guest ::
| Home > Publications database > Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. > print |
| Home > Publications database > Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. > print |
| 001 | 168129 | ||
| 005 | 20240229133558.0 | ||
| 024 | 7 | _ | |a 10.1093/ije/dyab036 |2 doi |
| 024 | 7 | _ | |a pmid:33755131 |2 pmid |
| 024 | 7 | _ | |a 0300-5771 |2 ISSN |
| 024 | 7 | _ | |a 1464-3685 |2 ISSN |
| 024 | 7 | _ | |a altmetric:102669986 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2021-00697 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Hurson, Amber N |b 0 |
| 245 | _ | _ | |a Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. |
| 260 | _ | _ | |a Oxford |c 2021 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1642519336_16197 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a Volume 50, Issue 6, December 2021, Pages 1897–1911 |
| 520 | _ | _ | |a Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines. |
| 536 | _ | _ | |a 313 - Krebsrisikofaktoren und Prävention (POF4-313) |0 G:(DE-HGF)POF4-313 |c POF4-313 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 650 | _ | 7 | |a Breast cancer |2 Other |
| 650 | _ | 7 | |a iCARE |2 Other |
| 650 | _ | 7 | |a model validation |2 Other |
| 650 | _ | 7 | |a polygenic risk score |2 Other |
| 650 | _ | 7 | |a risk prediction |2 Other |
| 650 | _ | 7 | |a risk stratification |2 Other |
| 700 | 1 | _ | |a Pal Choudhury, Parichoy |b 1 |
| 700 | 1 | _ | |a Gao, Chi |b 2 |
| 700 | 1 | _ | |a Hüsing, Anika |0 P:(DE-He78)6519c85d61a3def7974665471b8a4f74 |b 3 |
| 700 | 1 | _ | |a Eriksson, Mikael |b 4 |
| 700 | 1 | _ | |a Shi, Min |b 5 |
| 700 | 1 | _ | |a Jones, Michael E |b 6 |
| 700 | 1 | _ | |a Evans, D Gareth R |b 7 |
| 700 | 1 | _ | |a Milne, Roger L |b 8 |
| 700 | 1 | _ | |a Gaudet, Mia M |b 9 |
| 700 | 1 | _ | |a Vachon, Celine M |b 10 |
| 700 | 1 | _ | |a Chasman, Daniel I |b 11 |
| 700 | 1 | _ | |a Easton, Douglas F |b 12 |
| 700 | 1 | _ | |a Schmidt, Marjanka K |b 13 |
| 700 | 1 | _ | |a Kraft, Peter |b 14 |
| 700 | 1 | _ | |a Garcia-Closas, Montserrat |b 15 |
| 700 | 1 | _ | |a Chatterjee, Nilanjan |b 16 |
| 700 | 1 | _ | |a Group, B-CAST Risk Modelling |b 17 |e Collaboration Author |
| 773 | _ | _ | |a 10.1093/ije/dyab036 |g p. dyab036 |0 PERI:(DE-600)1494592-7 |n 6 |p 1897–1911 |t International journal of epidemiology |v 50 |y 2021 |x 1464-3685 |
| 909 | C | O | |p VDB |o oai:inrepo02.dkfz.de:168129 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 3 |6 P:(DE-He78)6519c85d61a3def7974665471b8a4f74 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF4-310 |0 G:(DE-HGF)POF4-313 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Krebsrisikofaktoren und Prävention |x 0 |
| 913 | 0 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF3-310 |0 G:(DE-HGF)POF3-313 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-300 |4 G:(DE-HGF)POF |v Cancer risk factors and prevention |x 0 |
| 914 | 1 | _ | |y 2021 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2021-02-04 |w ger |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b INT J EPIDEMIOL : 2019 |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2021-02-04 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2021-02-04 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-02-04 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2021-02-04 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b INT J EPIDEMIOL : 2019 |d 2021-02-04 |
| 920 | 1 | _ | |0 I:(DE-He78)C020-20160331 |k C020 |l C020 Epidemiologie von Krebs |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)C020-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|