About CanRisk

The CanRisk tool is a web interface to BOADICEA — the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm — and the CanRisk-Prostate model. It calculates future breast, ovarian and prostate cancer risks.

These validated multifactorial models allow healthcare professionals to obtain breast, ovarian and prostate cancer risks for unaffected individuals on the basis of rare (high risk and moderate risk) breast, ovarian and prostate cancer genetic susceptibility variants, common cancer genetic susceptibility variants (using polygenic scores), explicit family history, personal lifestyle, hormonal and reproductive risk factors, and mammographic density in the case of breast cancer.

The breast and ovarian cancer models are described in Lee et al. 2019, Lee et al. 2021, Lee et al. 2022, Ficorella, Eriksson et al. 2025 and Ficorella, Yang et al. 2025. The prostate cancer model is described in Nyberg et al. 2022. For details see 'What information do the breast, ovarian and prostate cancer models use to determine risks?'

The CanRisk Tool carries the CE and UKCA marking and has been created and maintained by the University of Cambridge.

This work is supported by grants through Cancer Research UK, the European Union’s Horizon 2020 and Innovation programme, Genome Canada and a Wellcome Trust Collaborative Award.

Please see more details here on the Cancer Research-UK funded CanRisk programme.

How to Cite CanRisk

If you use the CanRisk tool or web-services then we ask that you cite Lee et al. (Genet Med, 2019), Carver et al. (Cancer Epidemiol Biomarkers Prev, 2020), Archer et al. (PLoS One 2020) and Nyberg et al. (JCO, 2022) in any resulting work or publications. We would be grateful if you could please also acknowledge the funding that supports the running of CanRisk by including the following acknowledgement: The CanRisk tool is supported by grant PPRPGM-Nov20\100002 from Cancer Research UK.

If you use the CanRisk web-services in a third party application then we ask that you acknowledge this on the application and link to the CanRisk home page (canrisk.org). To allow us to manage resources, we also ask that you inform us of expected usage (number of calculations) and the user account that calculations will be run from.

Usage of the CanRisk logo is for CanRisk business only and work that the CanRisk team is accountable for.

CanRisk Team

Team Leaders

Antonis Antoniou

Department of Public Health and Primary Care
University of Cambridge

Douglas Easton

Department of Public Health and Primary Care
University of Cambridge

Fiona Walter

Wolfson Institute of Population Health
Queen Mary University of London

Marc Tischkowitz

Department of Medical Genetics and National Institute for Health Research
University of Cambridge

Juliet Usher-Smith

Department of Public Health and Primary Care
University of Cambridge

Steph Archer

Department of Psychology
University of Cambridge

Development Team

Department of Public Health and Primary Care, University of Cambridge

Lorenzo Ficorella

Research Associate

Adam Stokes

Senior Research Programme Manager

Tim Carver

Lead Web Developer

Clinical Advisor

Jonathan Roberts

Alumni

Andrew Lee

Research Associate

Chantal Babb de Villiers

Research Associate

Simon Hartley

Computer Programmer

Alex Cunningham

Senior Software Developer/Coordinator

Acknowledgments

The CanRisk team are grateful to the following for their assistance in reviewing the language translations of the tool.

Spanish

Miguel de la Hoya
Judith Balmaña
Adrià López Fernández

French

Penny Soucy
Jacques Simard
Antoine De Pauw

German

Karin Kast
Anne Quante

Dutch

Daoud Ait Moha
Lizet van der Kolk

Portuguese

Rodrigo Guindalini
Maria Isabel Achatz

Italian

Valeria Viassolo
Liliana Varesco

Publications

2026

Easton, D. F. et al. Updated Testing Sensitivities and Variants of Uncertain Significance in CanRisk. BJC Rep (accepted, 2026).

2025

Ficorella, L. et al. Incorporating continuous mammographic density into the BOADICEA breast cancer risk prediction model. JCO Precision Oncology (2025).
Ficorella, L. et al. Adapting the BOADICEA breast and ovarian cancer risk models for the ethnically diverse UK population. British Journal of Cancer (2025).
Stutzin Donoso, F. et al. Primary care online training on multifactorial breast cancer risk: pre-post evaluation study. BJGP Open (2025).

