Well-Differentiated Jejunoileal Neuroendocrine Tumors and Corresponding Liver Metastases: Mesenteric Fibrogenesis and Extramural Vascular Invasion in Tumor Progression
Bibliographic record
Abstract
Background: Patients with jejunoileal neuroendocrine tumors (JINETs) can live for many years despite liver metastases. Evidence suggests that tumor heterogeneity is prognostically important, hence the selection of Ki67 hotspots for tumor grading. According to the stepwise metastasis model, clonal hotspots should predominate in the metastases. However, an alternative view holds that the polyclonality of metastases is consistent with origin from genetically heterogeneous clusters of disseminated cells. The shortcomings of Ki67 grading are also being recognized, thus renewing the search for other prognostic parameters. Methods: A 20-year retrospective study that paired JINETs and hepatic metastases was conducted by analyzing them for various parameters. Results: There were 43 patients (mean follow-up of 7.234 years); 14 were dead due to the disease, 22 were alive with the disease, and 7 were alive with no evidence of the disease. Most JI NETs (22/30) were grade 1, eight were grade 2, and none were grade 3. Tumor grades for both the primaries and liver metastases were not prognostic (p-values = 0.1260 and 0.2566, respectively). Seventeen of the 41 JI NETs showed mesenteric fibrogenesis (MF), and 18 had EMVI, with a high level of agreement between these parameters (92.68%) (kappa value 0.85), and both were strongly associated with poor outcomes. Conclusions: JINETs and their liver metastases tend to have low proliferation rates. However, an important mechanism in the metastatic cascade appears to be mesenteric fibrogenesis. It encases vessels, which enhances extramural vascular invasion, thereby conveying clusters of tumor cells to the liver. This supports the polyclonal nature of tumor progression rather than origin from hotspot aberrant clones.
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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 | 0.001 | 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) | 0.002 | 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".