A Systematic Review and Meta-Analysis on the Diagnostic and Prognostic Values of 18F-FDG PET in Uveal Melanoma and Its Hepatic Metastasis
Bibliographic record
Abstract
In this systematic review and meta-analysis (PRISMA-compliant), we tried to investigate diagnostic and prognostic values of 18F-FDG PET in uveal melanoma. A systematic search was conducted on the main medical literature databases to include studies that evaluated 18F-FDG PET as the imaging modality to evaluate patients with uveal melanoma. Overall, 27 studies were included. Twelve had data about the detection rate of 18F-FDG PET in primary intra-ocular tumours. The pooled sensitivity was 45% (95%CI: 41–50%). Furthermore, studies showed that the larger the primary tumour, the higher its uptake. Among the included studies, 13 assessed 18F-FDG PET in detecting metastasis. The pooled sensitivity and specificity were 96% (95%CI: 81–99%) and 100% (95%CI: 94–100%), respectively. Regarding liver metastasis, they were 95% (95%CI: 79–99%) and 100% (95%CI: 91–100%), respectively. Noteworthy, the level of 18F-FDG uptake was a strong predictor of patient survival. Lastly, 18F-FDG PET could characterise lesions from the histopathology perspective, distinguishing high-risk from low-risk diseases. Overall, although not reliable in detecting primary intra-ocular tumours, 18F-FDG PET is highly accurate for diagnosing metastatic uveal melanomas. It can also be a highly valuable modality in terms of patient prognostication. Thus, 18F-FDG PET can be recommended in patients diagnosed with uveal melanoma to enhance decision-making and patient management.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".