Number and Station of Lymph Node Metastasis After Curative-intent Resection of Intrahepatic Cholangiocarcinoma Impact Prognosis
Bibliographic record
Abstract
OBJECTIVES: To determine the prognostic implication of the number and station of LNM, and the minimal number of LNs needed for evaluation to accurately stage patients with intrahepatic cholangiocarcinoma (ICC). BACKGROUND: Impact of the number and station of LNM on long-term survival, and the minimal number of LNs needed for accurate staging of ICC patients remain poorly defined. METHODS: Data on patients who underwent curative-intent resection for ICC was collected from 15 high-volume centers worldwide. External validation was performed using the SEER registry. Primary outcomes included overall (OS), disease-specific, and recurrence-free survival. RESULTS: Among 603 patients who underwent curative-intent resection, median and 5-year OS were 30.6 months and 30.4%. Patients with 1 or 2 LNM had comparable worse OS versus patients with no nodal disease (median OS, 1 LNM 18.0, 2 LNM 20.0 vs no LNM 45.0 months, both P < 0.001), yet better OS versus patients with 3 or more LNM (median OS, 1-2 LNM 19.8 vs ≥3 LNM 16.0 months, P < 0.01). On multivariable analysis, a proposed new nodal staging with N1 (1-2 LNM) (Ref. N0, HR 2.40, P < 0.001) and N2 (≥3 LNM) [Ref. N0, hazard ratio (HR) 3.85, P < 0.001] categories were independently associated with incrementally worse OS. Patients with no nodal metastasis, 1-2 LNM and ≥3 LNM also had an increasingly worse disease-specific survival, and recurrence-free survival (both P < 0.05). Total number of LNs examined ≥6 had the greatest discriminatory power relative to OS among patients with 1-2 LNM, and patients with ≥3 LNM in both the multi-institutional (area under the curve 0.780) and SEER database (area under the curve 0.820) (n = 1036). Among patients who underwent an adequate regional lymphadenectomy (total number of LNs examined ≥6), LNM beyond the HDL was associated with worse OS versus LNM within the HDL only (median OS, 14.0 vs 24.0 months, HR 2.41, P = 0.003). CONCLUSION: Standard lymphadenectomy of at least 6 LNs is strongly recommended and should include examination beyond station 12 to have the greatest chance of accurate staging. The proposed new nodal staging of N0, N1, and N2 should be considered to stratify outcomes among patients after curative-intent resection of ICC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".