Prospective breast cancer risk prediction model for women undergoing screening mammography

William E. Barlow, Emily White, Rachel Ballard-Barbash, Pamela M. Vacek, Linda Titus-Ernstoff, Patricia (Patty) Carney, Jeffrey A. Tice, Diana S M Buist, Berta M. Geller, Robert Rosenberg, Bonnie C. Yankaskas, Karla Kerlikowske

Research output: Contribution to journalArticle

316 Citations (Scopus)

Abstract

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 2392998 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.

Original languageEnglish (US)
Pages (from-to)1204-1214
Number of pages11
JournalJournal of the National Cancer Institute
Volume98
Issue number17
DOIs
StatePublished - Sep 6 2006

Fingerprint

Mammography
Breast Neoplasms
Logistic Models
Breast
Hormones
Confidence Intervals
Age Factors
Menopause
Body Mass Index
Breast Density
Therapeutics

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Cite this

Barlow, W. E., White, E., Ballard-Barbash, R., Vacek, P. M., Titus-Ernstoff, L., Carney, P. P., ... Kerlikowske, K. (2006). Prospective breast cancer risk prediction model for women undergoing screening mammography. Journal of the National Cancer Institute, 98(17), 1204-1214. https://doi.org/10.1093/jnci/djj331

Prospective breast cancer risk prediction model for women undergoing screening mammography. / Barlow, William E.; White, Emily; Ballard-Barbash, Rachel; Vacek, Pamela M.; Titus-Ernstoff, Linda; Carney, Patricia (Patty); Tice, Jeffrey A.; Buist, Diana S M; Geller, Berta M.; Rosenberg, Robert; Yankaskas, Bonnie C.; Kerlikowske, Karla.

In: Journal of the National Cancer Institute, Vol. 98, No. 17, 06.09.2006, p. 1204-1214.

Research output: Contribution to journalArticle

Barlow, WE, White, E, Ballard-Barbash, R, Vacek, PM, Titus-Ernstoff, L, Carney, PP, Tice, JA, Buist, DSM, Geller, BM, Rosenberg, R, Yankaskas, BC & Kerlikowske, K 2006, 'Prospective breast cancer risk prediction model for women undergoing screening mammography', Journal of the National Cancer Institute, vol. 98, no. 17, pp. 1204-1214. https://doi.org/10.1093/jnci/djj331
Barlow, William E. ; White, Emily ; Ballard-Barbash, Rachel ; Vacek, Pamela M. ; Titus-Ernstoff, Linda ; Carney, Patricia (Patty) ; Tice, Jeffrey A. ; Buist, Diana S M ; Geller, Berta M. ; Rosenberg, Robert ; Yankaskas, Bonnie C. ; Kerlikowske, Karla. / Prospective breast cancer risk prediction model for women undergoing screening mammography. In: Journal of the National Cancer Institute. 2006 ; Vol. 98, No. 17. pp. 1204-1214.
@article{0417945692774352b4ef8f83e220dfbe,
title = "Prospective breast cancer risk prediction model for women undergoing screening mammography",
abstract = "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 2392998 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.",
author = "Barlow, {William E.} and Emily White and Rachel Ballard-Barbash and Vacek, {Pamela M.} and Linda Titus-Ernstoff and Carney, {Patricia (Patty)} and Tice, {Jeffrey A.} and Buist, {Diana S M} and Geller, {Berta M.} and Robert Rosenberg and Yankaskas, {Bonnie C.} and Karla Kerlikowske",
year = "2006",
month = "9",
day = "6",
doi = "10.1093/jnci/djj331",
language = "English (US)",
volume = "98",
pages = "1204--1214",
journal = "Journal of the National Cancer Institute",
issn = "0027-8874",
publisher = "Oxford University Press",
number = "17",

}

TY - JOUR

T1 - Prospective breast cancer risk prediction model for women undergoing screening mammography

AU - Barlow, William E.

AU - White, Emily

AU - Ballard-Barbash, Rachel

AU - Vacek, Pamela M.

AU - Titus-Ernstoff, Linda

AU - Carney, Patricia (Patty)

AU - Tice, Jeffrey A.

AU - Buist, Diana S M

AU - Geller, Berta M.

AU - Rosenberg, Robert

AU - Yankaskas, Bonnie C.

AU - Kerlikowske, Karla

PY - 2006/9/6

Y1 - 2006/9/6

N2 - 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 2392998 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.

AB - 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 2392998 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.

UR - http://www.scopus.com/inward/record.url?scp=33748692404&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33748692404&partnerID=8YFLogxK

U2 - 10.1093/jnci/djj331

DO - 10.1093/jnci/djj331

M3 - Article

C2 - 16954473

AN - SCOPUS:33748692404

VL - 98

SP - 1204

EP - 1214

JO - Journal of the National Cancer Institute

JF - Journal of the National Cancer Institute

SN - 0027-8874

IS - 17

ER -