Literature DB >> 28003316

Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women.

Matthew P Banegas1, Esther M John2, Martha L Slattery3, Scarlett Lin Gomez2, Mandi Yu4, Andrea Z LaCroix5, David Pee6, Rowan T Chlebowski7, Lisa M Hines8, Cynthia A Thompson9, Mitchell H Gail10.   

Abstract

Background: There is no model to estimate absolute invasive breast cancer risk for Hispanic women.
Methods: The San Francisco Bay Area Breast Cancer Study (SFBCS) provided data on Hispanic breast cancer case patients (533 US-born, 553 foreign-born) and control participants (464 US-born, 947 foreign-born). These data yielded estimates of relative risk (RR) and attributable risk (AR) separately for US-born and foreign-born women. Nativity-specific absolute risks were estimated by combining RR and AR information with nativity-specific invasive breast cancer incidence and competing mortality rates from the California Cancer Registry and Surveillance, Epidemiology, and End Results program to develop the Hispanic risk model (HRM). In independent data, we assessed model calibration through observed/expected (O/E) ratios, and we estimated discriminatory accuracy with the area under the receiver operating characteristic curve (AUC) statistic.
Results: The US-born HRM included age at first full-term pregnancy, biopsy for benign breast disease, and family history of breast cancer; the foreign-born HRM also included age at menarche. The HRM estimated lower risks than the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT) for US-born Hispanic women, but higher risks in foreign-born women. In independent data from the Women's Health Initiative, the HRM was well calibrated for US-born women (observed/expected [O/E] ratio = 1.07, 95% confidence interval [CI] = 0.81 to 1.40), but seemed to overestimate risk in foreign-born women (O/E ratio = 0.66, 95% CI = 0.41 to 1.07). The AUC was 0.564 (95% CI = 0.485 to 0.644) for US-born and 0.625 (95% CI = 0.487 to 0.764) for foreign-born women. Conclusions: The HRM is the first absolute risk model that is based entirely on data specific to Hispanic women by nativity. Further studies in Hispanic women are warranted to evaluate its validity. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

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Year:  2016        PMID: 28003316      PMCID: PMC5174188          DOI: 10.1093/jnci/djw215

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN: 0027-8874            Impact factor:   13.506


  29 in total

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Authors:  Lois B Travis; Deirdre Hill; Graça M Dores; Mary Gospodarowicz; Flora E van Leeuwen; Eric Holowaty; Bengt Glimelius; Michael Andersson; Eero Pukkala; Charles F Lynch; David Pee; Susan A Smith; Mars B Van't Veer; Timo Joensuu; Hans Storm; Marilyn Stovall; John D Boice; Ethel Gilbert; Mitchell H Gail
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2.  Validation studies for models projecting the risk of invasive and total breast cancer incidence.

Authors:  J P Costantino; M H Gail; D Pee; S Anderson; C K Redmond; J Benichou; H S Wieand
Journal:  J Natl Cancer Inst       Date:  1999-09-15       Impact factor: 13.506

3.  Low incidence of familial breast cancer among Hispanic women.

Authors:  M L Bondy; M R Spitz; S Halabi; J J Fueger; V G Vogel
Journal:  Cancer Causes Control       Date:  1992-07       Impact factor: 2.506

4.  Design of the Women's Health Initiative clinical trial and observational study. The Women's Health Initiative Study Group.

Authors: 
Journal:  Control Clin Trials       Date:  1998-02

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Authors:  M H Gail; L A Brinton; D P Byar; D K Corle; S B Green; C Schairer; J J Mulvihill
Journal:  J Natl Cancer Inst       Date:  1989-12-20       Impact factor: 13.506

6.  Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women.

Authors:  Martha L Slattery; Carol Sweeney; Sandra Edwards; Jennifer Herrick; Kathy Baumgartner; Roger Wolff; Maureen Murtaugh; Richard Baumgartner; Anna Giuliano; Tim Byers
Journal:  Breast Cancer Res Treat       Date:  2007-03       Impact factor: 4.872

7.  Genetic ancestry modifies the association between genetic risk variants and breast cancer risk among Hispanic and non-Hispanic white women.

