© 2002 by Oxford University Press
Journal of the National Cancer Institute, Vol. 94, No. 11, 844-851,
June 5, 2002
© 2002 Oxford University Press
ARTICLE |
Pretest Prediction of BRCA1 or BRCA2 Mutation by Risk Counselors and the Computer Model BRCAPRO
Affiliations of authors: D. M. Euhus, K. C. Smith, L. Robinson, A. Stucky, G. Tomlinson, The University of Texas Southwestern Medical Center at Dallas; O. I. Olopade, S. Cummings, University of Chicago, Chicago, IL; J. E. Garber, A. Chittenden, Dana-Farber Cancer Institute, Boston, MA; G. B. Mills, P. Rieger, The University of Texas M. D. Anderson Cancer Center, Houston; L. Esserman, B. Crawford, University of California San Francisco Medical Center; K. S. Hughes, C. A. Roche, Massachusetts General Hospital, Boston; P. A. Ganz, J. Seldon, University of California Los Angeles Jonsson Comprehensive Cancer Center, Los Angeles; C. J. Fabian, J. Klemp, University of Kansas Medical Center, Kansas City.
Correspondence to: David M. Euhus, M.D., Division of Surgical Oncology, E6.222, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390–9155 (e-mail: david.euhus{at}UTSouthwestern.edu).
Background: Because BRCA gene mutation testing is costly, occasionally uninformative, and frequently associated with ethical and legal issues, careful patient selection is required prior to testing. Estimation of BRCA gene mutation probability is an important component of pretest counseling, but the accuracy of these estimates is currently unknown. We measured the performance of eight cancer risk counselors and of a computer model, BRCAPRO, at identifying families likely to carry a BRCA gene mutation. Methods: Eight cancer risk counselors and the computer model BRCAPRO estimated BRCA gene mutation probabilities for 148 pedigrees selected from an initial sample of 272 pedigrees. The final sample was limited to pedigrees with a proband affected by breast or ovarian cancer and BRCA1 and BRCA2 gene sequencing results unequivocally reported as negative or positive for a deleterious mutation. Sensitivity, specificity, negative predictive value, positive predictive value, and areas under receiver operator characteristics (ROC) curves were calculated for each risk counselor and for BRCAPRO. All statistical tests were two sided. Results: Using a greater-than-10% BRCA gene mutation probability threshold, the median sensitivity for identifying mutation carriers was 94% (range = 81% to 98%) for the eight risk counselors and 92% (range = 91% to 92%) for BRCAPRO. Median specificity at this threshold was 16% (range = 6% to 34%) for the risk counselors and 32% (range = 30% to 34%) for BRCAPRO (P = .04). Median area under the ROC curves was 0.671 for the risk counselors (range = 0.620 to 0.717) and 0.712 (range = 0.706 to 0.720) for BRCAPRO (P = .04). There was a slight, but not statistically significant, improvement in all counselor performance measures when BRCAPRO-assigned gene mutation probability information was included with the pedigrees. Conclusions: Sensitivity for identifying BRCA gene mutation carriers is similar for experienced risk counselors and the computer model BRCAPRO. Because the computer model consistently demonstrated superior specificity, overall discrimination between BRCA gene mutation carriers and BRCA gene mutation noncarriers was slightly better for BRCAPRO.
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