Do all patients with HER2-positive breast cancer require one year of adjuvant trastuzumab?: A systematic review and meta-analysis.
Bibliographic record
Abstract
522 Background: One year of adjuvant trastuzumab (T) remains the standard treatment for patients with HER2 positive breast cancer. Results from randomized trials with diverse non-inferiority margins comparing one year to a shorter duration of adjuvant T were not consistent, particularly with the PERSEPHONE and the final PHARE and Short-HER trials’ results. Our objective was to conduct a systematic review and meta-analysis of randomized trials in patients with HER2 positive breast cancer to assess whether a shorter duration of adjuvant T was non-inferior to one year of treatment. Methods: PubMed, EMBASE and The Cochrane Library were searched for eligible randomized trials. Hazard ratios (HR) for disease free and overall survival (DFS, OS) were weighted using generic inverse variance and pooled in a meta-analysis using random-effects models. The median of non-inferiority margins derived from each trial was calculated to set a non-inferiority margin of 1.29 for the pooled analysis. Subgroup analyses compared survival outcomes by estrogen receptor (ER) status, nodal status, length and timing of trastuzumab treatment. Results: Data of 11,376 patients from 5 trials were analyzed. A shorter duration of T was non-inferior to one year of therapy for DFS (HR 1.13, 95%CI 1.03-1.24) but worse for OS (HR 1.16, 95%CI 1.01-1.32). In addition, the non-inferiority for DFS was met for patients with ER positive disease (HR 1.1, 95%CI 0.95-1.28) and patients treated with 6 months (HR 1.09, 95%CI 0.98-1.22) or sequential T (HR 0.97, 95%CI 0.75-1.27). Conversely, the non-inferiority for DFS was not met for patients with ER negative disease (HR 1.22, 95%CI 1.06-1.41), patients treated with 9 weeks (HR 1.26, 95%CI 1.02-1.55) or concomitant T (HR 1.25, 95%CI 1.07-1.45) and patients with node negative (HR 1.12, 95% 0.93-1.35) or positive (HR 1.16, 95%CI 0.99-1.36) disease. Conclusions: Within the limitations of the available data and the different non-inferiority margins used in randomized trials, a shorter duration of adjuvant T is non-inferior to one year of therapy for DFS in patients with HER2 positive breast cancer, particularly in patients with ER positive disease. Further trials with appropriately chosen non-inferiority margins are needed to confirm the optimal duration of T in patients with low-risk 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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".