Appropriate use of tissue sampling and somatostatin receptor PET imaging in the diagnosis of pancreatic neuroendocrine tumors: results of an International Delphi Consensus
Bibliographic record
Abstract
BACKGROUND: Current guidelines lack clarity regarding the appropriate use of preoperative ultrasound-guided (EUS) biopsy and receptor positron emission tomography (SSTR PET) imaging for pancreatic neuroendocrine tumors (PNETs). This study aims to reach expert consensus on the optimal sequencing of SSTR PET and EUS biopsy in the diagnostic workup and management of patients with suspected PNETs. METHODS: A three-round modified Delphi process was used. A multidisciplinary panel of experts was recruited via snowball sampling. A set of 22 baseline statements pertaining to diagnostic workup, imaging, and biopsy was developed based on literature review and feedback obtained through a focus group. Survey rounds were conducted electronically and anonymously. A panel of international experts was asked to indicate whether they agreed, disagreed, or lacked the appropriate background to answer each statement. Of the 55 experts invited, 38 (69%) accepted to participate. Consensus was achieved with > 80% agreement. RESULTS: Response rates were 97%, 100%, and 100% in rounds 1, 2, and 3, respectively. Following rounds 1 and 2, 29 final statements achieved consensus in the following three domains: diagnostic workup (15 statements), imaging (nine statements), and tissue sampling (five statements). Cronbach's alpha value, a measure of internal consistency, was 0.91 and 0.85 for rounds 1 and 2, respectively. The final set of statements achieved a 95% approval rate in round 3. CONCLUSION: This international Delphi study provides expert consensus-based guidance on the appropriate use of EUS biopsy in the diagnostic workup of PNETs in the era of SSTR PET imaging.
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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 | 0.293 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".