Clinical Utility of 18F-FDG PET in Neuroendocrine Tumors Prior to Peptide Receptor Radionuclide Therapy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The role of 18F-FDG PET in patients with variable grades of neuroendocrine tumors (NETs) prior to peptide receptor radionuclide therapy (PRRT) has not been adequately elucidated. We aimed to evaluate the impact of 18F-FDG PET status on disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) in neuroendocrine tumor (NET) patients receiving PRRT. We searched the MEDLINE, Embase, Cochrane Library, and Web of Science databases up to July 2020 and used the Newcastle-Ottawa scale (NOS) criteria to assess quality/risk of bias. A total of 5091 articles were screened. In 12 studies, 1492 unique patients with NETs of different origins were included. The DCR for patients with negative 18F-FDG PET status prior to PRRT initiation was 91.9%, compared to 74.2% in patients with positive 18F-FDG PET status (random effects odds ratio (OR): 4.85; 95% CI: 2.27–10.36). Adjusted analysis of pooled hazard ratios (HRs) confirmed longer PFS and OS in NET patients receiving PRRT with negative 18F-FDG PET (random effects HR:2.45; 95%CIs: 1.48–4.04 and HR:2.25; 95% CIs:1.55–3.28, respectively). In conclusion, 18F-FDG PET imaging prior to PRRT administration appears to be a useful tool in NET patients to predict tumor response and survival outcomes and a negative FDG uptake of the tumor is associated with prolonged PFS and OS.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".