Authors:  Laura Fejerman; Mariana C Stern; Elad Ziv; Esther M John; Gabriela Torres-Mejia; Lisa M Hines; Roger Wolff; Wei Wang; Kathy B Baumgartner; Anna R Giuliano; Martha L Slattery
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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.  Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information.

Authors:  Matthew E Mealiffe; Renee P Stokowski; Brian K Rhees; Ross L Prentice; Mary Pettinger; David A Hinds
Journal:  J Natl Cancer Inst       Date:  2010-10-18       Impact factor: 13.506

10.  The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms.

Authors:  Anne Marie McCarthy; Brad Keller; Despina Kontos; Leigh Boghossian; Erin McGuire; Mirar Bristol; Jinbo Chen; Susan Domchek; Katrina Armstrong
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  16 in total

1.  Performance of Breast Cancer Risk-Assessment Models in a Large Mammography Cohort.

Authors:  Anne Marie McCarthy; Zoe Guan; Michaela Welch; Molly E Griffin; Dorothy A Sippo; Zhengyi Deng; Suzanne B Coopey; Ahmet Acar; Alan Semine; Giovanni Parmigiani; Danielle Braun; Kevin S Hughes
Journal:  J Natl Cancer Inst       Date:  2020-05-01       Impact factor: 13.506

2.  Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening.

Authors:  Mitchell H Gail; Ruth M Pfeiffer
Journal:  J Natl Cancer Inst       Date:  2018-09-01       Impact factor: 13.506

3.  Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk.

Authors:  James Sun; Dung-Tsa Chen; Jiannong Li; Weihong Sun; Sean J Yoder; Tania E Mesa; Marek Wloch; Richard Roetzheim; Christine Laronga; M Catherine Lee
Journal:  J Surg Res       Date:  2019-08-13       Impact factor: 2.192

4.  Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening.

Authors:  Constance D Lehman; Sarah Mercaldo; Leslie R Lamb; Tari A King; Leif W Ellisen; Michelle Specht; Rulla M Tamimi
Journal:  J Natl Cancer Inst       Date:  2022-10-06       Impact factor: 11.816

5.  Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries.

Authors:  Amber N Hurson; Parichoy Pal Choudhury; Chi Gao; Anika Hüsing; Mikael Eriksson; Min Shi; Michael E Jones; D Gareth R Evans; Roger L Milne; Mia M Gaudet; Celine M Vachon; Daniel I Chasman; Douglas F Easton; Marjanka K Schmidt; Peter Kraft; Montserrat Garcia-Closas; Nilanjan Chatterjee
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6.  Breast Cancer Population Attributable Risk Proportions Associated with Body Mass Index and Breast Density by Race/Ethnicity and Menopausal Status.

Authors:  Michael C S Bissell; Karla Kerlikowske; Brian L Sprague; Jeffery A Tice; Charlotte C Gard; Katherine Y Tossas; Garth H Rauscher; Amy Trentham-Dietz; Louise M Henderson; Tracy Onega; Theresa H M Keegan; Diana L Miglioretti
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2020-07-29       Impact factor: 4.254

Review 7.  Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities.

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Journal:  NPJ Digit Med       Date:  2020-07-30

8.  Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women.

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Journal:  Breast Cancer Res       Date:  2018-12-20       Impact factor: 6.466

9.  Towards a more precise and individualized assessment of breast cancer risk.

Authors:  Marie E Wood; Nicholas H Farina; Thomas P Ahern; Melissa E Cuke; Janet L Stein; Gary S Stein; Jane B Lian
Journal:  Aging (Albany NY)       Date:  2019-02-20       Impact factor: 5.682

Review 10.  Breast Cancer Incidence and Risk Reduction in the Hispanic Population.

Authors:  Eric J Power; Megan L Chin; Mohamed M Haq
Journal:  Cureus       Date:  2018-02-26
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