Comparative efficacy of neoadjuvant to adjuvant chemotherapy for the treatment of early-stage HER2 negative breast cancer: A population-based analysis.
Bibliographic record
Abstract
e12100 Background: The use of neoadjuvant treatment has increased over the past decade due to its ability to assess tumour sensitivity to systemic treatment in vivo, and to downstage women for increased breast conserving surgery. Recent studies have stratified patients with residual disease to receive additional treatment, which has resulted in meaningful improvements in survival. However, meta-analysis data suggest similar long-term outcomes for patients treated with neoadjuvant chemotherapy (NACT) compared to adjuvant chemotherapy (ACT) in historical randomized trials. The comparative efficacy in a real-world setting utilizing modern chemotherapy regimens is unknown. Methods: A retrospective review of the BC Cancer Breast Cancer Outcomes Unit (BCOU) was performed to identify patients with stage I-III HER-2 negative breast cancer treated with chemotherapy at the BC Cancer Agency from 2005-2010. Patients were then divided into 2 groups: those who received neoadjuvant chemotherapy (NACT) and those who received adjuvant chemotherapy (ACT). A matched analysis (age, stage, subtype) for patients treated with NACT vs ACT (matched 1:3) was then performed using a propensity scoring method to compare distant disease-free survival (DDFS), breast cancer specific survival (BCSS) and overall survival (OS). No patients received adjuvant chemotherapy for residual disease after NACT. Results: A total of 656 patients met the inclusion criteria, consisting of 164 NACT and 492 ACT cases. Median age was 49 years (37-68) in the NACT group vs 49 (37-65) in the ACT group (p = 0.71). The majority had stage 3 disease, 64% in both groups (p = 1.0). Most were hormone receptor positive (HR+), 67.1% vs 70.7% in the NACT vs ACT groups, respectively (p = 0.41). 5-year DDFS was 75% with NACT (95%CI 67, 82) and 77% with ACT (95%CI 72, 81), p = 0.87. 5-year OS for patients treated with NACT was 77% (95%CI 71, 84) and 80% (95%CI 75, 85) for patients treated with ACT, p = 0.33. 5-year BCSS was 80% with NACT (95% CI 70, 86) and 82% (95%CI 77, 86) with ACT, p = 0.75. Multivariate analysis for tumour size, nodal involvement and subtype are ongoing. Conclusions: The use of NACT compared to ACT in a population-based setting did not result in significant differences in DDFS, OS or BCSS. Acknowledging the comparative efficacy of these approaches will be informative to determine if the addition of subsequent adjuvant treatment for patients with residual disease after NACT will lead to differential benefits in a population-based setting.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".