Long-term results of screening with magnetic resonance imaging in women with BRCA mutations
Bibliographic record
Abstract
BACKGROUND: The addition of breast magnetic resonance imaging (MRI) to screening mammography for women with BRCA mutations significantly increases sensitivity, but there is little data on clinical outcomes. We report screening performance, cancer stage, distant recurrence rate, and breast cancer-specific mortality in our screening study. METHODS: From 1997 to 2009, 496 women aged 25 to 65 years with a known BRCA1/2 mutation, of whom 380 had no previous cancer history, were enrolled in a prospective screening trial that included annual MRI and mammography. RESULTS: In 1847 screening rounds, 57 cancers were identified (53 screen-detected, 1 interval, and 3 incidental at prophylactic mastectomy), of which 37 (65%) were invasive. Sensitivity of MRI vs mammography was 86% vs 19% over the entire study period (P<0.0001), but was 74% vs 35% from 1997 to 2002 (P=0.02) and 94% vs 9% from 2003 to 2009 (P<0.0001), respectively. The relative sensitivities of MRI and mammography did not differ by mutation, age, or invasive vs non-invasive disease. Of the incident cancers, 97% were Stage 0 or 1. Of 28 previously unaffected women diagnosed with invasive cancer, 1 BRCA1 mutation carrier died following relapse of a 3 cm, node-positive breast cancer diagnosed on her first screen at age 48 (annual breast cancer mortality rate=0.5%). Three patients died of other causes. None of the 24 survivors has had a distant recurrence at a median follow-up of 8.4 years since diagnosis. CONCLUSION: Magnetic resonance imaging surveillance of women with BRCA1/2 mutations will detect the majority of breast cancers at a very early stage. The absence of distant recurrences of incident cancers to date is encouraging. However, longer follow-up is needed to confirm the safety of breast surveillance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".