Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Issue 1 (December 2015)
- Record Type:
- Journal Article
- Title:
- Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort. Issue 1 (December 2015)
- Main Title:
- Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort
- Authors:
- Brentnall, Adam
Harkness, Elaine
Astley, Susan
Donnelly, Louise
Stavrinos, Paula
Sampson, Sarah
Fox, Lynne
Sergeant, Jamie
Harvie, Michelle
Wilson, Mary
Beetles, Ursula
Gadde, Soujanya
Lim, Yit
Jain, Anil
Bundred, Sara
Barr, Nicola
Reece, Valerie
Howell, Anthony
Cuzick, Jack
Evans, D. - Abstract:
- Abstract Introduction The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model). Methods Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression. Results The analysis included 50, 628 women aged 47–73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34–1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25–1.48), O/E 60 % (95 % CI 44–74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CIAbstract Introduction The Predicting Risk of Cancer at Screening study in Manchester, UK, is a prospective study of breast cancer risk estimation. It was designed to assess whether mammographic density may help in refinement of breast cancer risk estimation using either the Gail model (Breast Cancer Risk Assessment Tool) or the Tyrer-Cuzick model (International Breast Intervention Study model). Methods Mammographic density was measured at entry as a percentage visual assessment, adjusted for age and body mass index. Tyrer-Cuzick and Gail 10-year risks were based on a questionnaire completed contemporaneously. Breast cancers were identified at the entry screen or shortly thereafter. The contribution of density to risk models was assessed using odds ratios (ORs) with profile likelihood confidence intervals (CIs) and area under the receiver operating characteristic curve (AUC). The calibration of predicted ORs was estimated as a percentage [(observed vs expected (O/E)] from logistic regression. Results The analysis included 50, 628 women aged 47–73 years who were recruited between October 2009 and September 2013. Of these, 697 had breast cancer diagnosed after enrolment. Median follow-up was 3.2 years. Breast density [interquartile range odds ratio (IQR-OR) 1.48, 95 % CI 1.34–1.63, AUC 0.59] was a slightly stronger univariate risk factor than the Tyrer-Cuzick model [IQR-OR 1.36 (95 % CI 1.25–1.48), O/E 60 % (95 % CI 44–74), AUC 0.57] or the Gail model [IQR-OR 1.22 (95 % CI 1.12–1.33), O/E 46 % (95 % CI 26–65 %), AUC 0.55]. It continued to add information after allowing for Tyrer-Cuzick [IQR-OR 1.47 (95 % CI 1.33–1.62), combined AUC 0.61] or Gail [IQR-OR 1.45 (95 % CI 1.32–1.60), combined AUC 0.59]. Conclusions Breast density may be usefully combined with the Tyrer-Cuzick model or the Gail model. … (more)
- Is Part Of:
- Breast cancer research. Volume 17:Issue 1(2015)
- Journal:
- Breast cancer research
- Issue:
- Volume 17:Issue 1(2015)
- Issue Display:
- Volume 17, Issue 1 (2015)
- Year:
- 2015
- Volume:
- 17
- Issue:
- 1
- Issue Sort Value:
- 2015-0017-0001-0000
- Page Start:
- 1
- Page End:
- 10
- Publication Date:
- 2015-12
- Subjects:
- Breast -- Cancer -- Periodicals
616.99449 - Journal URLs:
- https://breast-cancer-research.biomedcentral.com/ ↗
http://www.bibliothek.uni-regensburg.de/ezeit/?2041618 ↗
http://link.springer.com/ ↗
http://pubmedcentral.nih.gov/tocrender.fcgi?journal=6 ↗
http://www.biomedcentral.com/1465-5411/ ↗ - DOI:
- 10.1186/s13058-015-0653-5 ↗
- Languages:
- English
- ISSNs:
- 1465-542X
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library DSC - BLDSS-3PM
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