Literature DB >> 23060039

Baseline mammographic breast density and the risk of invasive breast cancer in postmenopausal women participating in the NSABP study of tamoxifen and raloxifene (STAR).

Reena S Cecchini1, Joseph P Costantino, Jane A Cauley, Walter M Cronin, D Lawrence Wickerham, Hanna Bandos, Joel L Weissfeld, Norman Wolmark.   

Abstract

Mammographic breast density is an established risk factor for breast cancer. However, results are inconclusive regarding its use in risk prediction models. The current study evaluated 13,409 postmenopausal participants in the NSABP Study of Tamoxifen and Raloxifene. A measure of breast density as reported on the entry mammogram report was extracted and categorized according to The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) classifications. An increased risk of invasive breast cancer was associated with higher mammographic breast density (P < 0.001). The association remained significant after adjusting for age, treatment, and smoking history [HR 1.35, 95% confidence interval (CI): 1.16-1.58], as well as when added to a model including the Gail score (HR 1.33, 95% CI: 1.14-1.55). At five years after random assignment, time-dependent area under the curve (AUC) improved from 0.63 for a model with Gail score alone to 0.64 when considering breast density and Gail score. Breast density was also significant when added to an abbreviated model tailored for estrogen receptor-positive breast cancers (P = 0.02). In this study, high BI-RADS breast density was significantly associated with increased breast cancer risk when considered in conjunction with Gail score but provided only slight improvement to the Gail score for predicting the incidence of invasive breast cancer. The BI-RADS breast composition classification system is a quick and readily available method for assessing breast density for risk prediction evaluations; however, its addition to the Gail model does not seem to provide substantial predictability improvements in this population of postmenopausal healthy women at increased risk for breast cancer.

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Year:  2012        PMID: 23060039      PMCID: PMC4131535          DOI: 10.1158/1940-6207.CAPR-12-0273

Source DB:  PubMed          Journal:  Cancer Prev Res (Phila)        ISSN: 1940-6215


  35 in total

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2.  Survival model predictive accuracy and ROC curves.

Authors:  Patrick J Heagerty; Yingye Zheng
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3.  Accuracy of assigned BI-RADS breast density category definitions.

Authors:  Brandi T Nicholson; Alexander P LoRusso; Mark Smolkin; Viktor E Bovbjerg; Gina R Petroni; Jennifer A Harvey
Journal:  Acad Radiol       Date:  2006-09       Impact factor: 3.173

4.  Mammographic density measured with quantitative computer-aided method: comparison with radiologists' estimates and BI-RADS categories.

Authors:  Katherine E Martin; Mark A Helvie; Chuan Zhou; Marilyn A Roubidoux; Janet E Bailey; Chintana Paramagul; Caroline E Blane; Katherine A Klein; Seema S Sonnad; Heang-Ping Chan
Journal:  Radiology       Date:  2006-07-20       Impact factor: 11.105

5.  Update of the National Surgical Adjuvant Breast and Bowel Project Study of Tamoxifen and Raloxifene (STAR) P-2 Trial: Preventing breast cancer.

Authors:  Victor G Vogel; Joseph P Costantino; D Lawrence Wickerham; Walter M Cronin; Reena S Cecchini; James N Atkins; Therese B Bevers; Louis Fehrenbacher; Eduardo R Pajon; James L Wade; André Robidoux; Richard G Margolese; Joan James; Carolyn D Runowicz; Patricia A Ganz; Steven E Reis; Worta McCaskill-Stevens; Leslie G Ford; V Craig Jordan; Norman Wolmark
Journal:  Cancer Prev Res (Phila)       Date:  2010-04-19

6.  Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population.

Authors:  Jeffrey A Tice; Steven R Cummings; Elad Ziv; Karla Kerlikowske
Journal:  Breast Cancer Res Treat       Date:  2005-11       Impact factor: 4.872

Review 7.  Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk.

