MRI background parenchymal enhancement, breast density and breast cancer risk factors: A cross-sectional study in pre- and post-menopausal women.
Bibliographic record
Abstract
<title>Abstract</title> <bold>Background: </bold>Histologically normal breast fibroglandular tissue (FGT) enhances on contrast MRI and is called background parenchymal enhancement (BPE). Having high BPE is associated with an increased risk of breast cancer. We examined the relationship between MRI-FGT (a volumetric assessment of breast density) and BPE and breast cancer risk factors.<bold>Methods: </bold>This was a cross-sectional study of 419 women without breast cancer undergoing contrast-enhanced breast MRI. All women completed a questionnaire at the time of MRI. Prevalence ratios (PR) and 95% confidence intervals (CI) describing the relationship between breast cancer risk factors and BPE and MRI-FGT were generated using modified Poisson regression. <bold>Results: </bold>In multivariable adjusted models a positive association between BMI and BPE was observed, with a 5-unit increase in BMI associated with a 16% and 38% increase in prevalence of high BPE in pre- and post-menopausal women respectively. Conversely, a strong inverse relationship between BMI and MRI-FGT was observed in both pre- (PR=0.65, 95% CI 0.57, 0.76 per 5-unit increase of BMI) and post-menopausal (PR=0.67, 95% CI 0.57, 0.79, per 5-unit increase in BMI) women. Current use of oral contraceptives was associated with high BPE while use of preventive medication (e.g., tamoxifen) was associated with low BPE. <bold>Conclusion: </bold>This study identifies patient characteristics and exposures associated with BPE and MRI-FGT. BPE is a new imaging marker of breast cancer risk. The results of this study provide further support for the role of hormonal exposures on BPE.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".