The Clinicopathological Significance of Tumor Cell Subtyping in Appendiceal Neuroendocrine Tumors: A Series of 135 Tumors
Bibliographic record
Abstract
Appendiceal neuroendocrine tumors (NETs) are common and often are identified as incidental lesions at the time of appendectomy. The guidelines for management are based on tumor size, degree of invasion, and the Ki67 proliferation index. Most small bowel NETs are composed of serotonin-producing EC-cells, but there are multiple other neuroendocrine cell types. In the rectum, there are L-cell tumors that express peptide YY (PYY), glucagon-like peptides (GLPs), and pancreatic polypeptide (PP); they are thought to have a better prognosis than serotonin-producing tumors. We investigated whether the appendix has distinct neuroendocrine tumor types based on cell type and whether that distinction has clinical significance. We collected 135 appendiceal NETs from the pathology archives of UHN Toronto and UHCMC (Cleveland). We analyzed the expression of biomarkers including CDX2, SATB2, PSAP, serotonin, glucagon (that detects GLPs), PYY, and pancreatic polypeptide (PP) and correlated the results with clinicopathologic parameters. Immunohistochemistry identified three types of appendiceal NETs. There were 75 (56%) classified as EC-cell tumors and 37 (27%) classified as L-cell tumors; the remaining 23 (17%) expressed serotonin and one of the L-cell biomarkers and were classified as mixed. EC-cell tumors were significantly larger with more extensive invasion involving the muscularis propria, subserosa, and mesoappendix compared with L-cell tumors. Mixed tumors were intermediate in all of these parameters. Both EC-cell and mixed tumors had lymphatic and/or vascular invasion while L-cell tumors had none. Unlike EC-cell NETs, L-cell tumors were not associated with lymph node metastasis. Tumor type correlated with pT stage and the only patient with distant metastatic disease in this series had an EC-cell tumor. Our study confirms that appendiceal NETs are not a homogeneous tumor population. There are at least three types of appendiceal NET, including EC-cell, L-cell, and mixed tumors. This information is important for surveillance of patients, as monitoring urinary 5HIAA levels is only appropriate for patients with serotonin-producing tumors, whereas measurement of GLPs and/or PP is more appropriate for patients with L-cell tumors. Our data also show that tumor type is of significance with EC-cell tumors exhibiting the most aggressive behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".