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

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

Validating Breast Cancer Risk Prediction Models in the Korean Cancer Prevention Study-II Biobank

2020 ◽  
Vol 29 (6) ◽  
pp. 1271-1277 ◽  
Author(s):  
Yon Ho Jee ◽  
Chi Gao ◽  
Jihye Kim ◽  
Seho Park ◽  
Sun Ha Jee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Prevention ◽  
Risk Prediction ◽  
Prediction Models ◽  
Prevention Study ◽  
Risk Prediction Models

scholarly journals Uncertainty quantification in breast cancer risk prediction models using self-reported family health history

2017 ◽  
Vol 1 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Lance T. Pflieger ◽  
Clinton C. Mason ◽  
Julio C. Facelli
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Clinical Setting ◽  
Prediction Models ◽  
Family Health ◽  
Clinical Settings ◽  
Risk Prediction Models ◽  
Health History

Introduction. Family health history (FHx) is an important factor in breast and ovarian cancer risk assessment. As such, multiple risk prediction models rely strongly on FHx data when identifying a patient’s risk. These models were developed using verified information and when translated into a clinical setting assume that a patient’s FHx is accurate and complete. However, FHx information collected in a typical clinical setting is known to be imprecise and it is not well understood how this uncertainty may affect predictions in clinical settings. Methods. Using Monte Carlo simulations and existing measurements of uncertainty of self-reported FHx, we show how uncertainty in FHx information can alter risk classification when used in typical clinical settings. Results. We found that various models ranged from 52% to 64% for correct tier-level classification of pedigrees under a set of contrived uncertain conditions, but that significant misclassification are not negligible. Conclusions. Our work implies that (i) uncertainty quantification needs to be considered when transferring tools from a controlled research environment to a more uncertain environment (i.e, a health clinic) and (ii) better FHx collection methods are needed to reduce uncertainty in breast cancer risk prediction in clinical settings.

scholarly journals A systematic review and quality assessment of individualised breast cancer risk prediction models

2019 ◽  
Vol 121 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Javier Louro ◽  
Margarita Posso ◽  
Michele Hilton Boon ◽  
Marta Román ◽  
Laia Domingo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Quality Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Legal Liability and the Uncertain Nature of Risk Prediction: The Case of Breast Cancer Risk Prediction Models

2012 ◽  
Vol 15 (6) ◽  
pp. 335-340 ◽  
Author(s):  
L. Black ◽  
B.M. Knoppers ◽  
D. Avard ◽  
J. Simard
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Legal Liability ◽  
Risk Prediction Models

The impact of patient age on breast cancer risk prediction models

2018 ◽  
Vol 24 (4) ◽  
pp. 592-598 ◽  
Author(s):  
Suzanne B. Coopey ◽  
Ahmet Acar ◽  
Molly Griffin ◽  
Jessica Cintolo-Gonzalez ◽  
Alan Semine ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Patient Age ◽  
Risk Prediction Models ◽  
Patient Âgé ◽  
The Impact
Sign in / Sign up

Export Citation Format

Share Document