Breast Cancer Research and Treatment

, Volume 152, Issue 1, pp 193–197 | Cite as

Assessing absolute changes in breast cancer risk due to modifiable risk factors

  • Anne S. Quante
  • Julia Herz
  • Alice S. Whittemore
  • Christine Fischer
  • Konstantin Strauch
  • Mary Beth TerryEmail author
Brief Report


Clinical risk assessment involves absolute risk measures, but information on modifying risk and preventing cancer is often communicated in relative terms. To illustrate the potential impact of risk factor modification in model-based risk assessment, we evaluated the performance of the IBIS Breast Cancer Risk Evaluation Tool, with and without current body mass index (BMI), for predicting future breast cancer occurrence in a prospective cohort of 665 postmenopausal women. Overall, IBIS’s accuracy (overall agreement between observed and assigned risks) and discrimination (AUC concordance between assigned risks and outcomes) were similar with and without the BMI information. However, in women with BMI > 25 kg/m2, adding BMI information improved discrimination (AUC = 63.9 % and 61.4 % with and without BMI, P < 0.001). The model-assigned 10-year risk difference for a woman with high (27 kg/m2) versus low (21 kg/m2) BMI was only 0.3 % for a woman with neither affected first-degree relatives nor BRCA1 mutation, compared to 4.5 % for a mutation carrier with three such relatives. This contrast illustrates the value of using information on modifiable risk factors in risk assessment and in sharing information with patients of their absolute risks with and without modifiable risk factors.


Breast cancer IBIS BMI Prediction model Risk evaluation 



This work was supported by Grant UM1 CA164920 from the USA National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR.

Conflict of interest

The authors declare that they have no competing interests.


The authors declare that they do not have a financial relationship with the organization that sponsored the research.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Anne S. Quante
    • 1
    • 2
  • Julia Herz
    • 1
    • 2
  • Alice S. Whittemore
    • 3
  • Christine Fischer
    • 4
  • Konstantin Strauch
    • 1
    • 2
  • Mary Beth Terry
    • 5
    • 6
    Email author
  1. 1.Chair of Genetic Epidemiology, Institute of Medical Informatics, Biometry and EpidemiologyLudwig-Maximilians-UniversitätMunichGermany
  2. 2.Institute of Genetic EpidemiologyHelmholtz Zentrum München - German Research Center for Environmental HealthNeuherbergGermany
  3. 3.Department of Health Research and PolicyStanford University School of MedicineStanfordUnited States
  4. 4.Institute of Human GeneticsUniversity of HeidelbergHeidelbergGermany
  5. 5.Department of Epidemiology, Joseph L. Mailman School of Public HealthColumbia UniversityNew YorkUnited States
  6. 6.Herbert Irving Comprehensive Cancer Center, Columbia Medical CenterNew YorkUnited States

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