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dc.contributor.authorHüsing, Anika
dc.contributor.authorFortner, Renée T
dc.contributor.authorKühn, Tilman
dc.contributor.authorOvervad, Kim
dc.contributor.authorTjønneland, Anne
dc.contributor.authorOlsen, Anja
dc.contributor.authorBoutron-Ruault, Marie-Christine
dc.contributor.authorSeveri, Gianluca
dc.contributor.authorFournier, Agnes
dc.contributor.authorBoeing, Heiner
dc.contributor.authorTrichopoulou, Antonia
dc.contributor.authorBenetou, Vassiliki
dc.contributor.authorOrfanos, Philippos
dc.contributor.authorMasala, Giovanna
dc.contributor.authorPala, Valeria
dc.contributor.authorTumino, Rosario
dc.contributor.authorFasanelli, Francesca
dc.contributor.authorPanico, Salvatore
dc.contributor.authorBueno de Mesquita, H Bas
dc.contributor.authorPeeters, Petra H
dc.contributor.authorvan Gills, Carla H
dc.contributor.authorQuirós, J Ramón
dc.contributor.authorAgudo, Antonio
dc.contributor.authorSánchez, Maria-Jose
dc.contributor.authorChirlaque, Maria-Dolores
dc.contributor.authorBarricarte, Aurelio
dc.contributor.authorAmiano, Pilar
dc.contributor.authorKhaw, Kay-Tee
dc.contributor.authorTravis, Ruth C
dc.contributor.authorDossus, Laure
dc.contributor.authorLi, Kuanrong
dc.contributor.authorFerrari, Pietro
dc.contributor.authorMerritt, Melissa A
dc.contributor.authorTzoulaki, Ioanna
dc.contributor.authorRiboli, Elio
dc.contributor.authorKaaks, Rudolf
dc.date.accessioned2018-08-02T11:18:33Z
dc.date.available2018-08-02T11:18:33Z
dc.date.issued2017-08-01
dc.identifier.citationAdded Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort. 2017, 23 (15):4181-4189 Clin. Cancer Res.en
dc.identifier.issn1078-0432
dc.identifier.pmid28246273
dc.identifier.doi10.1158/1078-0432.CCR-16-3011
dc.identifier.urihttp://hdl.handle.net/10029/622086
dc.description.abstractPurpose: 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.
dc.language.isoenen
dc.rightsinfo:eu-repo/semantics/closedAccessen
dc.subject.meshAged
dc.subject.meshBiomarkers, Tumor
dc.subject.meshBreast Neoplasms
dc.subject.meshCase-Control Studies
dc.subject.meshEstradiol
dc.subject.meshFemale
dc.subject.meshGonadal Steroid Hormones
dc.subject.meshHumans
dc.subject.meshInsulin-Like Growth Factor Binding Protein 3
dc.subject.meshInsulin-Like Growth Factor I
dc.subject.meshMiddle Aged
dc.subject.meshPostmenopause
dc.subject.meshPremenopause
dc.subject.meshPrognosis
dc.subject.meshProlactin
dc.subject.meshRisk Factors
dc.subject.meshSex Hormone-Binding Globulin
dc.subject.meshTestosterone
dc.titleAdded Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort.en
dc.typeArticleen
dc.identifier.journalClin Cancer Res 2017; 23(15):4181-9en
html.description.abstractPurpose: 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.


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