Prognostic impact of perineural invasion in intrahepatic cholangiocarcinoma: multicentre study
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the prognostic impact of perineural invasion (PNI) on tumour recurrence and survival among patients with resected intrahepatic cholangiocarcinoma (ICC). METHODS: This was a multicentre, retrospective study of patients who underwent resection with curative intent for ICC between 2000 and 2017. The relationship between PNI, clinicopathological characteristics, and long-term survival was analysed in the overall cohort and the subset of patients with early-stage ICC. RESULTS: Among 1095 patients who underwent resection of ICC, PNI was present in 239 (21.8 per cent). In univariable analysis, PNI was associated with worse disease-free survival (DFS) (median 13.2 versus 16.1 months for patients with and without PNI respectively; P = 0.038) and overall survival (OS) (26.4 versus 41.5 months; P < 0.001). In multivariable analysis, PNI was an independent risk factor associated with reduced DFS (hazard ratio (HR) 1.56, 95 per cent c.i. 1.06 to 2.13; P = 0.019) and OS (HR 1.74, 1.16 to 2.60; P = 0.007). In subgroup analysis of patients with early-stage disease (AJCC T1-2, 981 patients; or N0, 249 patients), PNI remained associated with worse DFS (T1-2: median 13.7 versus 16.6 months in patients with and without PNI respectively, P = 0.019; N0: 11.7 versus 17.5 months, P = 0.022) and OS (T1-2: 28.5 versus 45.7 months, P < 0.001; N0: 34.9 versus 47.5 months, P = 0.036). CONCLUSION: PNI is a strong independent predictor of tumour recurrence and long-term survival following resection of ICC with curative intent, even among patients with early-stage disease. The presence of PNI should be assessed routinely.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".