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Tytuł pozycji:

Prospective breast cancer risk prediction model for women undergoing screening mammography.

Tytuł :
Prospective breast cancer risk prediction model for women undergoing screening mammography.
Autorzy :
Barlow WE; Cancer Research and Biostatistics, 1730 Minor Avenue, Suite 1900, Seattle, WA 98101, USA. />White E
Ballard-Barbash R
Vacek PM
Titus-Ernstoff L
Carney PA
Tice JA
Buist DS
Geller BM
Rosenberg R
Yankaskas BC
Kerlikowske K
Pokaż więcej
Źródło :
Journal Of The National Cancer Institute [J Natl Cancer Inst] 2006 Sep 06; Vol. 98 (17), pp. 1204-14.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural
Język :
English
Journal Info :
Publisher: Oxford University Press Country of Publication: United States NLM ID: 7503089 Publication Model: Print Cited Medium: Internet ISSN: 1460-2105 (Electronic) Linking ISSN: 00278874 NLM ISO Abbreviation: J. Natl. Cancer Inst. Subsets: MEDLINE
Imprint Name(s) :
Publication: <2003-> : Cary, NC : Oxford University Press
Original Publication: Bethesda, Md., U. S. Dept. of Health, Education, and Welfare, Public Health Service, National Institutes of Health; Washington, for sale by the Supt. of Docs., U. S. Govt. Print. Off.
MeSH Terms :
Mammography*
Mass Screening*
Models, Statistical*
Breast Neoplasms/*diagnostic imaging
Breast Neoplasms/*epidemiology
Adult ; Aged ; Breast Neoplasms/prevention & control ; Carcinoma, Ductal, Breast/diagnostic imaging ; Carcinoma, Ductal, Breast/epidemiology ; Confounding Factors (Epidemiology) ; Female ; Humans ; Incidence ; Logistic Models ; Middle Aged ; Multivariate Analysis ; Odds Ratio ; Postmenopause ; Predictive Value of Tests ; Premenopause ; Prognosis ; Prospective Studies ; Registries ; Risk Assessment ; Risk Factors ; SEER Program ; United States/epidemiology
Grant Information :
U01CA70040 United States CA NCI NIH HHS; U01CA63740 United States CA NCI NIH HHS; U01CA69976 United States CA NCI NIH HHS; U01CA70013 United States CA NCI NIH HHS; U01 CA086082 United States CA NCI NIH HHS; U01CA63736 United States CA NCI NIH HHS; U01CA86082 United States CA NCI NIH HHS; U01CA86076 United States CA NCI NIH HHS; U01CA63731 United States CA NCI NIH HHS
Entry Date(s) :
Date Created: 20060907 Date Completed: 20060919 Latest Revision: 20161124
Update Code :
20181210
PMID :
16954473
Czasopismo naukowe
Background: Risk prediction models for breast cancer can be improved by the addition of recently identified risk factors, including breast density and use of hormone therapy. We used prospective risk information to predict a diagnosis of breast cancer in a cohort of 1 million women undergoing screening mammography.
Methods: There were 2,392,998 eligible screening mammograms from women without previously diagnosed breast cancer who had had a prior mammogram in the preceding 5 years. Within 1 year of the screening mammogram, 11,638 women were diagnosed with breast cancer. Separate logistic regression risk models were constructed for premenopausal and postmenopausal examinations by use of a stringent (P<.0001) criterion for the inclusion of risk factors. Risk models were constructed with 75% of the data and validated with the remaining 25%. Concordance of the predicted with the observed outcomes was assessed by a concordance (c) statistic after logistic regression model fit. All statistical tests were two-sided.
Results: Statistically significant risk factors for breast cancer diagnosis among premenopausal women included age, breast density, family history of breast cancer, and a prior breast procedure. For postmenopausal women, the statistically significant factors included age, breast density, race, ethnicity, family history of breast cancer, a prior breast procedure, body mass index, natural menopause, hormone therapy, and a prior false-positive mammogram. The model may identify high-risk women better than the Gail model, although predictive accuracy was only moderate. The c statistics were 0.631 (95% confidence interval [CI] = 0.618 to 0.644) for premenopausal women and 0.624 (95% CI = 0.619 to 0.630) for postmenopausal women.
Conclusion: Breast density is a strong additional risk factor for breast cancer, although it is unknown whether reduction in breast density would reduce risk. Our risk model may be able to identify women at high risk for breast cancer for preventive interventions or more intensive surveillance.
Comment in: J Natl Cancer Inst. 2006 Sep 6;98(17):1172-3. (PMID: 16954464)

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