Multimodality Breast Cancer Screening in Women with a Familial or Genetic Predisposition
Bibliographic record
Abstract
BACKGROUND: Women with a predisposition for breast cancer require a tailored screening program for early cancer detection. We evaluated the performance of mammography (MG), ultrasonography (US), and magnetic resonance imaging (MRI) screening in these women. PATIENTS AND METHODS: In asymptomatic women either confirmed as BRCA1/2 carriers, or having a greater than 30% probability of being so as estimated by brcapro [Berry D, Parmigiani G. Duke SPORE (Specialized Program of Research Excellence) in Breast Cancer. 1999], we conducted a prospective comparative trial consisting of annual MRI and MG, and biannual US and clinical breast examination. All evaluations were done within 30 days of one another. For each screening round, imaging tests were independently interpreted by three radiologists. RESULTS: The study enrolled 184 women, and 387 screening rounds were performed, detecting 12 cancers (9 infiltrating, 3 in situ), for an overall cancer yield of 6.5%. At diagnosis, 7 infiltrating cancers were smaller than 2 cm (T1); only 1 woman presented with axillary nodal metastases. All tumours were negative for the human epidermal growth factor receptor 2. Of the 12 cancers, MRI detected 10, and MG, 7; US did not identify any additional cancers. The overall recall rate after MRI was 21.8%, as compared with 11.4% for US and 16.1% for MG. Recall rates declined with successive screening rounds. In total, 45 biopsies were performed: 21 as a result of an US abnormality; 17, because of an MRI lesion; and 7, because of a MG anomaly. INTERPRETATION: In high-risk women, MRI offers the best sensitivity for breast cancer screening. The combination of yearly MRI and MG reached a negative predictive value of 100%. The recall rate is greatest with MRI, but declines for all modalities with successive screening rounds.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".