Adapting the BOADICEA breast and ovarian cancer risk models for the ethnically diverse UK population
Bibliographic record
Abstract
Abstract Background BOADICEA is a widely used algorithm for predicting breast and ovarian cancer risks, using a combination of genetic and lifestyle/environmental risk factors. However, it has largely been developed using data from individuals of White ethnicity. Methods We utilised data from multiple sources to derive estimates for the distributions of risk factors and their effect sizes in major UK ethnic groups (White, Black, South Asian, East Asian, and Mixed). We combined these with ethnicity-specific population cancer incidences to update BOADICEA so that it provides ethnicity-specific risk estimates. We also developed and included a method for deriving adjusted polygenic scores for individuals of mixed genetic ancestry. Results The predicted average absolute risks were smaller in all non-White ethnic groups than in Whites, and the risk distributions were narrower. The proportion of women classified as at moderate or high risk of breast or ovarian cancer, according to national guidelines, was considerably smaller in non-White women. Discussion The updated BOADICEA (v7), available in the CanRisk tool ( www.canrisk.org ), is based on estimates more appropriate for non-White women in the UK. Further validation of the model in prospective studies is required. Considering these findings, risk classification guidelines for non- White women may need to be revised.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".