Prevalence and risk factors for lymph node metastasis in duodenal neuroendocrine tumors: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Although the status of lymph node metastasis (LNM) is crucial in determining treatment strategy for duodenal neuroendocrine tumors (D-NETs), robust evidence for their potential LNM risk remains lacking. This systematic review aimed to summarize the prevalence and risk factors of LNM in D-NETs. METHODS: This systematic review of electronic databases identified eligible case-control and cohort studies for D-NET resected either endoscopically or surgically, published from 1990 to 2023. The primary outcome was the pooled prevalence of LNM in D-NETs. Secondary outcomes included the pooled prevalence of LNM according to tumor location and functionality, as well as identifying pathological risk factors for LNM. Meta-analysis was performed. RESULTS: We identified 36 studies that involved 1,396 patients with D-NETs, including 326 with LNM. The pooled prevalence of LNM in D-NETs was 22.7% (95% confidence interval [CI] 17.3-29.2%). The prevalence was high in ampullary/peri-ampullary D-NETs and functional D-NETs (46.8 and 53.3%, respectively), whereas it was low in non-functional, non-ampullary D-NETs (NAD-NETs) (9.5%). Pathological risk factors for LNM in NAD-NETs included tumor size > 10 mm (odds ratio [OR] 7.31 [95% CI 3.28-16.31]), tumor invasion into the muscularis propria or deeper (OR 7.79 [3.65-16.61]), lymphovascular invasion (OR 5.67 [2.29-14.06]), and World Health Organization grading of G2 (OR 2.47 [1.03-5.92]). CONCLUSION: Approximately one-fourth of the patients with D-NETs had LNM. Endoscopic resection might be acceptable for non-functional NAD-NETs with diameters of 10 mm or less, but additional surgical resection with lymphadenectomy may be recommended for cases exhibiting pathological risk factors.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".