Prognostic Evaluations Tailored to Specific Gastric Neuroendocrine Neoplasms: Analysis Of 200 Cases with Extended Follow-Up
Bibliographic record
Abstract
BACKGROUND: Gastric neuroendocrine neoplasms (NENs) are very heterogeneous, ranging from mostly indolent, atrophic gastritis-associated, type I neuroendocrine tumors (NETs), through highly malignant, poorly differentiated neuroendocrine carcinomas (pdNECs), to sporadic type III NETs with intermediate prognosis, and various rare tumor types. Histologic differentiation, proliferative grade, size, level of gastric wall invasion, and local or distant metastases are used as prognostic markers. However, their value remains to be tailored to specific gastric NENs. METHODS: Series of type I NETs (n = 123 cases), type III NETs (n = 34 cases), and pdNECs (n = 43 cases) were retrospectively collected from four pathology centers specializing in endocrine pathology. All cases were characterized clinically and histopathologically. During follow-up (median 93 months) data were recorded to assess disease-specific patient survival. RESULTS: Type I NETs, type III NETs, and pdNECs differed markedly in terms of tumor size, grade, invasive and metastatic power, as well as patient outcome. Size was used to stratify type I NETs into subgroups with significantly different invasive and metastatic behavior. All 70 type I NETs < 0.5 cm (micro-NETs) were uneventful. Ki67-based grading proved efficient for the prognostic stratification of type III NETs; however, grade 2 (G2) was not associated with tumor behavior in type I NETs. Although G3 NETs (2 type I and 9 type III) had a very poor prognosis, it was found that patient survival was longer with type III G3 NETs compared to pdNECs. CONCLUSIONS: Given the marked, tumor type-related behavior differences, evaluation of gastric NEN prognostic parameters should be tailored to the type of neoplastic disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".