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
Sign in / Sign up

Export Citation Format

Share Document