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. 2025 May 1;117(5):948-958.
doi: 10.1093/jnci/djae335.

Evaluating the performance of the Breast and Ovarian Analysis of Disease Incidence Algorithm model in predicting 10-year breast cancer risks in UK Biobank

Carmen Petitjean  1   2 , Naomi Wilcox  3 , Lorenzo Ficorella  3 , Joe Dennis  3 , Jonathan Tyrer  3   4 , Michael Lush  3 , Jacques Simard  5 , Douglas Easton  3   4 , Antonis C Antoniou  3 , Xin Yang  3
Affiliations

Evaluating the performance of the Breast and Ovarian Analysis of Disease Incidence Algorithm model in predicting 10-year breast cancer risks in UK Biobank

Carmen Petitjean et al. J Natl Cancer Inst. .

Abstract

Background: The Breast and Ovarian Analysis of Disease Incidence Algorithm (BOADICEA) model predicts breast cancer risk using cancer family history, epidemiological, and genetic data. We evaluated its validity in a large prospective cohort.

Methods: We assessed model calibration, discrimination and risk classification ability in 217 885 women (6838 incident breast cancers) aged 40-70 years of self-reported White ethnicity with no previous cancer from the UK Biobank. Age-specific risk classification was assessed using relative risk thresholds equivalent to the absolute lifetime risk categories of less than 17%, 17%-30%, and 30% or more, recommended by the National Institute for Health and Care Excellence guidelines. We predicted 10-year risks using BOADICEA v.6 considering cancer family history, questionnaire-based risk factors, a 313-single nucleotide polymorphisms polygenic score, and pathogenic variants. Mammographic density data were not available.

Results: The polygenic risk score was the most discriminative risk factor (area under the curve [AUC] = 0.65). Discrimination was highest when considering all risk factors (AUC = 0.66). The model was well calibrated overall (expected-to-observed ratio = 0.99, 95% confidence interval [CI] = 0.97 to 1.02; calibration slope = 0.99, 95% CI = 0.99 to 1.00), and in deciles of predicted risks. Discrimination was similar in women aged younger and older than 50 years. There was some underprediction in women aged younger than 50 years (expected-to-observed ratio = 0.89, 95% CI = 0.84 to 0.94; calibration slope = 0.96, 95% CI = 0.94 to 0.97), which was explained by the higher breast cancer incidence in UK Biobank than the UK population incidence in this age group. The model classified 87.2%, 11.4%, and 1.4% of women in relative risk categories less than 1.6, 1.6-3.1, and at least 3.1, identifying 25.6% of incident breast cancer patients in category relative risk of at least 1.6.

Conclusion: BOADICEA, implemented in CanRisk (www.canrisk.org), provides valid 10-year breast cancer risk, which can facilitate risk-stratified screening and personalized breast cancer risk management.

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Conflict of interest statement

ACA and DFE are named creators of the BOADICEA model, which has been licensed by Cambridge Enterprise (University of Cambridge). All other authors declare no conflict of interest.

Figures

Figure 1.
Figure 1.
Flowchart summarizing selection of participants in the UK Biobank. PRS = polygenic risk scores.
Figure 2.
Figure 2.
Calibration of 10-year predicted breast cancer risks under different risk factor combinations in Breast and Ovarian Analysis of Disease Incidence Algorithm. FH = family history; QRF = questionnaire-based risk factors; PRS = polygenic risk scores; PV = pathogenic variants.
Figure 3.
Figure 3.
Calibration of 10-year predicted breast cancer risks under different risk factor combinations by age groups. FH = family history; QRF = questionnaire-based risk factors; PRS = polygenic risk scores; PV = pathogenic variants.
Figure 4.
Figure 4.
Calibration of 10-year predicted breast cancer risks in pathogenic variant carriers. FH = family history; QRF = questionnaire-based risk factors; PRS = polygenic risk scores; PV = pathogenic variants.
See this image and copyright information in PMC

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