Literature DB >> 22581816

Assessing individual breast cancer risk within the U.K. National Health Service Breast Screening Program: a new paradigm for cancer prevention.

D Gareth R Evans1, Jane Warwick, Susan M Astley, Paula Stavrinos, Sarah Sahin, Sarah Ingham, Helen McBurney, Barbara Eckersley, Michelle Harvie, Mary Wilson, Ursula Beetles, Ruth Warren, Alan Hufton, Jamie C Sergeant, William G Newman, Iain Buchan, Jack Cuzick, Anthony Howell.   

Abstract

The aim of this study is to determine breast cancer risk at mammographic screening episodes and integrate standard risk factors with mammographic density and genetic data to assess changing the screening interval based on risk and offer women at high risk preventive strategies. We report our experience of assessing breast cancer risk within the U.K. National Health Service Breast Screening Program using results from the first 10,000 women entered into the "Predicting Risk Of breast Cancer At Screening" study. Of the first 28,849 women attending for screening at fifteen sites in Manchester 10,000 (35%) consented to study entry and completed the questionnaire. The median 10-year Tyrer-Cuzick breast cancer risk was 2.65% (interquartile range, 2.10-3.45). A total of 107 women (1.07%) had 10-year risks 8% or higher (high breast cancer risk), with a further 8.20% having moderately increased risk (5%-8%). Mammographic density (percent dense area) was 60% or more in 8.3% of women. We collected saliva samples from 478 women for genetic analysis and will extend this to 18% of participants. At time of consent to the study, 95.0% of women indicated they wished to know their risk. Women with a 10-year risk of 8% or more or 5% to 8% and mammographic density of 60% or higher were invited to attend or be telephoned to receive risk counseling; 81.9% of those wishing to know their risk have received risk counseling and 85.7% of these were found to be eligible for a risk-reducing intervention. These results confirm the feasibility of determining breast cancer risk and acting on the information in the context of population-based mammographic screening. ©2012 AACR.

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Year:  2012        PMID: 22581816     DOI: 10.1158/1940-6207.CAPR-11-0458

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


  47 in total

1.  Prediction of reader estimates of mammographic density using convolutional neural networks.

Authors:  Georgia V Ionescu; Martin Fergie; Michael Berks; Elaine F Harkness; Johan Hulleman; Adam R Brentnall; Jack Cuzick; D Gareth Evans; Susan M Astley
Journal:  J Med Imaging (Bellingham)       Date:  2019-01-31

2.  How does semi-automated computer-derived CT measure of breast density compare with subjective assessments to assess mean glandular breast density, in patients with breast cancer?

Authors:  G J Bansal; S Kotugodella
Journal:  Br J Radiol       Date:  2014-11-06       Impact factor: 3.039

3.  Factors Associated with Interest in Gene-Panel Testing and Risk Communication Preferences in Women from BRCA1/2 Negative Families.

Authors:  Kristina G Flores; Laurie E Steffen; Christopher J McLouth; Belinda E Vicuña; Amanda Gammon; Wendy Kohlmann; Lucretia Vigil; Zoneddy R Dayao; Melanie E Royce; Anita Y Kinney
Journal:  J Genet Couns       Date:  2016-08-06       Impact factor: 2.537

4.  One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

Authors:  Karla Kerlikowske; Charlotte C Gard; Brian L Sprague; Jeffrey A Tice; Diana L Miglioretti
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2015-03-30       Impact factor: 4.254

5.  Benign breast disease, mammographic breast density, and the risk of breast cancer.

Authors:  Jeffrey A Tice; Ellen S O'Meara; Donald L Weaver; Celine Vachon; Rachel Ballard-Barbash; Karla Kerlikowske
Journal:  J Natl Cancer Inst       Date:  2013-06-06       Impact factor: 13.506

6.  Screen-detected versus interval cancers: Effect of imaging modality and breast density in the Flemish Breast Cancer Screening Programme.

Authors:  Lore Timmermans; Luc Bleyen; Klaus Bacher; Koen Van Herck; Kim Lemmens; Chantal Van Ongeval; Andre Van Steen; Patrick Martens; Isabel De Brabander; Mathieu Goossens; Hubert Thierens
Journal:  Eur Radiol       Date:  2017-03-13       Impact factor: 5.315

7.  In utero DDT exposure and breast density before age 50.

Authors:  Nickilou Y Krigbaum; Piera M Cirillo; Julie D Flom; Jasmine A McDonald; Mary Beth Terry; Barbara A Cohn
Journal:  Reprod Toxicol       Date:  2019-11-08       Impact factor: 3.143

8.  Development and pilot testing of an online decision aid for women considering risk-stratified breast screening.

Authors:  Jocelyn Lippey; Louise Keogh; Ian Campbell; Gregory Bruce Mann; Laura Forrest
Journal:  J Community Genet       Date:  2022-01-21

9.  Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction.

Authors:  Elke M van Veen; Adam R Brentnall; Helen Byers; Elaine F Harkness; Susan M Astley; Sarah Sampson; Anthony Howell; William G Newman; Jack Cuzick; D Gareth R Evans
Journal:  JAMA Oncol       Date:  2018-04-01       Impact factor: 31.777

10.  From BRCA1 to Polygenic Risk Scores: Mutation-Associated Risks in Breast Cancer-Related Genes.

Authors:  Emma R Woodward; Elke M van Veen; D Gareth Evans
Journal:  Breast Care (Basel)       Date:  2021-03-31       Impact factor: 2.860

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