Authors:  Steven R Cummings; Jeffrey A Tice; Scott Bauer; Warren S Browner; Jack Cuzick; Elad Ziv; Victor Vogel; John Shepherd; Celine Vachon; Rebecca Smith-Bindman; Karla Kerlikowske
Journal:  J Natl Cancer Inst       Date:  2009-03-10       Impact factor: 13.506

8.  Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.

Authors:  Jeffrey A Tice; Steven R Cummings; Rebecca Smith-Bindman; Laura Ichikawa; William E Barlow; Karla Kerlikowske
Journal:  Ann Intern Med       Date:  2008-03-04       Impact factor: 25.391

9.  Determinants of percentage and area measures of mammographic density.

Authors:  Jennifer Stone; Ruth M L Warren; Elizabeth Pinney; Jane Warwick; Jack Cuzick
Journal:  Am J Epidemiol       Date:  2009-11-12       Impact factor: 4.897

10.  Projecting individualized absolute invasive breast cancer risk in African American women.

Authors:  Mitchell H Gail; Joseph P Costantino; David Pee; Melissa Bondy; Lisa Newman; Mano Selvan; Garnet L Anderson; Kathleen E Malone; Polly A Marchbanks; Worta McCaskill-Stevens; Sandra A Norman; Michael S Simon; Robert Spirtas; Giske Ursin; Leslie Bernstein
Journal:  J Natl Cancer Inst       Date:  2007-11-27       Impact factor: 13.506

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Journal:  Nat Med       Date:  2014-04       Impact factor: 53.440

Review 2.  Ongoing Use of Data and Specimens From National Cancer Institute-Sponsored Cancer Prevention Clinical Trials in the Community Clinical Oncology Program.

Authors:  Lori M Minasian; Catherine M Tangen; D Lawrence Wickerham
Journal:  Semin Oncol       Date:  2015-07-10       Impact factor: 4.929

3.  Mammographic density: intersection of advocacy, science, and clinical practice.

Authors:  Katherine Tossas-Milligan; Sundus Shalabi; Veronica Jones; Patricia J Keely; Matthew W Conklin; Kevin W Elicerie; Robert Winn; Christopher Sistrunk; Joseph Geradts; Gustavo Miranda-Carboni; Eric C Dietze; Lisa D Yee; Victoria L Seewaldt
Journal:  Curr Breast Cancer Rep       Date:  2019-07-24

4.  Emerging Concepts in Breast Cancer Risk Prediction.

Authors:  Gretchen L Gierach; Xiaohong R Yang; Jonine D Figueroa; Mark E Sherman
Journal:  Curr Obstet Gynecol Rep       Date:  2013-03

5.  Mammographic Density Laws and Inclusion-Time for Change.

Authors:  Katherine Y Tossas; Robert A Winn; Victoria L Seewaldt
Journal:  JAMA Oncol       Date:  2022-01-01       Impact factor: 33.006

6.  The perils of generalization: Rethinking breast cancer screening guidelines for young women of color.

Authors:  Victoria L Seewaldt; Leslie Bernstein
Journal:  Cancer       Date:  2021-08-24       Impact factor: 6.860

Review 7.  Risk determination and prevention of breast cancer.

Authors:  Anthony Howell; Annie S Anderson; Robert B Clarke; Stephen W Duffy; D Gareth Evans; Montserat Garcia-Closas; Andy J Gescher; Timothy J Key; John M Saxton; Michelle N Harvie
Journal:  Breast Cancer Res       Date:  2014-09-28       Impact factor: 6.466

8.  High mammographic density in women is associated with protumor inflammation.

Authors:  Cecilia W Huo; Prue Hill; Grace Chew; Paul J Neeson; Heloise Halse; Elizabeth D Williams; Michael A Henderson; Erik W Thompson; Kara L Britt
Journal:  Breast Cancer Res       Date:  2018-08-09       Impact factor: 6.466

9.  Association of mammographic density with pathologic findings.

Authors:  Nasrin Ahmadinejad; Samaneh Movahedinia; Sajjadeh Movahedinia; Mona Shahriari
Journal:  Iran Red Crescent Med J       Date:  2013-12-05       Impact factor: 0.611

  9 in total

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