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Published in: Familial Cancer 1/2015

Open Access 01-03-2015 | Original Article

Population-based genetic risk prediction and stratification for ovarian cancer: views from women at high risk

Authors: Belinda Rahman, Susanne F. Meisel, Lindsay Fraser, Lucy Side, Sue Gessler, Jane Wardle, Anne Lanceley

Published in: Familial Cancer | Issue 1/2015

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Abstract

There is an opportunity to improve outcomes for ovarian cancer (OC) through advances in risk stratification, early detection and diagnosis. A population-based OC genetic risk prediction and stratification program is being developed. A previous focus group study with individuals from the general population showed support for the proposed program. This qualitative interview study explores the attitudes of women at high risk of OC. Eight women participated in one-on-one, in-depth, semi-structured interviews to explore: experiences of learning of OC risk, risk perceptions, OC knowledge and awareness, and opinions on risk stratification approach. There was evidence of strong support for the proposed program. Benefits were seen as providing reassurance to women at low risk, and reducing worry in women at high risk through appropriate clinical management. Stratification into ‘low’ and ‘high’ risk groups was well-received. Participants were more hesitant about stratification to the ‘intermediate’ risk group. The data suggest formats to effectively communicate OC risk estimates will require careful thought. Interactions with GPs were highlighted as a barrier to OC risk assessment and diagnosis. These results are encouraging for the possible introduction and uptake of a risk prediction and stratification program for OC in the general population.
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Metadata
Title
Population-based genetic risk prediction and stratification for ovarian cancer: views from women at high risk
Authors
Belinda Rahman
Susanne F. Meisel
Lindsay Fraser
Lucy Side
Sue Gessler
Jane Wardle
Anne Lanceley
Publication date
01-03-2015
Publisher
Springer Netherlands
Published in
Familial Cancer / Issue 1/2015
Print ISSN: 1389-9600
Electronic ISSN: 1573-7292
DOI
https://doi.org/10.1007/s10689-014-9769-5

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