Use and perceived utility of <scp> [ <sup>18</sup> F]FDG PET/CT </scp> in neuroendocrine neoplasms: A consensus report from the <scp>European Neuroendocrine Tumor Society (ENETS) Advisory Board Meeting</scp> 2022
Bibliographic record
Abstract
Somatostatin receptor (SST) PET/CT is the gold standard for well-differentiated neuroendocrine tumours (NET) imaging. Higher grades of neuroendocrine neoplasms (NEN) show preferential [18F]FDG (FDG) uptake, and even low-grade NET may de-differentiate over time. FDG PET/CT's prognostic role is widely accepted; however, its impact on clinical decision-making remains controversial and its use varies widely. A questionnaire-based survey on FDG PET/CT use and perceived decision-making utility in NEN was submitted to the ENETS Advisory Board Meeting attendees (November 2022, response rate = 70%). In 3/15 statements, agreement was higher than 75%: (i) FDG was considered useful in NET, irrespective of grade, in case of mis-matched lesions (detectable on diagnostic CT but negative/faintly positive on SST PET/CT), especially if PRRT is contemplated (80%); (ii) in NET G3 if curative surgery is considered (82%); and (iii) in NEC prior to surgery with curative intent (98%). FDG use in NET G3, even in the presence of matched lesions, as a baseline for response assessment was favoured by 74%. Four statements obtained more than 60% consensus: (i) FDG use in NET G3 if locoregional therapy is considered (65%); (ii) in neuroendocrine carcinoma before initiating active therapy as a baseline for response assessment (61%); (iii) biopsy to re-assess tumour grade prior to a change in therapeutic management (68%) upon detection of FDG-positivity on the background of a prior G1-2 NET; (iv) 67% were in favour to reconsider PRRT to treat residual SST-positive lesions after achieving complete remission on FDG of the SST-negative disease component. Multidisciplinary opinion broadly supports the use of FDG PET/CT for characterisation of disease biology and to guide treatment selection across a range of indications, despite the lack of full consensus in many situations. This may reflect existing clinical access due to lack of reimbursement or experience with this investigation, which should be addressed by further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".