Lympho-vascular invasion impacts the prognosis in breast-conserving surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: It is estimated that breast cancer (BC) incidence, especially that of early-stage breast cancer cases continues to rise due to increased universal screening. Breast-conserving surgery (BCS) is the main intervention for early-stage BC. Lympho-vascular invasion (LVI) is reported to influence breast cancer prognosis but its prognostic value in breast-conserving treatment is controversial. METHODS: , 2021, without language restrictions, to identify studies that explored the prognosis of lympho-vascular invasion in breast-conserving surgery. Reviews of each study were conducted, and data extracted. The meta-analysis was performed with StataSE 16. Study quality assessment was evaluated using the Newcastle-Ottawa Scale. RESULTS: Overall, 15 studies with 21,704 patients deemed eligible for this study. Event-free survival (EFS), disease-free survival (DFS), overall survival (OS), distant metastases (DM), loco-regional recurrence (LRR), local recurrence (LR), breast recurrence (BR), disease specific survival (DSS), and breast cancer specific survival (BCSS), were extracted from each study. We found that LVI leads to poor OS (HR = 1.46, 95% CI: 1.17-1.83), DM (HR = 2.08, 95% CI: 1.66-2.60) and LR (HR = 2.00, 95% CI: 1.54-2.61). CONCLUSIONS: We confirmed that early-stage BC patients with LVI-positive have poorer OS, DFS, LRR, BCSS, DM and LR following receiving BCS than those LVI-negative patients. Mastectomy, in combination with radical systemic therapies could be considered, especially in those requiring second surgery. How to change the impact of LVI on the local recurrence rate and long-term survival in patients who undergo BCS may be a valuable research direction in the future.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".