Performance of<i>BRCA1/2</i>Mutation Prediction Models in Asian Americans
Bibliographic record
Abstract
PURPOSE: There are established differences in breast cancer epidemiology between Asian and white individuals, but little is known about hereditary breast cancer in Asian populations. Although increasing numbers of Asian individuals are clinically tested for BRCA1/2 mutations, it is not known whether computer models that predict mutations work accurately in Asian individuals. We compared the performance in Asian and white individuals of two widely used BRCA1/2 mutation prediction models, BRCAPRO and Myriad II. PATIENTS AND METHODS: We evaluated BRCAPRO and Myriad II in 200 Asian individuals and a matched control group of 200 white individuals who were tested for BRCA1/2 mutations at four cancer genetics clinics, by comparing numbers of observed versus predicted mutation carriers and by evaluating area under the receiver operating characteristic curve (AUC) for each model. RESULTS: BRCAPRO and Myriad II accurately predicted the number of white BRCA1/2 mutation carriers (25 observed v 24 predicted by BRCAPRO; 25 predicted by Myriad II, P > or = .69), but underpredicted Asian carriers by two-fold (49 observed v 25 predicted by BRCAPRO; 26 predicted by Myriad II; P < or = 3 x 10(-7)). For BRCAPRO, this racial difference reflects substantial underprediction of Asian BRCA2 mutation carriers (26 observed v 4 predicted; P = 1 x 10(-30)); for Myriad II, separate mutation predictions were not available. For both models, AUCs were nonsignificantly lower in Asian than white individuals, suggesting less accurate discrimination between Asian carriers and noncarriers. CONCLUSION: Both BRCAPRO and Myriad II underestimated the proportion of BRCA1/2 mutation carriers, and discriminated carriers from noncarriers less well, in Asian compared with white individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".