C L Christiansen1, F Wang, M B Barton, W Kreuter, J G Elmore, A E Gelfand, S W Fletcher. 1. Boston University School of Public Health, Health Services Department, and Center for Health Quality, Outcomes and Economic Research at Veterans Affairs Health Services Research and Development, Boston, MA 01730, USA. cindylc@bu.edu
Abstract
BACKGROUND: The cumulative risk of a false-positive mammogram can be substantial. We studied which variables affect the chance of a false-positive mammogram and estimated cumulative risks over nine sequential mammograms. METHODS: We used medical records of 2227 randomly selected women who were 40-69 years of age on July 1, 1983, and had at least one screening mammogram. We used a Bayesian discrete hazard regression model developed for this study to test the effect of patient and radiologic variables on a first false-positive screening and to calculate cumulative risks of a false-positive mammogram. RESULTS: Of 9747 screening mammograms, 6. 5% were false-positive; 23.8% of women experienced at least one false-positive result. After nine mammograms, the risk of a false-positive mammogram was 43.1% (95% confidence interval [CI] = 36.6%-53.6%). Risk ratios decreased with increasing age and increased with number of breast biopsies, family history of breast cancer, estrogen use, time between screenings, no comparison with previous mammograms, and the radiologist's tendency to call mammograms abnormal. For a woman with highest-risk variables, the estimated risk for a false-positive mammogram at the first and by the ninth mammogram was 98.1% (95% CI = 69.3%-100%) and 100% (95% CI = 99.9%-100%), respectively. A woman with lowest-risk variables had estimated risks of 0.7% (95% CI = 0.2%-1.9%) and 4.6% (95% CI = 1. 1%-12.5%), respectively. CONCLUSIONS: The cumulative risk of a false-positive mammogram over time varies substantially, depending on a woman's own risk profile and on several factors related to radiologic screening. By the ninth mammogram, the risk can be as low as 5% for women with low-risk variables and as high as 100% for women with multiple high-risk factors.
BACKGROUND: The cumulative risk of a false-positive mammogram can be substantial. We studied which variables affect the chance of a false-positive mammogram and estimated cumulative risks over nine sequential mammograms. METHODS: We used medical records of 2227 randomly selected women who were 40-69 years of age on July 1, 1983, and had at least one screening mammogram. We used a Bayesian discrete hazard regression model developed for this study to test the effect of patient and radiologic variables on a first false-positive screening and to calculate cumulative risks of a false-positive mammogram. RESULTS: Of 9747 screening mammograms, 6. 5% were false-positive; 23.8% of women experienced at least one false-positive result. After nine mammograms, the risk of a false-positive mammogram was 43.1% (95% confidence interval [CI] = 36.6%-53.6%). Risk ratios decreased with increasing age and increased with number of breast biopsies, family history of breast cancer, estrogen use, time between screenings, no comparison with previous mammograms, and the radiologist's tendency to call mammograms abnormal. For a woman with highest-risk variables, the estimated risk for a false-positive mammogram at the first and by the ninth mammogram was 98.1% (95% CI = 69.3%-100%) and 100% (95% CI = 99.9%-100%), respectively. A woman with lowest-risk variables had estimated risks of 0.7% (95% CI = 0.2%-1.9%) and 4.6% (95% CI = 1. 1%-12.5%), respectively. CONCLUSIONS: The cumulative risk of a false-positive mammogram over time varies substantially, depending on a woman's own risk profile and on several factors related to radiologic screening. By the ninth mammogram, the risk can be as low as 5% for women with low-risk variables and as high as 100% for women with multiple high-risk factors.
Authors: Rebecca A Hubbard; Karla Kerlikowske; Chris I Flowers; Bonnie C Yankaskas; Weiwei Zhu; Diana L Miglioretti Journal: Ann Intern Med Date: 2011-10-18 Impact factor: 25.391
Authors: Paras Lakhani; Elliot D Menschik; Alberto F Goldszal; Joseph P Murray; Mark G Weiner; Curtis P Langlotz Journal: J Digit Imaging Date: 2006-03 Impact factor: 4.056
Authors: Joann G Elmore; Diana L Miglioretti; Lisa M Reisch; Mary B Barton; William Kreuter; Cindy L Christiansen; Suzanne W Fletcher Journal: J Natl Cancer Inst Date: 2002-09-18 Impact factor: 13.506