Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme
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Accurate individualized breast cancer risk assessment is essential to provide risk–benefit analysis prior to initiating interventions designed to lower breast cancer risk and start surveillance. We have previously shown that a manual adaptation of Claus tables was as accurate as the Tyrer–Cuzick model and more accurate at predicting breast cancer than the Gail, Claus model and Ford models. Here we reassess the manual model with longer follow up and higher numbers of cancers. Calibration of the manual model was assessed using data from 8,824 women attending the family history evaluation and screening programme in Manchester UK, with a mean follow up of 9.71 years. After exclusion of 40 prevalent cancers, 406 incident breast cancers occurred, and 385.1 were predicted (O/E = 1.05, 95 % CI 0.95–1.16) using the manual model. Predictions were close to that of observed cancers in all risk categories and in all age groups, including women in their forties (O/E = 0.99, 95 % CI 0.83–1.16). Manual risk prediction with use of adjusted Claus tables and curves with modest adjustment for hormonal and reproductive factors was a well-calibrated approach to breast cancer risk estimation in a UK family history clinic.
KeywordsBreast cancer Risk estimation Prospective Claus Tyrer–Cuzick
This article presents independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research programme (Reference Number RP-PG-0707-10031: “Improvement in risk prediction, early detection and prevention of breast cancer”). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We acknowledge the support of the Manchester Biomedical Research Centre and the Genesis Breast Cancer Prevention Appeal. We would like to thank the study radiologists: Prof. Caroline Boggis, Prof. Anil Jain, Dr. YY Lim, Dr. Emma Hurley, Dr. Soujanya Gadde and Dr. Mary Wilson; the breast physicians Dr. Sally Bundred and Dr. Nicky Barr; and the advanced radiographer practitioners Elizabeth Lord, Rita Borgen and Jill Johnson, for mammography reporting. This study was funded By NIHR as part of the FH-risk study.
Conflict of interest
The authors declare no conflict of interest.
- 6.The University of Texas Southwestern Medical Center at Dallas. CancerGene. Available at http://www4.utsouthwestern.edu/breasthealth/cagene/CGdownload.asp
- 10.Cyrillic 3.0 pedigree software. Accessed on March 30, 2004. Details available at http://www.exetersoftware.com/cat/cyrillic/cyrillic.html
- 13.Lalloo F, Kerr B, Friedman J, Evans DGR (2005) Risk estimation for breast cancer. In: Evans DGR, Kerr B, Lalloo F, Friedman J (eds) Risk assessment and management in cancer genetics. Oxford University Press, Oxford, pp 47–64Google Scholar
- 15.McIntosh A, Shaw C, Evans G, Turnbull N, Bahar N, Barclay M, Easton D, Emery J, Gray J, Halpin J, Hopwood P, McKay J, Sheppard C, Sibbering M, Watson W, Wailoo A, Hutchinson A (2004 updated 2006) Clinical guidelines and evidence review for the classification and care of women at risk of familial breast cancer. National Collaborating Centre for Primary Care/University of Sheffield, London. NICE guideline CG041. www.nice.org.uk
- 16.Breslow NE, Day NE (1987) Statistical methods in cancer research Vol II. The design and analysis of cohort studies (IARC) Scientific Publication No 82. International Agency for Research on Cancer, LyonGoogle Scholar
- 18.https://pluto.srl.cam.ac.uk/cgi-bin/bd2/v2/bd.cgi. Accessed 9 August 2013
- 19.Antoniou AC, Cunningham AP, Peto J, Evans DG, Lalloo F, Narod SA, Risch HA, Eyfjord JE, Hopper JL, Southey MC, Olsson H, Johannsson O, Borg A, Pasini B, Radice P, Manoukian S, Eccles DM, Tang N, Olah E, Anton-Culver H, Warner E, Lubinski J, Gronwald J, Gorski B, Tryggvadottir L, Syrjakoski K, Kallioniemi OP, Eerola H, Nevanlinna H, Pharoah PD, Easton DF (2008) The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer 98(12):2015CrossRefGoogle Scholar
- 21.Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, Morris E, Pisano E, Schnall M, Sener S, Smith RA, Warner E, Yaffe M, Andrews KS, Russell CA, American Cancer Society Breast Cancer Advisory Group (2007) American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57:75–89PubMedCrossRefGoogle Scholar
- 22.Evans DGR, Lennard F, Pointon LJ, Ramus SJ, Gayther SA, Sodha N, Kwan-Lim GE, Leach MO, Warren R, Thompson D, Easton DF, Eeles R, On behalf of The UK study of MRI screening for breast cancer in women at high risk (MARIBS) (2009) Eligibility for MRI screening in the UK: effect of strict selection criteria and anonymous DNA testing on breast cancer incidence in the MARIBS study. Cancer Epid Biomarkers Prev 18(7):2123–2131CrossRefGoogle Scholar