Trends and outcomes in patients receiving neoadjuvant chemotherapy for breast cancer in Ontario: A population-based study.
Bibliographic record
Abstract
e12664 Background: Modern neoadjuvant chemotherapy (NAC) regimens in breast cancer offer higher rates of pathologic complete response, an ability to guide further adjuvant treatment, and may de-escalate surgery. However, the utilization of NAC has been heterogeneous in Ontario. This study aimed to describe trends in NAC use in Ontario, report survival outcomes, and explore factors associated with overall survival in these patients. Methods: This was a population-based cohort study using linked health administrative data in Ontario, Canada. From the Ontario Cancer Registry, we identified women ≥18 years who underwent neoadjuvant chemotherapy followed by surgery for operable (cT1N1, cT2-3N0, or cT2-3N1) breast cancer between 2012 and 2020. Patients were divided by receptor subtype (triple negative breast cancer [TNBC], ER+/HER2-, ER+/HER2+, ER-/HER2+) and characteristics were compared using standardized mean differences (SMDs). Five-year overall survival (OS) and breast cancer-specific survival (BCSS) were determined and reported for each subtype. Associations with OS and BCSS were calculated for patient and disease characteristics using univariate and multivariable Cox proportional hazards models and Fine and Gray models, respectively. Results: 3,804 women underwent NAC for cT1N1, cT2-3N0, or cT2-3N1 breast cancer in Ontario between 2012 and 2020. The largest contributing subtype was ER+/HER2- patients (39.3%) followed by those with HER2+ disease (ER-/HER2+ 13.8%, ER+/HER2+ 23.4%), and TNBC (23.5%). The median age was 50 years (IQR 42-59 years). Most patients were clinically node-positive (2,736; 71.9%), and underwent mastectomy (2,397; 63.0%). There was a significant increase in the number of patients receiving NAC in Ontario, from 217 in 2012 to 667 in 2019 ( p<0.001), with a greater increase among patients with TNBC or HER2+ disease. Compared to those with ER+/HER2- disease, TNBC patients were treated for smaller tumours (28.6% T3 vs. 39.8%; SMD = 0.24), and were more likely to be node-negative (39.1% N0 vs. 22.1%; SMD = 0.38). Similar trends for nodal status were found for patients with ER-/HER2+ (26.7% N0) and ER+/HER2+ cancer (27.9% N0). 5-year OS for the entire cohort was 88.1% (95% CI 87.1 – 89.2%). Survival was highest for ER+/HER2+ patients (94.2%, 95% CI 92.6 – 95.8%) and lowest for TNBC patients (80.1%, 95% CI 77.5 – 82.8%). Similar patterns were seen for BCSS. Factors associated with OS in a multivariable model included older age (increase in 5 years HR 1.1, 95% CI 1.07 – 1.14), N1 status (HR 1.99, 95% CI 1.61 – 2.47), larger tumour size (T3 HR 1.83, 95% CI 1.34 – 2.5), mastectomy (HR 1.45, 95% CI 1.2 – 1.75), and TNBC (versus ER+/HER2- HR 1.9, 95% CI 1.56 – 2.32). Conclusions: The use of NAC has increased in Ontario, particularly among TNBC and HER2+ patients. Women with TNBC have worse outcomes compared to those with ER+/HER2- disease, despite being treated for less advanced disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".