The role of biomarker in pancreatic neuroendocrine tumor: a narrative review
Bibliographic record
Abstract
Abstract Pancreatic neuroendocrine tumors (pNET) are heterogenous tumors originated from the diffuse neuroendocrine cells of pancreas, which show the function of synthesis, storage and secretion of peptide hormones and biomimetic amines. Biomarkers play a crucial role in the diagnosing, evaluating prognosis and predicting treatment response for pNET patients. Traditional NET markers such as chromogranin A and Neuron Specific Enolase, as a diagnostic biomarker, have relatively low sensitivity and specificity in pNET patients. The emergence of new types of biomarkers provides more reliable indicators for diagnosis and prognosis evaluation. Among them, NETest score is a promising biomarker with the highest diagnostic sensitivity (80%) and specificity (94%). In addition, this molecule can be also used as a prognostic biomarker, which can predict disease progression and shorter overall survival. Biomarkers related to therapeutic targets, such as vascular endothelial growth factor, vascular endothelial growth factor receptor, and key molecules of mTOR signaling pathway, have capability to predict response of treatment. With the development of next-generation sequencing, chip array, and digital droplet PCR, novel biomarkers such as circulating tumor cells, tumor-derived exosomes, and circulating tumor DNA and mRNA are expected to provide more accurate diagnosis, prognostic information, and prospective therapeutic targets. In this paper, biomarkers of pancreatic neuroendocrine tumor and their role in diagnosis, prognosis, diagnosis, treatment and monitoring are systematically introduced. Our conclusions can provide new basis for clinicians in the diagnosis and treatment process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".