Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts.
dc.contributor.author | Li, Kuanrong | |
dc.contributor.author | Anderson, Garnet | |
dc.contributor.author | Viallon, Vivian | |
dc.contributor.author | Arveux, Patrick | |
dc.contributor.author | Kvaskoff, Marina | |
dc.contributor.author | Fournier, Agnès | |
dc.contributor.author | Krogh, Vittorio | |
dc.contributor.author | Tumino, Rosario | |
dc.contributor.author | Sánchez, Maria-Jose | |
dc.contributor.author | Ardanaz, Eva | |
dc.contributor.author | Chirlaque, María-Dolores | |
dc.contributor.author | Agudo, Antonio | |
dc.contributor.author | Muller, David C | |
dc.contributor.author | Smith, Todd | |
dc.contributor.author | Tzoulaki, Ioanna | |
dc.contributor.author | Key, Timothy J | |
dc.contributor.author | Bueno-de-Mesquita, Bas | |
dc.contributor.author | Trichopoulou, Antonia | |
dc.contributor.author | Bamia, Christina | |
dc.contributor.author | Orfanos, Philippos | |
dc.contributor.author | Kaaks, Rudolf | |
dc.contributor.author | Hüsing, Anika | |
dc.contributor.author | Fortner, Renée T | |
dc.contributor.author | Zeleniuch-Jacquotte, Anne | |
dc.contributor.author | Sund, Malin | |
dc.contributor.author | Dahm, Christina C | |
dc.contributor.author | Overvad, Kim | |
dc.contributor.author | Aune, Dagfinn | |
dc.contributor.author | Weiderpass, Elisabete | |
dc.contributor.author | Romieu, Isabelle | |
dc.contributor.author | Riboli, Elio | |
dc.contributor.author | Gunter, Marc J | |
dc.contributor.author | Dossus, Laure | |
dc.contributor.author | Prentice, Ross | |
dc.contributor.author | Ferrari, Pietro | |
dc.date.accessioned | 2019-02-26T12:25:55Z | |
dc.date.available | 2019-02-26T12:25:55Z | |
dc.date.issued | 2018-12-03 | |
dc.identifier.issn | 1465-542X | |
dc.identifier.pmid | 30509329 | |
dc.identifier.doi | 10.1186/s13058-018-1073-0 | |
dc.identifier.uri | http://hdl.handle.net/10029/622847 | |
dc.description.abstract | Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. We built two models, for ER+ (Model Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for Model Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention. | en_US |
dc.language.iso | en | en_US |
dc.subject | Breast cancer | en_US |
dc.subject | EPIC | en_US |
dc.subject | Estrogen receptor | en_US |
dc.subject | Prospective cohort | en_US |
dc.subject | Risk prediction | en_US |
dc.subject | WHI | en_US |
dc.title | Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts. | en_US |
dc.type | Article | en_US |
dc.identifier.journal | Breast Cancer Res 2018; 20(1):147 | en_US |
dc.source.journaltitle | Breast cancer research : BCR |