specialists’ views on and use of risk prediction models in clinical practice: A mixed methods approach

2014 ◽  
Vol 54 (3) ◽  
pp. 361-367 ◽  
Author(s):  
Ellen G. Engelhardt ◽  
Arwen H. Pieterse ◽  
Nanny van Duijn-Bakker ◽  
Judith R. Kroep ◽  
Hanneke C. J. M. de Haes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Practice ◽  
Mixed Methods ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Mixed Methods Approach ◽  
Cancer Specialists

Utilization of breast cancer risk prediction models by cancer genetic counselors in clinical practice predominantly in the United States

2021 ◽  
Author(s):  
Min Seon Park ◽  
Scott M. Weissman ◽  
Kristen J. Vogel Postula ◽  
Carmen S. Williams ◽  
Caitlin B. Mauer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
United States ◽  
Clinical Practice ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
The United States ◽  
Cancer Genetic ◽  
Risk Prediction Models

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.

Abstract 268: Absolute breast cancer risk according to three risk prediction models: Inverse associations with risk of death and poor prognostic features

2014 ◽  
Author(s):  
Mark E. Sherman ◽  
Laura Ichikawa ◽  
Diana Miglioretti ◽  
Pamela Vacek ◽  
Jeffrey Tice ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Prognostic Features ◽  
Risk Of Death

Abstract 2600: Improving breast cancer risk prediction models: the addition of a genetic risk score, mammographic density, and endogenous hormones

2016 ◽  
Author(s):  
Xuehong Zhang ◽  
Megan Rice ◽  
Shelley S. Tworoger ◽  
Bernard A. Rosner ◽  
A. Heather Eliassen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Mammographic Density ◽  
Genetic Risk ◽  
Prediction Models ◽  
Genetic Risk Score ◽  
Endogenous Hormones ◽  
Risk Prediction Models

Abstract P4-08-28: Clinical risk prediction models for breast cancer: A review of models developed between 2010 and 2018

2019 ◽  
Author(s):  
S Siesling ◽  
T Hueting ◽  
B Tip ◽  
R Mentink ◽  
E Koffijberg
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Clinical Risk

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.

Sign in / Sign up

Export Citation Format

Share Document