gms | German Medical Science

51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (gmds)

10. - 14.09.2006, Leipzig

Is Risk Stratification for Breast Cancer Screening Methodologically Feasible?

Meeting Abstract

Suche in Medline nach

  • SW Fletcher - Harvard Medical School, Boston MA, USA

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (gmds). 51. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Leipzig, 10.-14.09.2006. Düsseldorf, Köln: German Medical Science; 2006. Doc06gmds431

Die elektronische Version dieses Artikels ist vollständig und ist verfügbar unter: http://www.egms.de/de/meetings/gmds2006/06gmds041.shtml

Veröffentlicht: 1. September 2006

© 2006 Fletcher.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.de). Er darf vervielf&aauml;ltigt, verbreitet und &oauml;ffentlich zug&aauml;nglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.


Gliederung

Text

Background

In 2004 the Institute of Medicine of the U.S. National Academies published a report, Saving Women’s Lives – Strategies for Improving Breast Cancer Detection and Diagnosis. A major recommendation was to develop tools to identify women who would benefit most from breast cancer screening, based on individually tailored risk prediction techniques. This talk examines what is known about risk stratification for breast cancer and the statistical and epidemiologic issues encountered when applying risk stratification to breast cancer screening.

Methods

A literature review of the breast cancer prediction tool developed by Gail et al at the U.S. National Cancer Institute was undertaken to determine the tool’s ability to predict breast cancer occurrence, for both calibration and discrimination.

Results

Few studies have been conducted. Those reporting on the validity of the Gail model found calibration high. One study reporting both calibration and discrimination found calibration high but discrimination little better than chance (concordance statistic = = .58). Estimates for young women with genetic mutations for breast cancer suggest much better discrimination.

Discussion

Risk stratification for breast cancer development in the general population presently works well at the population level but poorly at the individual level. The low prevalence of breast cancer in the general population and the small relative risks for most breast cancer risk factors and the fact that many factors are spread out over the population may be insurmountable problems in using risk stratification techniques when screening decisions are made for individual women. Young women with genetic mutations for breast cancer may be an exception.


References

1.
Fletcher SW. Risk stratification for breast cancer detection. In: Herdman R, Norton L, eds. Saving women’s lives - Strategies for improving breast cancer detection and diagnosis. A Breast Cancer Research Foundation and Institute of Medicine Symposium. Washington DC. The National Academies Press. 2005:43-50.
2.
Rockhill B, Spiegelman D, Byrne C, Hunter DJ, Colditz GA. Validation of the Gail et al model of breast cancer risk prediction and implications for chemoprevention. J Natl Cancer Inst. 2001;93:358-366.
3.
Wald NJ, Hackshaw AK, Frost CD. When can a risk factor be used as a worthwhile screening test? BMJ. 1999;319:1562-1565.