Algorithmic approach to neuroendocrine tumors in targeted biopsies: Practical applications of immunohistochemical markers
Bibliographic record
Abstract
Neuroendocrine tumors (NETs) constitute a heterogeneous group of neoplasms with distinct biological behaviors, depending on the site of origin and the degree of tumor proliferation. Although advances in biochemical and radiological modalities have enhanced the ability to detect NETs, tissue diagnosis remains the gold standard to assess tumor characteristics for treatment decision making. In an era with growing demands for precision diagnostics based on smaller tissue samples, immunohistochemistry has become an indispensable tool in the pathologist's repertoire. In conjunction with clinical findings and cytomorphology, complementary use of 1) markers of neuroendocrine differentiation, 2) markers confirming epithelial nature, 3) markers of cellular proliferation, 4) transcription factors and hormonal markers, as well as 5) predictive and prognostic markers may be necessary to guide patient management in NETs. The current review summarizes common applications of these immunohistochemical markers when confronted with a potential neuroendocrine neoplasm, and proposes a stepwise algorithmic approach to avoid diagnostic errors in targeted biopsies. Cancer Cytopathol 2016;124:871-884. © 2016 American Cancer Society.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".