The Landscape of Breast Cancer Molecular and Histologic Subtypes in Canada
Bibliographic record
Abstract
Purpose: To characterize the histologic and molecular subtype distribution of, and survival from, breast cancer (BC) among Canadian women overall, and by stage and age at diagnosis. Methods: Invasive BC cases from the Canadian Cancer Registry for women aged 15–99 years between 2012 and 2017 in Canada, excluding Quebec, were examined using pre-existing mortality linkages. Stage at diagnosis, molecular, and histologic subtypes, and 5-year net survival (NS) by age, subtype, and stage were determined. Results: 107,271 women with BC were included. Luminal A was the most common subtype, present in increasing proportions as women aged, up to a maximum of 55% of cases in 70–74. Ductal and luminal A were most likely to be diagnosed at stage I, while HER2+ had the highest proportion of diagnosis at stage III; triple negative (TN) and unknown had the highest proportion of stage IV. For all stages combined, luminal A had a five-year NS of 98%, while TN was 74%. NS for stage I BC was 99–100% for all subtypes, excepting TN, which was 96%. Survival decreased with advancing stage, most markedly for TN, for which stage III was 47% and stage IV 7%. Survival by equivalent stage and subtype was comparable across age groups but declined in older age categories. Conclusions: The varying natural histories of BC subtypes and histologies can inform prognoses, health system economics, and screening practices. The NS of 96% or greater for stage I, regardless of subtype, highlights the importance of early detection for all subtypes of BC, especially in aggressive subtypes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".