{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T20:39:38Z","timestamp":1789504778383,"version":"build-2803163510"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Breast Cancer Res Treat"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Using nested case\u2013control data from the Nurses\u2019 Health Study, we compared the models\u2019 association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (<jats:italic>AUC<\/jats:italic>\u2009=\u20090.636 versus 0.511, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.0001) and for BRISK over IBIS for remaining lifetime risk (<jats:italic>AUC<\/jats:italic>\u2009=\u20090.647 versus 0.571, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected\/observed [<jats:italic>E\/O<\/jats:italic>]\u2009=\u20091.03; 95% CI 0.73, 1.46) and remaining lifetime risk (<jats:italic>E\/O<\/jats:italic>\u2009=\u20091.01; 95% CI 0.86, 1.17). The Gail 5-year risk (<jats:italic>E\/O<\/jats:italic>\u2009=\u20090.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (<jats:italic>E\/O<\/jats:italic>\u2009=\u20090.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (<jats:italic>NRI<\/jats:italic>\u2009=\u20090.31; standard error [<jats:italic>SE<\/jats:italic>]\u2009=\u20090.031) and IBIS remaining lifetime risk (<jats:italic>NRI<\/jats:italic>\u2009=\u20090.287; <jats:italic>SE<\/jats:italic>\u2009=\u20090.035).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10549-022-06834-7","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T19:18:25Z","timestamp":1675797505000},"page":"335-347","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk"],"prefix":"10.1007","volume":"198","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8635-5237","authenticated-orcid":false,"given":"Richard","family":"Allman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5660-440X","authenticated-orcid":false,"given":"Yi","family":"Mu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2448-2548","authenticated-orcid":false,"given":"Gillian S.","family":"Dite","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5397-3493","authenticated-orcid":false,"given":"Erika","family":"Spaeth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8567-173X","authenticated-orcid":false,"given":"John L.","family":"Hopper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6907-0056","authenticated-orcid":false,"given":"Bernard A.","family":"Rosner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"6834_CR1","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1016\/S0140-6736(01)06524-2","volume":"358","author":"Collaborative Group on Hormonal Factors in Breast Cancer","year":"2001","unstructured":"Collaborative Group on Hormonal Factors in Breast Cancer (2001) Familial breast cancer: collaborative reanalysis of individual data from 52 epidemiological studies including 58,209 women with breast cancer and 101,986 women without the disease. 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