The association between breast density and HER2-positive breast cancer: A population-based case-control study.
Bibliographic record
Abstract
e12556 Background: Little is known about the association between mammographic breast density and the subtypes of breast cancer including HER2-positive breast cancers (HER2-BrCa). The objective of this study was to assess the strength of association between breast density and HER2-BrCa in a population-based screening program. Methods: This is a population-based case-control breast cancer study of women aged 40 to 75 who underwent digital breast screening from 2009 to 2015 in Nova Scotia, Canada. Cases included women diagnosed with HER2-BrCa at screen or before their next screen (interval); controls included women without screen-detected cancer matched to cases by age and year of screen. Measures of mammographic breast density (percent density, BI-RADS-4th and -5th edition) were obtained from automated software (densitasai) and linked with clinical risk factor data (age, parity, total breast volume, post-menopausal status, hormone replacement therapy, family history and history of core biopsy). The association between breast density and cancer risk was assessed by calculating the odds ratios [OR] with 95% confidence intervals using multivariable logistic regression. Results: A total of 209 cases (median age, 58.8 years) and 6812 controls (median age, 59.4 years) were included. The risk of HER2-BrCa increased with increasing levels of percent breast density. High breast density according to BIRADS-4th and -5th editions was significantly associated with HER2-BrCa: BIRADS -4th 3/4 vs 1: OR 2.50 (1.68 - 3.68); BIRADS-5th C/D vs A: OR 2.58 (1.71 - 4.01). The association between higher breast density and increased risk of HER2-BrCa remained after adjustment for clinical factors. Conclusions: The risk of HER2-BrCa was associated with progressively higher mammographic breast density, although to a lesser extent than breast cancer in general. Accurate risk models including breast density may support the development of more breast-screening protocols that can lead to more strategic use of healthcare resources.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".