Lymph node dissection in clinically node-negative intrahepatic cholangiocarcinoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Lymph node dissection (LND) in clinically node-negative (cN0) intrahepatic cholangiocarcinoma (iCCA) remains controversial. While LND improves staging accuracy, its survival benefit in cN0-iCCA is uncertain. METHODS: This systematic review and meta-analysis followed PRISMA 2020 guidelines (PROSPERO registration: CRD420251050907). PubMed, Scopus, and Cochrane Library were searched through May 2025 for studies comparing curative-intent liver resection with or without LND in cN0-iCCA. Risk of bias was assessed using the Newcastle-Ottawa Scale, and meta-analysis was conducted using a random-effects model. RESULTS: Five retrospective studies comprising 1,290 patients (507 LND, 783 non-LND) were included. The pooled analysis showed a non-significant trend toward improved OS in the LND group (HR = 0.80, 95% CI: 0.56-1.15; I² = 77.17%). Subgroup analysis including only low-risk-of-bias studies (Newcastle-Ottawa Scale ≥ 7) demonstrated a significant survival benefit (HR = 0.71, 95% CI: 0.52-0.98; I² = 61.09%). For DFS, the pooled HR was 0.93 (95% CI: 0.63-1.38; I² = 74.77%). In the low-risk-of-bias subgroup, a trend toward improved DFS was observed (HR = 0.81, 95% CI: 0.51-1.28; I² = 67.29%), though not statistically significant. LND did not increase major postoperative complications (OR = 0.97, 95% CI: 0.68-1.38). However, considerable heterogeneity was observed across included studies (I² >70%), reflecting differences in patient selection, surgical extent, and definitions of clinically node-negative disease; therefore, these results should be interpreted with caution. CONCLUSION: LND may improve survival in selected patients with cN0-iCCA without increasing postoperative risk. However, the evidence is limited by retrospective design and heterogeneity. Prospective studies are warranted to confirm these findings. No funding was received.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.022 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".