Literature DB >> 21604157

Breast cancer risk assessment in women aged 70 and older.

Pamela M Vacek1, Joan M Skelly, Berta M Geller.   

Abstract

Although the benefit of screening mammography for women over 69 has not been established, it is generally agreed that screening recommendations for older women should be individualized based on health status and breast cancer risk. However, statistical models to assess breast cancer risk have not been previously evaluated in this age group. In this study, the original Gail model and three more recent models that include mammographic breast density as a risk factor were applied to a cohort of 19,779 Vermont women aged 70 and older. Women were followed for an average of 7.1 years and 821 developed breast cancer. The predictive accuracy of each risk model was measured by its c-statistic and associations between individual risk factors and breast cancer risk were assessed by Cox regression. C-statistics were 0.54 (95% CI = 0.52-0.56) for the Gail model, 0.54 (95% CI = 0.51-0.56) for the Tice modification of the Gail model, 0.55 (95% CI = 0.53-0.58) for a model developed by Barlow and 0.55 (95% CI = 0.53-0.58) for a Vermont model. These results indicate that the models are not useful for assessing risk in women aged 70 and older. Several risk factors in the models were not significantly associated with outcome in the cohort, while others were significantly related to outcome but had smaller relative risks than estimated by the models. Age-related attenuation of the effects of some risk factors makes the prediction of breast cancer in older women particularly difficult.

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Year:  2011        PMID: 21604157     DOI: 10.1007/s10549-011-1576-1

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  10 in total

1.  Association of Patient Age With Outcomes of Current-Era, Large-Scale Screening Mammography: Analysis of Data From the National Mammography Database.

Authors:  Cindy S Lee; Debapriya Sengupta; Mythreyi Bhargavan-Chatfield; Edward A Sickles; Elizabeth S Burnside; Margarita L Zuley
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2.  Prospective approach to breast cancer risk prediction in African American women: the black women's health study model.

Authors:  Deborah A Boggs; Lynn Rosenberg; Lucile L Adams-Campbell; Julie R Palmer
Journal:  J Clin Oncol       Date:  2015-01-26       Impact factor: 44.544

3.  Decision-Making Regarding Mammography Screening for Older Women.

Authors:  Mara A Schonberg
Journal:  J Am Geriatr Soc       Date:  2016-12-05       Impact factor: 5.562

4.  Breast cancer risk in older women: results from the NIH-AARP Diet and Health Study.

Authors:  Louise A Brinton; Llewellyn Smith; Gretchen L Gierach; Ruth M Pfeiffer; Sarah J Nyante; Mark E Sherman; Yikyung Park; Albert R Hollenbeck; Cher M Dallal
Journal:  Cancer Causes Control       Date:  2014-05-09       Impact factor: 2.506

Review 5.  Management of operable breast cancer in older women.

Authors:  Ian S Fentiman
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Review 6.  Screening mammography in older women: a review.

Authors:  Louise C Walter; Mara A Schonberg
Journal:  JAMA       Date:  2014-04-02       Impact factor: 56.272

7.  Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement.

Authors:  Hatef Darabi; Kamila Czene; Wanting Zhao; Jianjun Liu; Per Hall; Keith Humphreys
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8.  Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis.

Authors:  Xin Wang; Yubei Huang; Lian Li; Hongji Dai; Fengju Song; Kexin Chen
Journal:  Breast Cancer Res       Date:  2018-03-13       Impact factor: 6.466

9.  High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer.

Authors:  Jingmei Li; Laszlo Szekely; Louise Eriksson; Boel Heddson; Ann Sundbom; Kamila Czene; Per Hall; Keith Humphreys
Journal:  Breast Cancer Res       Date:  2012-07-30       Impact factor: 6.466

10.  Risk assessment model for invasive breast cancer in Hong Kong women.

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Journal:  Medicine (Baltimore)       Date:  2016-08       Impact factor: 1.889

  10 in total

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