Breast-Conserving Surgery vs Mastectomy for Non-metastatic Breast Cancer: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Breast cancer is a leading cause of cancer-related deaths among women worldwide, accounting for 15% of all cancer deaths. The decision between breast-conserving surgery (BCS) and mastectomy (MX) plays a fundamental role in the management of early-stage breast cancer. Improved survival rates are an essential outcome influencing these decisions. This systematic review and meta-analysis aim to compare the effectiveness of BCS versus MX in terms of survival and recurrence outcomes. We conducted a comprehensive search of PubMed MEDLINE, Web of Science, Cochrane, and EMBASE databases on 03/19/2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Studies published from 1994 to 2024 comparing BCS with MX in breast cancer patients were included. The meta-analysis included 22 studies with a total sample size of 389,465 participants. For local recurrence, the random-effects model indicated an odds ratio (OR) of 1.65 (95% CI: 0.79-3.47, p = 0.19, I² = 70.6%). Regional recurrence showed an OR of 1.13 (95% CI: 0.26-5.03, p = 0.87, I² = 83.8%). For disease-free survival at five years, the hazard ratio (HR) was 0.78 (95% CI: 0.57-1.09, p = 0.14, I² = 98.2%), and at 10 years, the HR was 1.12 (95% CI: 0.79-1.58, p = 0.53, I² = 96.4%). Overall survival (OS) at five years showed a significant benefit for BCS (HR = 0.49, 95% CI: 0.34-0.71, p = 0.0001, I² = 98.8%), as did OS at 10 years (HR = 0.62, 95% CI: 0.42-0.91, p = 0.0149, I² = 98.8%). High heterogeneity was present in the survival outcomes (I² > 90%), limiting the robustness of the findings and suggesting variability across studies. BCS may offer comparable or superior survival outcomes compared to MX, but further research is needed to address the substantial heterogeneity and to develop personalized treatment guidelines. Notably, all 22 cohort studies were rated "good quality" on the Newcastle-Ottawa scale (NOS), supporting the overall reliability of the evidence.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 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".