CONTEXT: Models have been developed to predict the probability that a person carries a detectable germline mutation in the BRCA1 or BRCA2 genes. Their relative performance in a clinical setting is unclear. OBJECTIVE: To compare the performance characteristics of four BRCA1/BRCA2 gene mutation prediction models: LAMBDA, based on a checklist and scores developed from data on Ashkenazi Jewish (AJ) women; BRCAPRO, a Bayesian computer program; modified Couch tables based on regression analyses; and Myriad II tables collated by Myriad Genetics Laboratories. DESIGN AND SETTING: Family cancer history data were analyzed from 200 probands from the Mayo Clinic Familial Cancer Program, in a multispecialty tertiary care group practice. All probands had clinical testing for BRCA1 and BRCA2 mutations conducted in a single laboratory. MAIN OUTCOMES MEASURES: For each model, performance was assessed by the area under the receiver operator characteristic curve (ROC) and by tests of accuracy and dispersion. Cases "missed" by one or more models (model predicted less than 10% probability of mutation when a mutation was actually found) were compared across models. RESULTS: All models gave similar areas under the ROC curve of 0.71 to 0.76. All models except LAMBDA substantially under-predicted the numbers of carriers. All models were too dispersed. CONCLUSIONS: In terms of ranking, all prediction models performed reasonably well with similar performance characteristics. Model predictions were widely discrepant for some families. Review of cancer family histories by an experienced clinician continues to be vital to ensure that critical elements are not missed and that the most appropriate risk prediction figures are provided.
CONTEXT: Models have been developed to predict the probability that a person carries a detectable germline mutation in the BRCA1 or BRCA2 genes. Their relative performance in a clinical setting is unclear. OBJECTIVE: To compare the performance characteristics of four BRCA1/BRCA2 gene mutation prediction models: LAMBDA, based on a checklist and scores developed from data on Ashkenazi Jewish (AJ) women; BRCAPRO, a Bayesian computer program; modified Couch tables based on regression analyses; and Myriad II tables collated by Myriad Genetics Laboratories. DESIGN AND SETTING: Family cancer history data were analyzed from 200 probands from the Mayo Clinic Familial Cancer Program, in a multispecialty tertiary care group practice. All probands had clinical testing for BRCA1 and BRCA2 mutations conducted in a single laboratory. MAIN OUTCOMES MEASURES: For each model, performance was assessed by the area under the receiver operator characteristic curve (ROC) and by tests of accuracy and dispersion. Cases "missed" by one or more models (model predicted less than 10% probability of mutation when a mutation was actually found) were compared across models. RESULTS: All models gave similar areas under the ROC curve of 0.71 to 0.76. All models except LAMBDA substantially under-predicted the numbers of carriers. All models were too dispersed. CONCLUSIONS: In terms of ranking, all prediction models performed reasonably well with similar performance characteristics. Model predictions were widely discrepant for some families. Review of cancer family histories by an experienced clinician continues to be vital to ensure that critical elements are not missed and that the most appropriate risk prediction figures are provided.
Authors: Carlos H Barcenas; G M Monawar Hosain; Banu Arun; Jihong Zong; Xiaojun Zhou; Jianfang Chen; Jill M Cortada; Gordon B Mills; Gail E Tomlinson; Alexander R Miller; Louise C Strong; Christopher I Amos Journal: J Clin Oncol Date: 2006-01-20 Impact factor: 44.544
Authors: Fabiola Medeiros; Michael G Muto; Yonghee Lee; Julia A Elvin; Michael J Callahan; Colleen Feltmate; Judy E Garber; Daniel W Cramer; Christopher P Crum Journal: Am J Surg Pathol Date: 2006-02 Impact factor: 6.394
Authors: F J Couch; M L DeShano; M A Blackwood; K Calzone; J Stopfer; L Campeau; A Ganguly; T Rebbeck; B L Weber Journal: N Engl J Med Date: 1997-05-15 Impact factor: 91.245
Authors: Rita Nanda; L Philip Schumm; Shelly Cummings; James D Fackenthal; Lise Sveen; Foluso Ademuyiwa; Melody Cobleigh; Laura Esserman; Noralane M Lindor; Susan L Neuhausen; Olufunmilayo I Olopade Journal: JAMA Date: 2005-10-19 Impact factor: 56.272
Authors: Nicholas J Taylor; Nandita Mitra; Lu Qian; Marie-Françoise Avril; D Timothy Bishop; Brigitte Bressac-de Paillerets; William Bruno; Donato Calista; Francisco Cuellar; Anne E Cust; Florence Demenais; David E Elder; Anne-Marie Gerdes; Paola Ghiorzo; Alisa M Goldstein; Thais C Grazziotin; Nelleke A Gruis; Johan Hansson; Mark Harland; Nicholas K Hayward; Marko Hocevar; Veronica Höiom; Elizabeth A Holland; Christian Ingvar; Maria Teresa Landi; Gilles Landman; Alejandra Larre-Borges; Graham J Mann; Eduardo Nagore; Håkan Olsson; Jane M Palmer; Barbara Perić; Dace Pjanova; Antonia L Pritchard; Susana Puig; Helen Schmid; Nienke van der Stoep; Margaret A Tucker; Karin A W Wadt; Xiaohong R Yang; Julia A Newton-Bishop; Peter A Kanetsky Journal: J Am Acad Dermatol Date: 2019-02-05 Impact factor: 11.527
Authors: J J T van Harssel; C E P van Roozendaal; Y Detisch; R D Brandão; A D C Paulussen; M Zeegers; M J Blok; E B Gómez García Journal: Fam Cancer Date: 2010-06 Impact factor: 2.375