RT Journal Article
T1 Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort.
A1 Hüsing, Anika
A1 Fortner, Renée T
A1 Kühn, Tilman
A1 Overvad, Kim
A1 Tjønneland, Anne
A1 Olsen, Anja
A1 Boutron-Ruault, Marie-Christine
A1 Severi, Gianluca
A1 Fournier, Agnes
A1 Boeing, Heiner
A1 Trichopoulou, Antonia
A1 Benetou, Vassiliki
A1 Orfanos, Philippos
A1 Masala, Giovanna
A1 Pala, Valeria
A1 Tumino, Rosario
A1 Fasanelli, Francesca
A1 Panico, Salvatore
A1 Bueno de Mesquita, H Bas
A1 Peeters, Petra H
A1 van Gills, Carla H
A1 Quirós, J Ramón
A1 Agudo, Antonio
A1 Sanchez-Perez, Maria-Jose
A1 Chirlaque, Maria-Dolores
A1 Barricarte, Aurelio
A1 Amiano, Pilar
A1 Khaw, Kay-Tee
A1 Travis, Ruth C
A1 Dossus, Laure
A1 Li, Kuanrong
A1 Ferrari, Pietro
A1 Merritt, Melissa A
A1 Tzoulaki, Ioanna
A1 Riboli, Elio
A1 Kaaks, Rudolf
AB Purpose: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.Experimental Design: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case-control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone-binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting.Results: Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor-positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.Conclusions: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification. Clin Cancer Res; 23(15); 4181-9. ©2017 AACR.
YR 2017
FD 2017-02-28
LK http://hdl.handle.net/10668/10916
UL http://hdl.handle.net/10668/10916
LA en
DS RISalud
RD Aug 8, 2025