Comparing 5-Year and Lifetime Risks of Breast Cancer using the Prospective Family Study Cohort

Author:

MacInnis Robert J12ORCID,Knight Julia A34,Chung Wendy K56,Milne Roger L127ORCID,Whittemore Alice S8,Buchsbaum Richard9,Liao Yuyan10,Zeinomar Nur10,Dite Gillian S2ORCID,Southey Melissa C1711,Goldgar David12,Giles Graham G1213,Kurian Allison W14,Andrulis Irene L315,John Esther M16,Daly Mary B17,Buys Saundra S18,Phillips Kelly-Anne21920ORCID,Hopper John L2,Terry Mary Beth510,

Affiliation:

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

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.

Funder

US National Institute of Health

The Australian Breast Cancer Family Registry

Australian National Health

Medical Research Council

New South Wales Cancer Council

Victorian Health Promotion Foundation

Victorian Breast Cancer Research Consortium

Cancer Australia

National Breast Cancer Foundation

Breast Cancer Family Registry

US National Cancer Institute

Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer

Australian National Breast Cancer Foundation

National Health and Medical Research Council

Queensland Cancer Fund

Cancer Councils of New South Wales

Cancer Foundation of Western Australia

Breast Cancer Research Foundation

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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