Epidemiology

Breast Cancer Research and Treatment

, Volume 138, Issue 1, pp 249-259

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study

  • Roberto Pastor-BarriusoAffiliated withNational Center for Epidemiology, Carlos III Institute of HealthConsortium for Biomedical Research in Epidemiology and Public Health (CIBERESP) Email author 
  • , Nieves AscunceAffiliated withNavarre Breast Cancer Screening Program, Navarre Institute of Public HealthConsortium for Biomedical Research in Epidemiology and Public Health (CIBERESP)
  • , María EderraAffiliated withNavarre Breast Cancer Screening Program, Navarre Institute of Public HealthConsortium for Biomedical Research in Epidemiology and Public Health (CIBERESP)
  • , Nieves ErdozáinAffiliated withNavarre Breast Cancer Screening Program, Navarre Institute of Public HealthConsortium for Biomedical Research in Epidemiology and Public Health (CIBERESP)
  • , Alberto MurilloAffiliated withNavarre Breast Cancer Screening Program, Navarre Institute of Public Health
  • , José E. Alés-MartínezAffiliated withMedical Oncology Unit, Hospital Nuestra Señora de Sonsoles
  • , Marina PollánAffiliated withNational Center for Epidemiology, Carlos III Institute of HealthConsortium for Biomedical Research in Epidemiology and Public Health (CIBERESP)

Abstract

The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45–68 years who were free of breast cancer at the 1996–1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36–1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94–1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92–1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521–0.564) and 0.544 (95 % CI 0.523–0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors.

Keywords

Risk prediction model Invasive breast cancer Spanish cohort Calibration and discrimination accuracy Model recalibration Screening applications