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MacInnis RJ, Knight JA, Chung WK, Milne RL, Whittemore AS, Buchsbaum R, Liao Y, Zeinomar N, Dite GS, Southey MC, Goldgar D, Giles GG, Kurian AW, Andrulis IL, John EM, Daly MB, Buys SS, Phillips KA, Hopper JL, Terry MB. Comparing 5-Year and Lifetime Risks of Breast Cancer using the Prospective Family Study Cohort. J Natl Cancer Inst 2020; 113:785-791. [PMID: 33301022 DOI: 10.1093/jnci/djaa178] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/06/2020] [Accepted: 10/13/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Clinical guidelines often use predicted lifetime risk from birth to define criteria for making decisions regarding breast cancer screening rather than thresholds based on absolute 5-year risk from current age. METHODS We used the Prospective Family Cohort Study of 14 657 women without breast cancer at baseline in which, during a median follow-up of 10 years, 482 women were diagnosed with invasive breast cancer. We examined the performances of the International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk models when using the alternative thresholds by comparing predictions based on 5-year risk with those based on lifetime risk from birth and remaining lifetime risk. All statistical tests were 2-sided. RESULTS Using IBIS, the areas under the receiver-operating characteristic curves were 0.66 (95% confidence interval = 0.63 to 0.68) and 0.56 (95% confidence interval = 0.54 to 0.59) for 5-year and lifetime risks, respectively (Pdiff < .001). For equivalent sensitivities, the 5-year incidence almost always had higher specificities than lifetime risk from birth. For women aged 20-39 years, 5-year risk performed better than lifetime risk from birth. For women aged 40 years or older, receiver-operating characteristic curves were similar for 5-year and lifetime IBIS risk from birth. Classifications based on remaining lifetime risk were inferior to 5-year risk estimates. Results were similar using BOADICEA. CONCLUSIONS Our analysis shows that risk stratification using clinical models will likely be more accurate when based on predicted 5-year risk compared with risks based on predicted lifetime and remaining lifetime, particularly for women aged 20-39 years.
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Affiliation(s)
- Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Wendy K Chung
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.,Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Alice S Whittemore
- Department of Health Research and Policy and of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Richard Buchsbaum
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Yuyan Liao
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Nur Zeinomar
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - David Goldgar
- Department of Dermatology and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.,Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Allison W Kurian
- Department of Medicine and Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | | | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada.,Department of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Esther M John
- Department of Epidemiology & Population Health and Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Saundra S Buys
- Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA
| | - Kelly-Anne Phillips
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA.,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
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