Wolfe's parenchymal pattern and percentage of the breast with mammographic densities: redundant or complementary classifications?
Bibliographic record
Abstract
Mammographic breast densities are one of the strongest breast cancer risk factors. The two most frequently used classifications of breast densities are Wolfe's parenchymal pattern and the percentage of the breast with densities. In this analysis, associations of these two classifications with breast cancer risk were compared, and the dose response curve of risk with densities was examined. Three case-control studies were combined totaling 1060 cases with newly diagnosed breast cancer and 2352 controls. A single observer had assessed parenchymal pattern and percent density without any information on subjects. Relative risks (RRs) were estimated with logistic regression and spline functions adjusting for age and body weight. The two classifications were strongly correlated (r = 0.81, P = 0.0001). Breast cancer risk increased progressively with percent density reaching a 5-6-fold increase for women with 85% or more of the breast with densities compared with women with no density. In contrast, women with P2 or DY patterns had only a 2-3-fold increase in risk compared with women with N1 pattern. More importantly, among women with P2 or DY, RR varied substantially with percent density, whereas, among women with a given percent density, RR varied little with parenchymal pattern. Comparisons of multivariate models reveal that in the presence of parenchymal pattern, inclusion of percent density in the model improved the prediction of breast cancer risk (chi(2) = 35.5, P = 0.0082) but not the opposite (chi(2) = 1.1, P = 0.7662). These findings show that the percentage of the breast with densities provide more information on breast cancer risk than Wolfe's parenchymal patterns and that, once percent breast density is taken into account, no more information on breast cancer risk is given by assessing parenchymal pattern.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".