Is Mammographic Breast Density a Breast Cancer Risk Factor in Women With <i>BRCA</i> Mutations?
Bibliographic record
Abstract
PURPOSE: Increased mammographic breast density is well recognized as a breast cancer risk factor in the general population. However, it is unclear whether it is a risk factor in women with BRCA mutations. We present the results of a nested case-control screening study investigating the relationship between breast density and breast cancer incidence in this population. PATIENTS AND METHODS: Women ages 25 to 65 years with known BRCA mutations were enrolled onto a single-center, high-risk breast cancer screening program. Using a computer-aided technique (Cumulus), quantitative percentage density (PD) was measured for each participant on her baseline mammogram by a single, blinded observer. RESULTS: Between November 1997 and March 2008, 462 women (mean age, 44 years; 245 BRCA1 and 217 BRCA2) were screened and 50 breast cancers were diagnosed (38 invasive, 12 ductal carcinoma in situ [DCIS]). Density was not measured in 40 women of whom four developed cancer (three invasive, one DCIS). Mean PD (+/- standard deviation [SD]) for 376 women who did not develop breast cancer was 34% (23) compared with 31% (21) for 46 women with cancer (P = .51). Logistic regression model of breast cancer incidence and PD revealed an odds ratio of 0.99 (+/- 0.01 SD) for a one-unit increase in PD (P = .44). Results remained nonsignificant in multivariate analysis, as well as when women with pure DCIS were excluded. CONCLUSION: Increased mammographic breast density is not associated with higher breast cancer incidence in women with BRCA mutations. On the basis of these findings, density should not be considered a factor for these women in decision making regarding prophylactic surgery or chemoprevention.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".