Is There a Difference in the Association between Percent Mammographic Density and Subtypes of Breast Cancer? Luminal A and Triple-Negative Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Mammographic density is a potentially modifiable risk factor for breast cancer. To what extent mammographic density is a predictor for both hormone receptor-positive and hormone receptor-negative tumors is unclear. Even less is known about whether mammographic density predicts subtypes of breast cancer defined by expression status of the three receptors: estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER-2). METHODS: We estimated the association of percent mammographic density with subtypes of invasive breast cancer among 479 population-based female breast cancer patients and 376 control subjects ages 35 to 64 years. The expression status of ER, PR, and HER-2 was assessed using immunohistochemistry methods in a single laboratory. We considered ER+ or PR+ plus HER-2- tumors as luminal A breast cancer and ER-/PR-/HER-2- tumors as triple-negative breast cancer. We used unconditional logistic regression methods to estimate odd ratios (95% confidence intervals) for both case-control and case-case comparisons. RESULTS: Mammographic density was associated with increased risk of both invasive breast cancer subtypes, luminal A and triple-negative, in the case-control analysis. Results from case-case comparisons yielded no differences between the two subtypes among all women combined or in analyses done separately by race (White versus African American women) or menopausal status (premenopausal versus postmenopausal women; all P values > 0.05). CONCLUSIONS: Our results suggest that percent mammographic density is positively associated with both luminal A and triple-negative breast cancer.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".