Appendiceal Well-Differentiated Neuroendocrine Tumors: A Single-Center Experience and New Insights into the Effective Use of Immunohistochemistry
Bibliographic record
Abstract
Background . Appendiceal well-differentiated neuroendocrine tumor is the most common histological type of appendiceal tumor. The majority of tumors are found incidentally at the tip of the appendix, with few exceptions. Due to its primarily indolent nature, this entity presents unique pathological challenges, particularly in the appropriate use of immunohistochemistry which this study aims to clarify. Patients and methods . Patients diagnosed at University Health Network (Canada) between 2005–2019 were selected and reviewed. Results . We identified 70 patients and sex distribution was female 60%; median age 36.5 years. Among them, 63 patients underwent appendectomy, and seven had initial right hemicolectomy for non-appendix lesions. Mean tumor size was 5.0 mm. Tumor extent was submucosa (15%); muscularis propria (34%); subserosa or mesoappendix (42%); visceral peritoneum (8%). All were clinically non-functional and negative for nodal and distant metastasis. Ninety percent of tumors were WHO Grade 1; 10% were WHO Grade 2. Immunohistochemically, an average of six stains were performed per patient. Nearly all tumors were positive for chromogranin A, synaptophysin, CAM5.2, and CDX2. MIB-1 staining was < 3% in 58/63 tumors. Other immunohistochemical stainings performed were hormonal markers (serotonin, glucagon, pancreatic peptide, peptide YY). Subsequent right hemicolectomy was performed on five patients. All were followed up (median 4 years 8 months), and all were alive without recurrence except for one patient who died of another comorbidity. Conclusion . Tumors that are small, localized, and of low grade can be reasonably exempt from an extensive immunohistochemical panel in the absence of non-typical clinical and morphological features.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".