A Prospective Study of Breast Cancer Incidence and Stage Distribution in Women with a BRCA1 or BRCA2 Mutation under Surveillance with and without Magnetic Resonance Imaging.
Bibliographic record
Abstract
Abstract Introduction: The sensitivity of MRI for the detection of pre-clinical breast cancer exceeds that of mammography and of other screening tests. If MRI screening leads to reduced mortality in women with a BRCA1 or BRCA2 mutation, then it is expected that the incidence of advanced stage breast cancers should be reduced in a cohort of women undergoing regular MRI screening, compared to conventional screening.Methods: We followed 1275 women with a BRCA1 or BRCA2 mutation for a mean of 3.2 years for incident breast cancers. 445 women were enrolled in an MRI screening trial in Toronto and 830 controls, from elsewhere in Canada and the United States, underwent conventional screening. The cumulative incidences of DCIS, of early-stage and of late-stage breast cancer were estimated at six years in the two cohorts.Results: There were 41 cases of breast cancer diagnosed in the MRI-screened cohort and 76 cases of breast cancer diagnosed in the control cohort. The cumulative incidence of DCIS or stage I breast cancer at six years was 12.7% in the MRI-screened cohort and was 9.5% in the control group (p = 0.02; log rank test). The cumulative incidence of stage II – IV breast cancers at six years was 2.0% in the MRI-screened cohort and was 7.1% in the control group (p = 0.02; log rank test). The adjusted hazard ratio for the development of stage II – IV breast cancer associated with membership in the MRI-screened cohort was 0.30 (95% CI: 0.12 to 0.72; p = 0.008).Conclusion: Annual surveillance with MRI is associated with a significant reduction in the incidence of advanced stage breast cancer in BRCA1 and BRCA2 carriers.Funding for this study is generously provided by the Canadian Breast Cancer Research Alliance. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 26.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".