Impact of Lymph Node Dissection for Patients With Clinically Node-Negative Intrahepatic Cholangiocarcinoma: A Multicenter Cohort Study
Bibliographic record
Abstract
Background: Lymph node status is a prominent prognostic factor for intrahepatic cholangiocarcinoma (ICC). However, the prognostic value of performing lymph node dissection (LND) in patients with clinical node-negative ICC remains controversial. The aim of this study was to evaluate the clinical value of LND on long-term outcomes in this subgroup of patients. Methods: We retrospectively analyzed patients who underwent radical liver resection for clinically node-negative ICC from three tertiary hepatobiliary centers. The propensity score matching analysis at 1:1 ratio based on clinicopathological data was conducted between patients with and without LND. Recurrence-free survival (RFS) and overall survival (OS) were compared in the matched cohort. Results: Among 303 patients who underwent radical liver resection for ICC, 48 patients with clinically positive nodes were excluded, and a total of 159 clinically node-negative ICC patients were finally eligible for the study, with 102 in the LND group and 57 in the non-LND group. After propensity score matching, two well-balanced groups of 51 patients each were analyzed. No significant difference of median RFS (12.0 vs. 10.0 months, P = 0.37) and median OS (22.0 vs. 26.0 months, P = 0.47) was observed between the LND and non-LND group. Also, LND was not identified as one of the independent risks for survival. Among 51 patients who received LND, 11 patients were with positive lymph nodes (lymph node metastasis (LNM) (+)) and presented significantly worse outcomes than those with LND (-). On the other hand, postoperative adjuvant therapy was the independent risk factor for both RFS (hazard ratio (HR): 0.623, 95% confidence interval (CI): 0.393 - 0.987, P = 0.044) and OS (HR: 0.585, 95% CI: 0.359 - 0.952, P = 0.031). Furthermore, postoperative adjuvant therapy was associated with prolonged survivals of non-LND patients (P = 0.02 for RFS and P = 0.03 for OS). Conclusions: Based on the data, we found that LND did not significantly improve the prognosis of patients with clinically node-negative ICC. Postoperative adjuvant therapy was associated with prolonged survival of ICC patients, especially in non-LND individuals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".