Skip to main content

Advertisement

SpringerLink
Log in

Validation of the breast cancer surveillance consortium model of breast cancer risk

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

Abstract

Purpose

In order to use a breast cancer prediction model in clinical practice to guide screening and prevention, it must be well calibrated and validated in samples independent from the one used for development. We assessed the accuracy of the breast cancer surveillance consortium (BCSC) model in a racially diverse population followed for up to 10 years.

Methods

The BCSC model combines breast density with other risk factors to estimate a woman’s 5- and 10-year risk of invasive breast cancer. We validated the model in an independent cohort of 252,997 women in the Chicago area. We evaluated calibration using the ratio of expected to observed (E/O) invasive breast cancers in the cohort and discrimination using the area under the receiver operating characteristic curve (AUROC).

Results

In an independent cohort of 252,997 women (median age 50 years, 26% non-Hispanic Black), the BCSC model was well calibrated (E/O = 0.94, 95% confidence interval [CI] 0.90–0.98), but underestimated the incidence of invasive breast cancer in younger women and in women with low mammographic density. The AUROC was 0.633, similar to that observed in prior validation studies.

Conclusions

The BCSC model is a well-validated risk assessment tool for breast cancer that may be particularly useful when assessing the utility of supplemental screening in women with dense breasts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Data availability

The data that support the findings of this study are available from the Breast Cancer Surveillance Consortium, but restrictions apply to the availability of these data, which were used under a study agreement for this analysis, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the BCSC. Details about the BCSC data are available at http://www.bcsc-research.org/data/index.html.

References

  1. Gail MH, Pfeiffer RM (2018) Breast cancer risk model requirements for counseling, prevention, and screening. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djy013

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, Mulvihill JJ (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81(24):1879–1886

    Article  CAS  PubMed  Google Scholar 

  3. Rockhill B, Spiegelman D, Byrne C, Hunter DJ, Colditz GA (2001) Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. J Natl Cancer Inst 93(5):358–366

    Article  CAS  PubMed  Google Scholar 

  4. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K (2008) Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med 148(5):337–347

    Article  PubMed  PubMed Central  Google Scholar 

  5. Vachon CM, Pankratz VS, Scott CG, Haeberle L, Ziv E, Jensen MR, Brandt KR, Whaley DH, Olson JE, Heusinger K, Hack CC, Jud SM, Beckmann MW, Schulz-Wendtland R, Tice JA, Norman AD, Cunningham JM, Purrington KS, Easton DF, Sellers TA, Kerlikowske K, Fasching PA, Couch FJ (2015) The contributions of breast density and common genetic variation to breast cancer risk. J Natl Cancer Inst 107 (5). https://doi.org/10.1093/jnci/dju397

  6. Rauscher GH, Dabbous F, Dolecek TA, Friedewald SM, Tossas-Milligan K, Macarol T, Summerfelt WT (2017) Absence of an anticipated racial disparity in interval breast cancer within a large health care organization. Ann Epidemiol 27(10):654–658. https://doi.org/10.1016/j.annepidem.2017.09.002

    Article  PubMed  PubMed Central  Google Scholar 

  7. Tice JA, O’Meara ES, Weaver DL, Vachon C, Ballard-Barbash R, Kerlikowske K (2013) Benign breast disease, mammographic breast density, and the risk of breast cancer. J Natl Cancer Inst 105(14):1043–1049. https://doi.org/10.1093/jnci/djt124

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Viallon V, Ragusa S, Clavel-Chapelon F, Benichou J (2009) How to evaluate the calibration of a disease risk prediction tool. Stat Med 28(6):901–916. https://doi.org/10.1002/sim.3517

    Article  PubMed  Google Scholar 

  9. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56(2):337–344

    Article  CAS  Google Scholar 

  10. Tice JA, Miglioretti DL, Li CS, Vachon CM, Gard CC, Kerlikowske K (2015) Breast density and benign breast disease: risk assessment to identify women at high risk of breast cancer. J Clin Oncol 33(28):3137–3143. https://doi.org/10.1200/JCO.2015.60.8869

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bondy ML, Lustbader ED, Halabi S, Ross E, Vogel VG (1994) Validation of a breast cancer risk assessment model in women with a positive family history. J Natl Cancer Inst 86(8):620–625

