Temporal changes in mammographic breast density and breast cancer risk among women with benign breast disease
Bibliographic record
Abstract
INTRODUCTION: Benign breast disease (BBD) and high mammographic breast density (MBD) are prevalent and independent risk factors for invasive breast cancer. It has been suggested that temporal changes in MBD may impact future invasive breast cancer risk, but this has not been studied among women with BBD. METHODS: We undertook a nested case-control study within a cohort of 15,395 women with BBD in Kaiser Permanente Northwest (KPNW; 1970-2012, followed through mid-2015). Cases (n = 261) developed invasive breast cancer > 1 year after BBD diagnosis, whereas controls (n = 249) did not have breast cancer by the case diagnosis date. Cases and controls were individually matched on BBD diagnosis age and plan membership duration. Standardized %MBD change (per 2 years), categorized as stable/any increase (≥ 0%), minimal decrease of less than 5% or a decrease greater than or equal to 5%, was determined from baseline and follow-up mammograms. Associations between MBD change and breast cancer risk were examined using adjusted unconditional logistic regression. RESULTS: Overall, 64.5% (n = 329) of BBD patients had non-proliferative and 35.5% (n = 181) had proliferative disease with/without atypia. Women with an MBD decrease (≤ - 5%) were less likely to develop breast cancer (Odds Ratio (OR) 0.64; 95% Confidence Interval (CI) 0.38, 1.07) compared with women with minimal decreases. Associations were stronger among women ≥ 50 years at BBD diagnosis (OR 0.48; 95% CI 0.25, 0.92) and with proliferative BBD (OR 0.32; 95% CI 0.11, 0.99). DISCUSSION: Assessment of temporal MBD changes may inform risk monitoring among women with BBD, and strategies to actively reduce MBD may help decrease future breast cancer risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".