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Disease Incidence Algorithm (BOADICEA) model predicts breast cancer risk using cancer family history, epidemiological, and genetic data. We evaluated its validity in a large prospective cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We assessed model calibration, discrimination and risk classification ability in 217\u200a885 women (6838 incident breast cancers) aged 40-70\u2009years of self-reported White ethnicity with no previous cancer from the UK Biobank. Age-specific risk classification was assessed using relative risk thresholds equivalent to the absolute lifetime risk categories of less than 17%, 17%-30%, and 30% or more, recommended by the National Institute for Health and Care Excellence guidelines. We predicted 10-year risks using BOADICEA v.6 considering cancer family history, questionnaire-based risk factors, a 313\u2013single nucleotide polymorphisms polygenic score, and pathogenic variants. Mammographic density data were not available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The polygenic risk score was the most discriminative risk factor (area under the curve [AUC]\u2009=\u20090.65). Discrimination was highest when considering all risk factors (AUC\u2009=\u20090.66). The model was well calibrated overall (expected-to-observed ratio\u2009=\u20090.99, 95% confidence interval [CI]\u2009=\u20090.97 to 1.02; calibration slope\u2009=\u20090.99, 95% CI = 0.99 to 1.00), and in deciles of predicted risks. Discrimination was similar in women aged younger and older than 50\u2009years. There was some underprediction in women aged younger than 50 years (expected-to-observed ratio\u2009=\u20090.89, 95% CI\u2009=\u20090.84 to 0.94; calibration slope\u2009=\u20090.96, 95% CI = 0.94 to 0.97), which was explained by the higher breast cancer incidence in UK Biobank than the UK population incidence in this age group. The model classified 87.2%, 11.4%, and 1.4% of women in relative risk categories less than 1.6, 1.6-3.1, and at least 3.1, identifying 25.6% of incident breast cancer patients in category relative risk of at least\u20091.6.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>BOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year breast cancer risk, which can facilitate risk-stratified screening and personalized breast cancer risk management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jnci\/djae335","type":"journal-article","created":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T17:31:40Z","timestamp":1733938300000},"page":"948-958","source":"Crossref","is-referenced-by-count":21,"title":["Evaluating the performance of the Breast and Ovarian Analysis of Disease Incidence Algorithm model in predicting 10-year breast cancer risks in UK 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