ArticleJournal of the National Cancer Institute2025
Evaluating the performance of the Breast and Ovarian Analysis of Disease Incidence Algorithm model in predicting 10-year breast cancer risks in UK Biobank.
Article in Journal of the National Cancer Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The current state of polygenic scores for the development of lung cancer: a systematic review and validation in UK Biobank.British journal of cancer · 2026Pooled it
- Comparison of NICE criteria with the BOADICEA multifactorial risk model to guide breast cancer risk assessment and referral amongst women under age 50 within primary care.British journal of cancer · 2026Article
- Risk-based breast cancer screening.British journal of cancer · 2026Article
- Polygenic risk scores in cancer.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026Article
- Ultra-rare functional variants reveal early-onset breast cancer risk genes and pathways in the UK Biobank and All of Us Research Program.American journal of human genetics · 2026Article
- Article
- Perspectives from a multi-stakeholder workshop for implementing a risk-stratified breast cancer screening program in Canada.Communications medicine · 2026Review
- Challenges in the future of cancer screening.International journal of cancer · 2026Review
- Harnessing genomics for early cancer detection, risk stratification and prevention.Nature genetics · 2026Review
- AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2026.Breast care (Basel, Switzerland) · 2026Review
- Modeling Individual-Level Uncertainty From Missing Data in Multifactorial Breast Cancer Risk Prediction.JCO precision oncology · 2026Article
- Breast cancer risk prediction with a modified BOADICEA model in Danish women.British journal of cancer · 2026Article
- Implementing integrated genomic risk assessments for breast cancer: lessons learned from the Electronic Medical Records and Genomics study.JAMIA open · 2025Article
- AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2025.Breast care (Basel, Switzerland) · 2025Review
- Barriers and Facilitators to Delivering Multifactorial Risk Assessment and Communication for Personalized Breast Cancer Screening: A Qualitative Study Exploring Implementation in Canada.Current oncology (Toronto, Ont.) · 2025Article
- CanRisk-GP protocol: A feasibility study of incorporating proactive multifactorial breast cancer risk assessment into general practice.PloS one · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundThe 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.
methodsWe 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.
resultsThe 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.
conclusionBOADICEA, 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.