scholarly journals Symposium: Breast Cancer; Progress and Challenges in Prevention, Risk Prediction, Toumor Biology and Treatment

2013 ◽  
Vol 274 (2) ◽  
pp. 101-101
Keyword(s):  
Breast Cancer ◽  
Risk Prediction

scholarly journals Genomic Risk Prediction for Breast Cancer in Older Women

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3533
Author(s):  
Paul Lacaze ◽  
Andrew Bakshi ◽  
Moeen Riaz ◽  
Suzanne G. Orchard ◽  
Jane Tiller ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Older Women ◽  
Risk Prediction ◽  
Prediction Models ◽  
Cox Regression ◽  
Susceptibility Genes ◽  
Controlled Clinical Trial ◽  
Polygenic Risk Score ◽  
Double Blind ◽  
Genomic Risk

Genomic risk prediction models for breast cancer (BC) have been predominantly developed with data from women aged 40–69 years. Prospective studies of older women aged ≥70 years have been limited. We assessed the effect of a 313-variant polygenic risk score (PRS) for BC in 6339 older women aged ≥70 years (mean age 75 years) enrolled into the ASPREE trial, a randomized double-blind placebo-controlled clinical trial investigating the effect of daily 100 mg aspirin on disability-free survival. We evaluated incident BC diagnoses over a median follow-up time of 4.7 years. A multivariable Cox regression model including conventional BC risk factors was applied to prospective data, and re-evaluated after adding the PRS. We also assessed the association of rare pathogenic variants (PVs) in BC susceptibility genes (BRCA1/BRCA2/PALB2/CHEK2/ATM). The PRS, as a continuous variable, was an independent predictor of incident BC (hazard ratio (HR) per standard deviation (SD) = 1.4, 95% confidence interval (CI) 1.3–1.6) and hormone receptor (ER/PR)-positive disease (HR = 1.5 (CI 1.2–1.9)). Women in the top quintile of the PRS distribution had over two-fold higher risk of BC than women in the lowest quintile (HR = 2.2 (CI 1.2–3.9)). The concordance index of the model without the PRS was 0.62 (95% CI 0.56–0.68), which improved after addition of the PRS to 0.65 (95% CI 0.59–0.71). Among 41 (0.6%) carriers of PVs in BC susceptibility genes, we observed no incident BC diagnoses. Our study demonstrates that a PRS predicts incident BC risk in women aged 70 years and older, suggesting potential clinical utility extends to this older age group.

scholarly journals PCN152 Can Breast Cancer Risk Prediction Reduce the Risks of False Negative and False Positive Screening Mammograms?

2011 ◽  
Vol 14 (7) ◽  
pp. A462
Author(s):  
K. Armstrong ◽  
E. Handorf ◽  
J. Chen ◽  
M. Bristol-Demeter
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
False Positive ◽  
False Negative ◽  
Screening Mammograms

scholarly journals Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry

2015 ◽  
Vol 25 (2) ◽  
pp. 359-365 ◽  
Author(s):  
Gillian S. Dite ◽  
Robert J. MacInnis ◽  
Adrian Bickerstaffe ◽  
James G. Dowty ◽  
Richard Allman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Breast Cancer Family ◽  
Cancer Family ◽  
Clinical Models

An Intelligent Risk Prediction System for Breast Cancer Using Fuzzy Temporal Rules

2018 ◽  
Vol 42 (3) ◽  
pp. 227-232 ◽  
Author(s):  
U. Kanimozhi ◽  
S. Ganapathy ◽  
D. Manjula ◽  
A. Kannan
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Prediction System

scholarly journals European Breast Cancer Council manifesto 2018: Genetic risk prediction testing in breast cancer

2019 ◽  
Vol 106 ◽  
pp. 45-53 ◽  
Author(s):  
Emiel Rutgers ◽  
Judith Balmana ◽  
Marc Beishon ◽  
Karen Benn ◽  
D. Gareth Evans ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Genetic Risk Prediction ◽  
Cancer Council

Breast Cancer Risk Prediction via Area and Volumetric Estimates of Breast Density

2012 ◽  
pp. 236-243 ◽  
Author(s):  
Brad M. Keller ◽  
Emily F. Conant ◽  
Huen Oh ◽  
Despina Kontos
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Breast Density

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.

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