{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T01:12:28Z","timestamp":1791335548973,"version":"4.3.1"},"reference-count":38,"publisher":"BMJ","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Genet"],"accepted":{"date-parts":[[2013,2,21]]},"published-print":{"date-parts":[[2013,6]]},"abstract":"<jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Risk prediction models are widely used in clinical genetic counselling. Despite their frequent use, the genetic risk models BOADICEA, BRCAPRO, IBIS and extended Claus model (eCLAUS), used to estimate <jats:italic>BRCA1\/2<\/jats:italic> mutation carrier probabilities, have never been comparatively evaluated in a large sample from central Europe. Additionally, a novel version of BOADICEA that incorporates tumour pathology information has not yet been validated.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Patients and methods<\/jats:title>\n                  <jats:p>Using data from 7352 German families we estimated <jats:italic>BRCA1\/2<\/jats:italic> carrier probabilities under each model and compared their discrimination and calibration. The incremental value of using pathology information in BOADICEA was assessed in a subsample of 4928 pedigrees with available data on breast tumour molecular markers oestrogen receptor, progesterone receptor and human epidermal growth factor 2.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>BRCAPRO (area under receiver operating characteristic curve (AUC)=0.80 (95% CI 0.78 to 0.81)) and BOADICEA (AUC=0.79 (0.78\u20130.80)), had significantly higher diagnostic accuracy than IBIS and eCLAUS (p&lt;0.001). The AUC increased when pathology information was used in BOADICEA: AUC=0.81 (95% CI 0.80 to 0.83, p&lt;0.001). At carrier thresholds of 10% and 15%, the net reclassification index was +3.9% and +5.4%, respectively, when pathology was included in the model. Overall, calibration was best for BOADICEA and worst for eCLAUS. With eCLAUS, twice as many mutation carriers were predicted than observed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Our results support the use of BRCAPRO and BOADICEA for decision making regarding genetic testing for <jats:italic>BRCA1\/2<\/jats:italic> mutations. However, model calibration has to be improved for this population. eCLAUS should not be used for estimating mutation carrier probabilities in clinical settings. Whenever possible, breast tumour molecular marker information should be taken into account.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/jmedgenet-2012-101415","type":"journal-article","created":{"date-parts":[[2013,4,6]],"date-time":"2013-04-06T20:30:02Z","timestamp":1365280202000},"page":"360-367","source":"Crossref","is-referenced-by-count":85,"title":["Evaluating the performance of the breast cancer genetic risk models BOADICEA, IBIS, BRCAPRO and Claus for predicting <i>BRCA1\/2<\/i> mutation carrier probabilities: a study based on 7352 families from the German Hereditary Breast and Ovarian Cancer Consortium"],"prefix":"10.1136","volume":"50","author":[{"given":"Christine","family":"Fischer","sequence":"first","affiliation":[{"name":"Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karoline","family":"Kuchenb\u00e4cker","sequence":"additional","affiliation":[{"name":"Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph","family":"Engel","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silke","family":"Zachariae","sequence":"additional","affiliation":[{"name":"Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerstin","family":"Rhiem","sequence":"additional","affiliation":[{"name":"Department of Gynaecology and Obstetrics, Center for Familiar Breast and Ovarian Cancer, University Hospital of Cologne, K\u00f6ln, 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