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Breast cancer risk prediction: an update to the Rosner–Colditz breast cancer incidence model

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

To update and expand the Rosner–Colditz breast cancer incidence model by evaluating the contributions of more recently identified risk factors as well as predicted percent mammographic density (MD) to breast cancer risk.

Methods

Using data from the Nurses’ Health Study (NHS) and NHSII, we added adolescent somatotype (9 unit scale), vegetable intake (servings/day), breastfeeding (months), physical activity (MET-h/week), and predicted percent MD to the Rosner–Colditz model to determine whether these variables improved model discrimination. We evaluated all invasive as well as ER+/PR+, ER+/PR−, and ER−/PR− breast cancer.

Results

In the NHS/NHSII, we accrued over 5200 cases of invasive breast cancer over more than 20 years of follow-up with complete data on the risk factors. Adolescent somatotype and predicted percent MD significantly improved the original Rosner–Colditz model for all invasive breast cancer (change in age-adjusted AUC = 0.020, p < 0.001). The relative risk (RR) of invasive breast cancer for a 4-unit increase in adolescent somatotype was 0.62 (95% CI 0.56, 0.70), whereas the RR for a 20-unit increase in predicted percent MD was 1.32 (95% CI 1.28, 1.36). Adolescent somatotype and predicted percent MD also significantly improved the ER+/PR+model (change in age-adjusted AUC = 0.020, p < 0.001) as well as the ER+/PR− model (change in age-adjusted AUC = 0.012, p = 0.007). Adolescent somatotype, predicted percent MD, breastfeeding, and vegetable intake improved the ER−/PR− model (change in AUC = 0.031, p < 0.0001). The RR of ER−/PR− disease for 5 vegetable servings/day increase was 0.83 (95% CI 0.70, 0.99), while the RR for every 12 months of breastfeeding was 0.88 (95% CI 0.77, 1.01). Physical activity did not improve risk classification in any model.

Conclusion

Adolescent somatotype and predicted percent MD significantly improved breast cancer risk classification using the Rosner–Colditz model. Further, risk factors specific to ER− disease, such as breastfeeding and vegetable intake, may also help improve risk prediction of this aggressive subtype.

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Abbreviations

NHS:

Nurses’ Health Study

BMI:

Body mass index

BBD:

Benign breast disease

HT:

Hormone therapy

MD:

Mammographic density

ER:

Estrogen receptor

PR:

Progesterone receptor

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Acknowledgments

This study was supported by research Grants from the National Cancer Institute, National Institutes of Health, UM1 CA186107, P01 CA87969, UM1 CA176726, R01CA175080, and T32 CA09001. We would like to thank the participants of the Nurses’ Health Study and Nurses’ Health Study II for their continuing contributions. We thank the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

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Authors and Affiliations

  1. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Bartlett 9, Boston, MA, 02114, USA

    Megan S. Rice & Bernard Rosner

  2. Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Shelley S. Tworoger, Susan E. Hankinson, Rulla M. Tamimi, A. Heather Eliassen & Walter C. Willett

  3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Shelley S. Tworoger, Susan E. Hankinson, Rulla M. Tamimi, A. Heather Eliassen & Walter C. Willett

  4. Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA

    Susan E. Hankinson

  5. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Walter C. Willett

  6. Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA

    Graham Colditz

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  1. Megan S. Rice
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  2. Shelley S. Tworoger
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  3. Susan E. Hankinson
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  4. Rulla M. Tamimi
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  7. Graham Colditz
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  8. Bernard Rosner
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Correspondence to Megan S. Rice.

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Rice, M.S., Tworoger, S.S., Hankinson, S.E. et al. Breast cancer risk prediction: an update to the Rosner–Colditz breast cancer incidence model. Breast Cancer Res Treat 166, 227–240 (2017). https://doi.org/10.1007/s10549-017-4391-5

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  • DOI: https://doi.org/10.1007/s10549-017-4391-5

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