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Percent mammographic density prediction: development of a model in the nurses’ health studies

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Abstract

Purpose

To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses’ Health Studies’ nested breast cancer case–control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk.

Methods

Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r 2 in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk.

Results

In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both).

Conclusion

These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.

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Acknowledgments

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.

Funding

This study was supported by research grants from the National Cancer Institute, National Institutes of Health, UM1 CA186107, P01 CA87969, UM1 CA176726, R01 CA175080, R01 CA124865, and R01 CA131332, Avon Foundation for Women, Susan G. Komen for the Cure®, and Breast Cancer Research Foundation.

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Authors and Affiliations

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

    Megan S. Rice

  2. Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, 02114, USA

    Bernard A. Rosner & Rulla M. Tamimi

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

    Rulla M. Tamimi

Authors
  1. Megan S. Rice
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  2. Bernard A. Rosner
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  3. Rulla M. Tamimi
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Correspondence to Megan S. Rice.

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The authors have no conflicts of interest to declare.

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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.

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Rice, M.S., Rosner, B.A. & Tamimi, R.M. Percent mammographic density prediction: development of a model in the nurses’ health studies. Cancer Causes Control 28, 677–684 (2017). https://doi.org/10.1007/s10552-017-0898-7

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  • DOI: https://doi.org/10.1007/s10552-017-0898-7

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