Effect of age and temporal patterns over 5 years in a Magnetic Resonance Imaging (MRI)-based breast surveillance study for BRCA mutation carriers
Bibliographic record
Abstract
9500 Background: With mammography (M) –based screening for BRCA mutation carriers, interval cancer rates are 44–56% and node +ve rates are 23–56%. (Brekelmans JCO 2001, Scheuer JCO 2002). Breast MRI is more sensitive than M but less specific (Robson ASCO 2002), and a reduction in breast cancer mortality is yet unproven. Methods: Since 11/97 BRCA mutation carriers ages 25 to 65 have been enrolled in a 5 year surveillance study of annual M, ultrasound (US), MRI, and semi-annual clinical breast examination (CBE). Results: 279 women (57% BRCA1, 39% previously affected) have had at least 1 round of screening, with 30 screen-detected cancers in 29 women and only 1 interval cancer (3%). Overall sensitivity of MRI was 84%, M 32% (p=0.005), US 40%, CBE 7%. These differences in sensitivity were almost identical for the 14 women ≥ age 50 vs. 17 women < age 50 at diagnosis, and for the 10 in-situ (DCIS) vs. 21 invasive cancers, and did not vary significantly over the 5 years. (See table below.) Conclusions: 1. MRI is significantly more sensitive than mammography independent of age. 2. MRI specificity improves more than ultrasound over time and is acceptable after the 1st year. 3. The extremely low interval cancer rate and tumour stage compared to historical controls, and the decrease in cancer detection rate and tumour stage after the first screen, all predict that MRI-based surveillance will likely lower cancer mortality rates in BRCA mutation carriers. Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Canadian Breast Cancer Research Alliance; Amersham Health; (Canadian) National Breast Cancer Fund; (US) NIH
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".