2024

Petitjean, C. et al. Evaluating the performance of the Breast and Ovarian Analysis of Disease Incidence Algorithm model in predicting 10-year breast cancer risks in UK Biobank. J Natl Cancer Inst (2024).
Stutzin Donoso, F. et al. Improving the communication of multifactorial cancer risk assessment results for different audiences: a co-design process. J Community Genet (2024).
Ficorella, L. et al. BOADICEA model: updates to the BRCA2 breast cancer risks for ages 60 years and older. BJC Rep (2024).
Yang, X et al. Validation of the BOADICEA model for epithelial tubo-ovarian cancer risk prediction in UK Biobank. British Journal of Cancer (2024).
Yang, X et al. Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers. J Med Genetics (2024).

2023

Mavaddat, N. et al. Incorporating alternative Polygenic Risk Scores into the BOADICEA breast cancer risk prediction model. Cancer Epidemiol Biomarkers Prev (2023).

2022

Nyberg, T. et al. CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer. Journal of Clinical Oncology (2022).
Lee, A. J. et al. Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1, updates to tumour pathology and cancer incidences. J Med Genetics (2022).
Yang, X et al. Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study. J Med Genetics (2022).

2021

Li, S. X. et al. Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models. Cancers, 13, 5194 (2021).
Lee, A. J. et al. A Comprehensive Epithelial Tubo-Ovarian Cancer Risk Prediction Model Incorporating Genetic and Epidemiological Risk Factors J Med Genetics (2021).
Parichoy Pal Choudhury et al. Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry. Breast Cancer Research 23, 22 (2021).

2020

Carver, T. et al. CanRisk Tool—A Web Interface for the Prediction of Breast and Ovarian Cancer Risk and the Likelihood of Carrying Genetic Pathogenic Variants. Cancer Epidemiol Biomarkers Prev (2020).
Archer, S. et al. Evaluating clinician acceptability of the prototype CanRisk tool for predicting risk of breast and ovarian cancer: A multi-methods study. PLoS ONE 15, e0229999 (2020)

2019

Mavaddat, N. et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. The American Journal of Human Genetics 104, 21–34 (2019).
Lee, A. et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med 21, 1708–1718 (2019).

2018

Carver, T. et al. pedigreejs: a web-based graphical pedigree editor. Bioinformatics 34, 1069–1071 (2018).

2016

Lee, A. J. et al. Incorporating truncating variants in PALB2, CHEK2, and ATM into the BOADICEA breast cancer risk model. Genet. Med. 18, 1190–1198 (2016).

2015

Jervis, S. et al. A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects. Journal of Medical Genetics 52, 465–475 (2015).

2014

Lee, A. J. et al. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br. J. Cancer 110, 535–545 (2014).

2013

MacInnis, R. J. et al. Prospective validation of the breast cancer risk prediction model BOADICEA and a batch-mode version BOADICEACentre. British Journal of Cancer 109, 1296–1301 (2013).

2012

Mavaddat, N. et al. Pathology of Breast and Ovarian Cancers among BRCA1 and BRCA2 Mutation Carriers: Results from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Cancer Epidemiol Biomarkers Prev 21, 134–147 (2012).
Cunningham, A. P., Antoniou, A. C. & Easton, D. F. Clinical software development for the Web: lessons learned from the BOADICEA project. BMC Medical Informatics and Decision Making 12, 30 (2012).

2010

Mavaddat, N., Rebbeck, T. R., Lakhani, S. R., Easton, D. F. & Antoniou, A. C. Incorporating tumour pathology information into breast cancer risk prediction algorithms. Breast Cancer Research 12, R28 (2010).

2008

Antoniou, A. C. et al. Predicting the likelihood of carrying a BRCA1 or BRCA2 mutation: validation of BOADICEA, BRCAPRO, IBIS, Myriad and the Manchester scoring system using data from UK genetics clinics. Journal of Medical Genetics 45, 425–431 (2008).
Antoniou, A. C. et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. British Journal of Cancer 98, 1457–1466 (2008).

2006

Barcenas, C. H. et al. Assessing BRCA Carrier Probabilities in Extended Families. JCO 24, 354–360 (2006).
Antoniou, A. C. & Easton, D. F. Risk prediction models for familial breast cancer. Future Oncol 2, 257–274 (2006).

2005

Antoniou, A. C. et al. BRCA1 and BRCA2 mutation predictions using the BOADICEA and BRCAPRO models and penetrance estimation in high-risk French-Canadian families. Breast Cancer Research 8, R3 (2005).

2004

Antoniou, A. C., Pharoah, P. P. D., Smith, P. & Easton, D. F. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. British Journal of Cancer 91, 1580–1590 (2004).