What is the impact of perineural invasion on the prognosis of cervical cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Perineural Invasion (PNI) is a marker of a highly invasive tumor with poor prognosis, but the real influence on the prognosis of cervical cancer is still debated. We aimed to systematically investigate the prognostic impact of PNI in cervical cancer. We searched PubMed, Embase, Cochrane databases, and ClinicalTrials.gov from inception to 20 April 2024. Cohort, case–control, and randomized controlled studies reporting the PNI status and survival outcomes of women with cervical cancer were included. Two reviewers extracted data independently and appraised study quality following the PRISMA guideline. The quality of the studies was assessed with Newcastle–Ottawa Scale. Random effect model was used if the heterogeneity was significant ( P ≤ 0.1, I 2 ≥ 50%). We included seven retrospective cohort studies (1561 women) in the analysis. PNI was remarkably associated with a worse survival (risk ratio [95% CI]: 2.79 [1.67- 4.66], I 2 = 78% for 5-year overall survival (OS); 2.16 [1.30–3.59], I 2 = 84% for 5-year disease-free survival (DFS)). After multivariate cox regression adjustment, the hazard ratio [95% CI] of PNI was 3.25 [1.09, 9.74] ( I 2 = 85%) for OS, and 2.50 [0.66, 9.46] ( I 2 = 89%) for DFS. PNI showed positive correlation with higher stage, larger tumor size, lymph node metastasis, deep stromal invasion, lymphovascular invasion, resection margin involvement, and parametrial invasion ( P < 0.05). Besides, PNI was associated with higher possibility of adjuvant therapy (risk difference [95% CI]: 0.28 [0.04–0.52], I 2 = 92%), especially for chemoradiation (0.25 [-0.02–0.53], I 2 = 76%). Subgroup analysis showed patients with PNI had poorer prognosis than those without PNI in patients with LNM or large tumor size ( P < 0.05). PNI demonstrated a significant association with reduced overall survival in cervical cancer patients and emerged as a potential independent prognostic indicator, which provided a foundation for future investigations to evaluate the clinical utility of PNI status in guiding therapeutic strategies. The protocol for this study was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under identifying number CRD42022315970.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".