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Showing content from https://pubmed.ncbi.nlm.nih.gov/14627668/ below:

Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme

. 2003 Nov;40(11):807-14. doi: 10.1136/jmg.40.11.807. Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme

Affiliations

Affiliation

¹ University of Manchester, UK.

PMID: 14627668
PMCID: PMC1735317
DOI: 10.1136/jmg.40.11.807

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Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme

E Amir et al. J Med Genet. 2003 Nov.

. 2003 Nov;40(11):807-14. doi: 10.1136/jmg.40.11.807. Affiliation

¹ University of Manchester, UK.

PMID: 14627668
PMCID: PMC1735317
DOI: 10.1136/jmg.40.11.807

Item in Clipboard

Abstract

Introduction: Accurate individualised breast cancer risk assessment is essential to provide risk-benefit analysis prior to initiating interventions designed to lower breast cancer risk. Several mathematical models for the estimation of individual breast cancer risk have been proposed. However, no single model integrates family history, hormonal factors, and benign breast disease in a comprehensive fashion. A new model by Tyrer and Cuzick has addressed these deficiencies. Therefore, this study has assessed the goodness of fit and discriminatory value of the Tyrer-Cuzick model against established models namely Gail, Claus, and Ford.

Methods: The goodness of fit and discriminatory accuracy of the models was assessed using data from 1933 women attending the Family History Evaluation and Screening Programme, of whom 52 developed cancer. All models were applied to these women over a mean follow up of 5.27 years to estimate risk of breast cancer.

Results: The ratios (95% confidence intervals) of expected to observed numbers of breast cancers were 0.48 (0.37 to 0.64) for Gail, 0.56 (0.43 to 0.75) for Claus, 0.49 (0.37 to 0.65) for Ford, and 0.81 (0.62 to 1.08) for Tyrer-Cuzick. The accuracy of the models for individual cases was evaluated using ROC curves. These showed that the area under the curve was 0.735 for Gail, 0.716 for Claus, 0.737 for Ford, and 0.762 for Tyrer-Cuzick.

Conclusion: The Tyrer-Cuzick model is the most consistently accurate model for prediction of breast cancer. The Gail, Claus, and Ford models all significantly underestimate risk, although the accuracy of the Claus model may be improved by adjustments for other risk factors.

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