Performance of breast cancer risk assessment models in a large mammography cohort
Menée à partir de données portant sur 35 921 femmes ayant subi une mammographie de dépistage (âge : de 40 à 84 ans), cette étude compare la performance de cinq modèles pour prédire le risque de cancer du sein
Background : Several breast cancer risk assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations.
Methods : We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC) and Tyrer-Cuzick models in predicting risk of breast cancer over six years among 35,921 women aged 40-84 who underwent mammography screening at Newton-Wellesley Hospital from 2007-2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier Score and positive and negative predictive values of each model.
Results : Our results confirmed good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E=0.98, 95% CI:0.91-1.06, AUC=0.64, 95% CI:0.61-0.65) than BRCAPRO (O/E=0.94, 95% CI:0.88-1.02, AUC=0.61, 95% CI:0.59-0.63) and Tyrer-Cuzick (version 8, O/E=0.84, 95% CI:0.79-0.91, AUC=0.62, 95% CI:0.60-0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E=0.97, 95% CI:0.89-1.05, AUC=0.64, 95% CI:0.62-0.66). All models had poorer predictive accuracy for HER2+ and triple negative breast cancers than hormone receptor positive HER2- breast cancers.
Conclusions : In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and non-genetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.
Journal of the National Cancer Institute , article en libre accès, 2018