Biomarkers to Inform Prognosis and Treatment for Unresectable or Metastatic GEP-NENs
Bibliographic record
Abstract
Importance: Evidence-based treatment decisions for advanced gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) require individualized patient-centered decision-making that accounts for patient and cancer characteristics. Objective: To create an accessible guidance document to educate clinicians and patients on biomarkers informing prognosis and treatment in unresectable or metastatic GEP-NENs. Methods: A multidisciplinary panel in-person workshop was convened to define methods. English language articles published from January 2016 to January 2023 in PubMed (MEDLINE) and relevant conference abstracts were reviewed to investigate prognostic and treatment-informing features in unresectable or metastatic GEP-NENs. Data from included studies were used to form evidence-based recommendations. Quality of evidence and strength of recommendations were determined using the Grading of Recommendations, Assessment, Development and Evaluations framework. Consensus was reached via electronic survey following a modified Delphi method. Findings: A total of 131 publications were identified, including 8 systematic reviews and meta-analyses, 6 randomized clinical trials, 29 prospective studies, and 88 retrospective cohort studies. After 2 rounds of surveys, 24 recommendations and 5 good clinical practice statements were developed, with full consensus among panelists. Recommendations focused on tumor and functional imaging characteristics, blood-based biomarkers, and carcinoid heart disease. A single strong recommendation was made for symptomatic carcinoid syndrome informing treatment in midgut neuroendocrine tumors. Conditional recommendations were made to use grade, morphology, primary site, and urinary 5-hydroxyindoleacetic levels to inform treatment. The guidance document was endorsed by the Commonwealth Neuroendocrine Tumour Collaboration and the North American Neuroendocrine Tumor Society. Conclusions and Relevance: The study results suggest that select factors have sufficient evidence to inform care in GEP-NENs, but the evidence for most biomarkers is weak. This article may help guide management and identify gaps for future research to advance personalized medicine and improve outcomes for patients with GEP-NENs.
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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 | 0.045 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".