Robert J MacInnis1,2, Julia A Knight3,4, Wendy K Chung5,6, Roger L Milne1,2,7, Alice S Whittemore8, Richard Buchsbaum9, Yuyan Liao10, Nur Zeinomar10, Gillian S Dite2, Melissa C Southey1,7,11, David Goldgar12, Graham G Giles1,2,13, Allison W Kurian14, Irene L Andrulis3,15, Esther M John16, Mary B Daly17, Saundra S Buys18, Kelly-Anne Phillips2,19,20, John L Hopper2, Mary Beth Terry5,10. 1. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia. 2. Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia. 3. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada. 4. Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. 5. Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA. 6. Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA. 7. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia. 8. Department of Health Research and Policy and of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA. 9. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA. 10. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA. 11. Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia. 12. Department of Dermatology and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA. 13. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia. 14. Department of Medicine and Epidemiology and Population Health, Stanford University, Stanford, CA, USA. 15. Department of Molecular Genetics and Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada. 16. Department of Epidemiology & Population Health and Medicine and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA. 17. Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA. 18. Department of Medicine and Huntsman Cancer Institute, University of Utah Health, Salt Lake City, UT, USA. 19. Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia. 20. Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
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.
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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