Journal of the National Cancer Institute Advance Access originally published online on November 13, 2007
JNCI Journal of the National Cancer Institute 2007 99(22):1695-1705; doi:10.1093/jnci/djm224
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© The Author 2007. Published by Oxford University Press.
ARTICLES |
Predicting Risk of Breast Cancer in Postmenopausal Women by Hormone Receptor Status
For the Women's Health Initiative Investigators
Affiliations of authors: Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (RTC); Fred Hutchinson Cancer Research Institute, Seattle, WA (GLA, AKA); Department of Preventive Medicine, State University of New York, Stony Brook, NY (DSL); Department of Epidemiology and Public Health, Albert Einstein College of Medicine, Bronx, NY (TR); Department of Medicine, University of California Davis, Sacramento, CA (SY); Department of Obstetrics and Gynecology, University of Wisconsin, Madison, WI (GS); Department of Medicine, Evanston Northwestern Healthcare, Evanston, IL (CAR); Department of Medicine, University of California, Irvine, CA (FAH)
Correspondence to: Rowan T. Chlebowski, MD, PhD, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA 90502 (e-mail: rchlebowski{at}gmail.com).
Background: Strategies for estrogen receptor (ER)–positive breast cancer risk reduction in postmenopausal women require screening of large populations to identify those with potential benefit. We evaluated and attempted to improve the performance of the Breast Cancer Risk Assessment Tool (i.e., the Gail model) for estimating invasive breast cancer risk by receptor status in postmenopausal women.
Methods: In The Women's Health Initiative cohort, breast cancer risk estimates from the Gail model and models incorporating additional or fewer risk factors and 5-year incidence of ER-positive and ER-negative invasive breast cancers were determined and compared by use of receiver operating characteristics and area under the curve (AUC) statistics. All statistical tests were two-sided.
Results: Among 147 916 eligible women, 3236 were diagnosed with invasive breast cancer. The overall AUC for the Gail model was 0.58 (95% confidence interval [CI]=0.56 to 0.60). The Gail model underestimated 5-year invasive breast cancer incidence by approximately 20% (P<.001), mostly among those with a low estimated risk. Discriminatory performance was better for the risk of ER-positive cancer (AUC = 0.60, 95% CI = 0.58 to 0.62) than for the risk of ER-negative cancer (AUC = 0.50, 95% CI = 0.45 to 0.54). Age and age at menopause were statistically significantly associated with ER-positive but not ER-negative cancers (P=.05 and P=.04 for heterogeneity, respectively). For ER-positive cancers, no additional risk factors substantially improved the Gail model prediction. However, a simpler model that included only age, breast cancer in first-degree relatives, and previous breast biopsy examination performed similarly for ER-positive breast cancer prediction (AUC=0.58, 95% CI= 0.56 to 0.60); postmenopausal women who were 55 years or older with either a previous breast biopsy examination or a family history of breast cancer had a 5-year breast cancer risk of 1.8% or higher.
Conclusions: In postmenopausal women, the Gail model identified populations at increased risk for ER-positive but not ER-negative breast cancers. A model with fewer variables appears to provide a simpler approach for screening for breast cancer risk.
| CONTEXT AND CAVEATS Prior knowledge Because many postmenopausal women must be screened to identify a population who will benefit from tamoxifen treatment for breast cancer risk reduction, methods to rapidly identify such a population are needed. Study design Data from the observational study and the clinical trial cohorts of the Women's Health Initiative were used. Four prediction models were investigated, including the Gail model (tested in the clinical trial cohort) and three logistic regression models (trained on the observational study cohort and tested on the clinical trial cohort). Contribution A model with only three risk factors—age, breast cancer in first-degree relatives, and previous breast biopsy examination—performed nearly as well as the Gail model for the prediction of estrogen receptor (ER)–positive breast cancer. Implications The new model with fewer variables than the Gail model may be as effective at identifying women at high risk for ER-positive breast cancer who would benefit from risk reduction interventions. Limitations Information on atypical hyperplasia, reproductive hormone levels, mammogram breast density, and bone mineral density, all risk factors for breast cancer, were not available. The Gail model was the only model evaluated; other models are in clinical use.
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Manuscript received May 23, 2007; revised September 5, 2007; accepted October 9, 2007.
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J Natl Cancer Inst 2007 99: 1657-1659.
J Natl Cancer Inst 2007 99: 1653.
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  M. H. Gail, W. F. Anderson, M. Garcia-Closas, and M. E. Sherman Absolute Risk Models for Subtypes of Breast Cancer J Natl Cancer Inst, November 21, 2007; 99(22): 1657 - 1659. [Full Text] [PDF]
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