Prospective Study of Breast Cancer Incidence in Women With a <i>BRCA1</i> or <i>BRCA2</i> Mutation Under Surveillance With and Without Magnetic Resonance Imaging
Bibliographic record
Abstract
PURPOSE: The sensitivity of magnetic resonance imaging (MRI) for breast cancer screening exceeds that of mammography. If MRI screening reduces mortality in women with a BRCA1 or BRCA2 mutation, it is expected that the incidence of advanced-stage breast cancers should be reduced in women undergoing MRI screening compared with those undergoing conventional screening. PATIENTS AND METHODS: We followed 1,275 women with a BRCA1 or BRCA2 mutation for a mean of 3.2 years. In total, 445 women were enrolled in an MRI screening trial in Toronto, Ontario, Canada, and 830 were in the comparison group. The cumulative incidences of ductal carcinoma in situ (DCIS), early-stage, and late-stage breast cancers were estimated at 6 years in the cohorts. RESULTS: There were 41 cases of breast cancer in the MRI-screened cohort (9.2%) and 76 cases in the comparison group (9.2%). The cumulative incidence of DCIS or stage I breast cancer at 6 years was 13.8% (95% CI, 9.1% to 18.5%) in the MRI-screened cohort and 7.2% (95% CI, 4.5% to 9.9%) in the comparison group (P = .01). The cumulative incidence of stages II to IV breast cancers was 1.9% (95% CI, 0.2% to 3.7%) in the MRI-screened cohort and 6.6% (95% CI, 3.8% to 9.3%) in the comparison group (P = .02). The adjusted hazard ratio for the development of stages II to IV breast cancer associated with MRI screening was 0.30 (95% CI, 0.12 to 0.72; P = .008). CONCLUSION: Annual surveillance with MRI is associated with a significant reduction in the incidence of advanced-stage breast cancer in BRCA1 and BRCA2 carriers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".