Contribution of mammography to MRI screening in BRCA mutation carriers by BRCA status and age: individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND: We investigated the additional contribution of mammography to screening accuracy in BRCA1/2 mutation carriers screened with MRI at different ages using individual patient data from six high-risk screening trials. METHODS: Sensitivity and specificity of MRI, mammography and the combination of these tests were compared stratified for BRCA mutation and age using generalised linear mixed models with random effect for studies. Number of screens needed (NSN) for additional mammography-only detected cancer was estimated. RESULTS: In BRCA1/2 mutation carriers of all ages (BRCA1 = 1,219 and BRCA2 = 732), adding mammography to MRI did not significantly increase screening sensitivity (increased by 3.9% in BRCA1 and 12.6% in BRCA2 mutation carriers, P > 0.05). However, in women with BRCA2 mutation younger than 40 years, one-third of breast cancers were detected by mammography only. Number of screens needed for mammography to detect one breast cancer not detected by MRI was much higher for BRCA1 compared with BRCA2 mutation carriers at initial and repeat screening. CONCLUSIONS: Additional screening sensitivity from mammography above that from MRI is limited in BRCA1 mutation carriers, whereas mammography contributes to screening sensitivity in BRCA2 mutation carriers, especially those ⩽ 40 years. The evidence from our work highlights that a differential screening schedule by BRCA status is worth considering.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.020 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".