Locoregional therapy in de novo metastatic breast cancer: Systemic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Locoregional therapy (LRT) in de novo metastatic disease is controversial with inconsistent results from randomized control trials (RCTs). METHODS: RCTs comparing LRT and systemic therapy to standard therapy alone in de novo metastatic breast cancer were identified. Hazard ratios (HRs) and their associated 95% confidence intervals (CIs) were computed and pooled in a meta-analysis using generic inverse variance. Overall survival (OS) and time to locoregional progression data were extracted for the intention to treat (ITT) population. Data on OS for pre-specified subgroups defined by tumor subtype and by site of metastases were also extracted. RESULTS: Analyses included 4 trials comprising 970 patients. LRT included standard surgery to the primary breast tumor in all studies, and adjuvant radiation per standard of care was required in 3 studies. Compared to standard treatment, LRT was not associated with improved OS in the ITT population (HR 0.97, 95% CI 0.72-1.29, p = 0.81). However, LRT was associated with improved time to locoregional progression (HR 0.36, 95% CI 0.14-0.95, p = 0.04). LRT was not associated with improved OS in any tumor subtypes, including hormone receptor positive (HR 0.96, 95% CI 0.65-1.43), triple negative (HR 1.4, 95% CI 0.50-3.91) and human epidermal growth factor receptor 2 positive disease (HR 0.93, 95% CI 0.68-1.28). Additionally, LRT did not improve OS in bone only disease (HR 0.97, 95% CI 0.58-1.62) and in visceral disease (HR = 1.02, 95% CI 0.77-1.35). Our critical appraisal has identified some methodological problems in the design and conduct of the studies included that could affect the meta-analysis result. CONCLUSIONS: LRT in de novo metastatic breast cancer is not associated with improved OS. Results are consistent among different breast cancer subgroups. However, this conclusion should be interpreted with caution in view of the limitations identified in meta-analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".