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A model for individualized risk prediction of contralateral breast cancer

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

Abstract

Purpose

Patients diagnosed with invasive breast cancer (BC) or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral BC (CBC). This is a particularly disturbing trend as a large proportion of these CPMs are believed to be medically unnecessary. Many BC patients tend to substantially overestimate their CBC risk. Thus, there is a pressing need to educate patients effectively on their CBC risk. We develop a CBC risk prediction model to aid physicians in this task.

Methods

We used data from two sources: Breast Cancer Surveillance Consortium and Surveillance, Epidemiology, and End Results to build the model. The model building steps are similar to those used in developing the BC risk assessment tool (popularly known as Gail model) for counseling women on their BC risk. Our model, named CBCRisk, is exclusively designed for counseling women diagnosed with unilateral BC on the risk of developing CBC.

Results

We identified eight factors to be significantly associated with CBC—age at first BC diagnosis, anti-estrogen therapy, family history of BC, high-risk pre-neoplasia status, estrogen receptor status, breast density, type of first BC, and age at first birth. Combining the relative risk estimates with the relevant hazard rates, CBCRisk projects absolute risk of developing CBC over a given period.

Conclusions

By providing individualized CBC risk estimates, CBCRisk may help in counseling of BC patients. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

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Acknowledgements

This work was supported in part by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (HHSN261201100031C). 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 US. For a full description of these sources, please see http://breastscreening.cancer.gov/work/acknowledgement.html. The collection of cancer incidence and vital status data used in this study was supported, in part, by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under Contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, Contract HHSN261201000035C awarded to the University of Southern California, and Contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under Agreement U58-DP003862-01 awarded to the California Department of Public Health; Vermont Cancer Registry, Vermont Department of Health; Cancer Surveillance System of the Fred Hutchinson Cancer Research Center, which is funded by Contract Nos. N01-CN-005230, N01-CN-67009, N01-PC-35142, HHSN261201000029C, and HHSN261201300012I from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute with additional support from the Fred Hutchinson Cancer Research Center and the State of Washington; New Hampshire State Cancer Registry supported in part by Cooperative Agreement U55/CCU-121912 awarded to the New Hampshire Department of Health and Human Services, Division of Public Health Services, Bureau of Disease Control and Health Statistics, Health Statistics and Data Management Section; North Carolina Central Cancer Registry, which is partially supported by the Centers for Disease Control and Prevention under Cooperative Agreement DP12-120503CONT14; Colorado Central Cancer Registry, which is partially supported by the Colorado State General Fund and the federal Centers for Disease Control and Prevention (National Program of Cancer Registries) under Cooperative Agreement U58000848; New Mexico Tumor Registry supported, in part, by National Cancer Institute (NCI) Contract NO1-PC-35138 and by the University of New Mexico Cancer Center, a recipient of NCI Cancer Support Grant P30-CA118100. We thank the participating women, mammography facilities, and radiologists for the data they have provided for this study. A list of the BCSC investigators and procedures for requesting BCSC data for research purposes are provided at: http://breastscreening.cancer.gov/. We thank Linn Abraham for providing BCSC data-related support. We are also thankful to an anonymous reviewer for providing constructive comments. They have led to an improved version of the paper.

Funding

This work was funded by the National Cancer Institute at the National Institutes of Health (Grant Number R21 CA186086).

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Authors and Affiliations

  1. Department of Mathematical Sciences, University of Texas at Dallas, 800 W Campbell Rd, FO 35, Richardson, TX, 75080, USA

    Marzana Chowdhury, Swati Biswas & Pankaj K. Choudhary

  2. Division of Surgical Oncology, Johns Hopkins University, Baltimore, USA

    David Euhus

  3. Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, USA

    Tracy Onega

Authors
  1. Marzana Chowdhury
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  2. David Euhus
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  3. Tracy Onega
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  4. Swati Biswas
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  5. Pankaj K. Choudhary
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Correspondence to Swati Biswas or Pankaj K. Choudhary.

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The authors declare that they have no conflict of interest.

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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. The ideas and opinions expressed herein are those of the authors, and endorsement by the State of California, the California Department of Public Health; New Hampshire Department of Health and Human Services; the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors is not intended nor should be inferred.

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Chowdhury, M., Euhus, D., Onega, T. et al. A model for individualized risk prediction of contralateral breast cancer. Breast Cancer Res Treat 161, 153–160 (2017). https://doi.org/10.1007/s10549-016-4039-x

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