scholarly journals Breast cancer risk prediction accuracy in Jewish Israeli high-risk women using the BOADICEA and IBIS risk models

2013 ◽  
Vol 95 (6) ◽  
pp. 174-177 ◽  
Author(s):  
YAEL LAITMAN ◽  
MONICA SIMEONOV ◽  
LITAL KEINAN-BOKER ◽  
IRENA LIPHSHITZ ◽  
EITAN FRIEDMAN

SummarySeveral breast cancer risk prediction models have been validated in ethnically diverse populations, but none in Israeli high-risk women. To validate the accuracy of the IBIS and BOADICEA risk prediction models in Israeli high-risk women, the 10-year and lifetime risk for developing breast cancer were calculated using both BOADICEA and IBIS models for high-risk, cancer-free women, counselled at the Sheba Medical Center from 1 June 1996–31 May 2000. Women diagnosed with breast cancer by 31 May 2011 were identified from the Israeli National Cancer Registry. The observed to expected breast cancer ratios were calculated to evaluate the predictive value of both algorithms. Overall, 358 mostly (N = 205, 57·2%) Ashkenazi women, were eligible, age range at counselling was 20–75 years (mean 46·76 ± 9·8 years). Over 13·6 ± 1·45 years (range 11–16 years), 15 women (4·19%) were diagnosed with breast cancer, at a mean age of 57 ± 8·6 years. The 10-year risks assigned by BOADICEA and IBIS ranged from 0·2 to 12·6% and 0·89 to 21·7%, respectively. The observed:expected breast cancer ratio was 15/18·6 (0·8–95% CI 0·48–1·33) and 15/28·6 (0·52–95% CI 0·32–0·87), using both models, respectively. In Jewish Israeli high-risk women the BOADICEA model has a better predictive value and accuracy in determining 10-year breast cancer risk than the IBIS model.

2017 ◽  
Vol 1 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Lance T. Pflieger ◽  
Clinton C. Mason ◽  
Julio C. Facelli

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.


2019 ◽  
Vol 121 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Javier Louro ◽  
Margarita Posso ◽  
Michele Hilton Boon ◽  
Marta Román ◽  
Laia Domingo ◽  
...  

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