Breast cancer subtype and screening sensitivity in the Quebec Mammography Screening Program
Bibliographic record
Abstract
Objective In mammography screening, interval cancers present a problem. The metric ‘screening sensitivity’ monitors both how well a programme detects cancers and avoids interval cancers. To our knowledge, the effect of breast cancer surrogate molecular subtypes on screening sensitivity has never been evaluated. We aimed to measure the 2-year screening sensitivity according to breast cancer subtypes. Methods We studied 734 women with an invasive breast cancer diagnosed between 2003 and 2007 after participating in one regional division of Quebec’s Mammography Screening Program. They represented 83% of all participating women with an invasive BC diagnosis in that region for that period. Tumours were categorized into ‘luminal A-like’, ‘luminal B-like’, ‘triple-negative’ and ‘HER2-positive’ subtypes. We used logistic regression and marginal standardization to estimate screening sensitivity, sensitivity ratios (SR) and sensitivity differences. We also assessed the mediating effect of grade. Results Adjusted 2-year screening sensitivity was 75.4% in luminal A-like, 66.1% in luminal B-like, 52.9% in triple-negative and 45.3% in HER2-positive, translating into sensitivity ratios of 0.88 (95% confidence interval [CI] = 0.78–0.98) for luminal B-like, 0.70 (CI = 0.56–0.88) for triple-negative and 0.60 (CI = 0.39–0.93) for HER2-positive, when compared with luminal A-like. Grade entirely mediated the subtype-sensitivity association for triple negative and mediated it partly for HER2-positive. Screening round (prevalent vs. incident) did not modify results. Conclusion There was substantial variation in screening sensitivity according to breast cancer subtypes. Aggressive phenotypes showed the lowest sensitivity, an effect that was mediated by grade. Tailoring screening according to women’s subtype risk factors might eventually lead to more efficient programs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".