Breast cancer risk assessment in patients seen in a gynecologic oncology clinic

Gail model for prediction of absolute risk of invasive breast cancer: independent evaluation in the Florence-European Prospective Investigation Into Cancer and Nutrition cohort

BACKGROUND

The Gail model 2 (GM) for predicting the absolute risk of invasive breast cancer has been used for counseling and to design intervention studies. Although the GM has been validated in US populations, its performance in other populations is unclear because of the wide variation in international breast cancer rates.

METHODS

We used data from a multicenter case-control study in Italy and from Italian cancer registries to develop a model (IT-GM) that uses the same risk factors as the GM. We evaluated the accuracy of the IT-GM and the GM using independent data from the Florence-European Prospective Investigation Into Cancer and Nutrition (EPIC) cohort. To assess model calibration (i.e., how well the model predicts the observed numbers of events in subsets of the population), we compared the number of expected incident breast cancers (E) predicted by these models with the number of observed incident breast cancers (O), and we computed the concordance statistic to measure discriminatory accuracy.

RESULTS

The overall E/O ratios were 0.96 (95% confidence interval [CI] = 0.84 to 1.11) and 0.93 (95% CI = 0.81 to 1.08) for the IT-GM and the GM, respectively. The IT-GM was somewhat better calibrated than GM in women younger than 50 years, but the GM was better calibrated when age at first live birth categories were considered (e.g., 20- to 24-year age-at-first-birth category E/O = 0.68, 95% CI = 0.53 to 0.94 for the IT-GM and E/O = 0.75, 95% CI = 0.58 to 1.03 for the GM). The concordance statistic was approximately 59% for both models, with 95% confidence intervals indicating that the models perform statistically significantly better than pure chance (concordance statistic of 50%).

CONCLUSIONS

There was no statistically significant evidence of miscalibration overall for either the IT-GM or the GM, and the models had equivalent discriminatory accuracy. The good performance of the IT-GM when applied on the independent data from the Florence-EPIC cohort indicates that GM can be improved for use in populations other than US populations. Our findings suggest that the Italian data may be useful for revising the GM to include additional risk factors for breast cancer.