Is Mammography Adequate for Screening Women with Inherited BRCA Mutations and Low Breast Density?
Bibliographic record
Abstract
BACKGROUND: Several observational studies have shown that magnetic resonance imaging (MRI) is significantly more sensitive than mammography for screening women over age 25 at high risk for hereditary breast cancer; however, MRI is more costly and less specific than mammography. We sought to determine the extent to which the low sensitivity of mammography is due to greater breast density. METHODS: Breast density was evaluated for all patients on a high-risk screening study who were diagnosed with breast cancer between November 1997 and July 2006. Density was measured in two ways: qualitatively using the four categories characterized by the Breast Imaging Reporting and Data System and quantitatively using a computer-aided technique and classified as (a) <or=10%, (b) 11% to 25%, (c) 26% to 50%, and (d) >50% density. Comparison of sensitivity of mammography (and MRI) for each individual density category and after combining the highest two and lowest two density categories was done using Fisher's exact test. RESULTS: A total of 46 breast cancers [15 ductal carcinoma in situ (DCIS) and 31 invasive] were diagnosed in 45 women (42 with BRCA mutations). Mean age was 48.3 (range, 32-68) years. Overall, sensitivity of mammography versus MRI was 20% versus 87% for DCIS and 26% versus 90% for invasive cancer. There was a trend towards greater mammographic sensitivity for invasive cancer in women with fattier breasts compared with those with greater breast density (37-43% versus 8-12%; P = 0.1), but this trend was not seen for DCIS. CONCLUSION: It is necessary to add MRI to mammography for screening women with BRCA mutations even if their breast density is low.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".