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Br J Cancer. 2014 Jan 21;110(2):535-45. doi: 10.1038/bjc.2013.730. Epub 2013 Dec 17.

BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface.

Author information

  • 1Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK.
  • 21] Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK [2] Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK.
  • 31] Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK [2] Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK [3] Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK.

Abstract

BACKGROUND:

The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions.

METHODS:

We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting.

RESULTS:

We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions.

CONCLUSION:

All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/.

PMID:
24346285
[PubMed - indexed for MEDLINE]
PMCID:
PMC3899766
Free PMC Article
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