    Article  CAS  PubMed  Google Scholar 

  12. Costantino JP, Gail MH, Pee D, Anderson S, Redmond CK, Benichou J, Wieand HS (1999) Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst 91(18):1541–1548

    Article  CAS  PubMed  Google Scholar 

  13. Boughey JC, Hartmann LC, Anderson SS, Degnim AC, Vierkant RA, Reynolds CA, Frost MH, Pankratz VS (2010) Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) model for breast cancer risk prediction in women with atypical hyperplasia. J Clin Oncol 28(22):3591–3596. https://doi.org/10.1200/JCO.2010.28.0784

    Article  PubMed  PubMed Central  Google Scholar 

  14. Brentnall AR, Cuzick J, Buist DSM, Bowles EJA (2018) Long-term accuracy of breast cancer risk assessment combining classic risk factors and breast density. JAMA Oncol. https://doi.org/10.1001/jamaoncol.2018.0174

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kerlikowske K, Zhu W, Tosteson AN, Sprague BL, Tice JA, Lehman CD, Miglioretti DL, Breast Cancer Surveillance C (2015) Identifying women with dense breasts at high risk for interval cancer: a cohort study. Ann Intern Med 162(10):673–681. https://doi.org/10.7326/M14-1465

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This research was funded by the Breast Cancer Surveillance Consortium program project (P01CA154292). Data collection for this work was additionally supported, in part, by funding from the National Cancer Institute (U54CA163303) and the Agency for Health Research and Quality (R01 HS018366-01A1). The collection of cancer and vital status data used in this study was supported in part by several state public health departments and cancer registries throughout the U.S. For a full description of these sources, please see: http://www.bcsc-research.org/work/acknowledgement.html. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. We thank the participating women, mammography facilities, and radiologists for the data they have provided for this study. You can learn more about the BCSC at: http://www.bcsc-research.org/.

Funding

This research was funded by the Breast Cancer Surveillance Consortium program project (P01CA154292). Data collection for this work was additionally supported, in part, by funding from the National Cancer Institute (U54CA163303) and the Agency for Health Research and Quality (R01 HS018366-01A1).

Author information

Authors and Affiliations

  1. Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, 1545 Divisadero Street, Suite 309, San Francisco, CA, 94143-0320, USA

    Jeffrey A. Tice

  2. General Internal Medicine Section, Department of Veteran Affairs and Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Karla Kerlikowske

  3. Department of Public Health Sciences, University of California, Davis, Davis, CA, USA

    Michael C. S. Bissell & Diana L. Miglioretti

  4. Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA

    Diana L. Miglioretti

  5. Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM, USA

    Charlotte C. Gard

  6. Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL, USA

    Garth H. Rauscher

  7. Advocate Health Care, Downers Grove, IL, USA

    Firas M. Dabbous

Authors
  1. Jeffrey A. Tice
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael C. S. Bissell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diana L. Miglioretti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Charlotte C. Gard
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Garth H. Rauscher
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Firas M. Dabbous
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Karla Kerlikowske
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeffrey A. Tice.

Ethics declarations

Conflict of interest

Jeffrey A. Tice, MD, declares that he has no conflict of interest. Michael C. S. Bissell declares that he has no conflict of interest. Diana L. Miglioretti, PhD, declares that she has no conflict of interest. Charlotte C. Gard, PhD, MBA, declares that she has no conflict of interest. Garth H. Rauscher, PhD, declares that he has no conflict of interest. Firas M. Dabbous, MS, PhD, declares that he has no conflict of interest. Karla Kerlikowske, MD, declares that she has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Each registry and the Statistical Coordinating Center received institutional review board approval and a Federal Certificate of Confidentiality and other protection for the identities of the research subjects. All procedures are Health Insurance Portability and Accountability Act (HIPAA) compliant.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tice, J.A., Bissell, M.C.S., Miglioretti, D.L. et al. Validation of the breast cancer surveillance consortium model of breast cancer risk. Breast Cancer Res Treat 175, 519–523 (2019). https://doi.org/10.1007/s10549-019-05167-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-019-05167-2

Keywords

Search

Navigation