<scp>18F</scp>‐<scp>FDG PET</scp>/<scp>CT</scp> for Surveillance in Salivary Gland Cancers: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the diagnostic accuracy of 18F-FDG-PET/CT compared to conventional imaging modalities (CIM) to detect recurrence of primary salivary gland cancers (SGCs). DATA SOURCES: Review performed on December 26, 2024, using Embase, CINHAL, MEDLINE, and PubMed. REVIEW METHODS: Two blinded reviewers selected studies reporting diagnostic accuracy of PET/CT in identifying locoregional recurrence and/or metastasis in patients with SGCs. The analysis was performed adhering to PRISMA guidelines using R 4.3.3. Pooled analysis with 95% confidence intervals (CI) were analyzed. RESULTS: A total of 12 studies were retained from the systematic review, including 264 patients evaluated in the meta-analysis. For local recurrence, there was a pooled sensitivity of 0.86 (95% CI 0.73-0.93) and a pooled specificity of 0.95 (95% CI 0.92-0.97) for PET/CT, and a pooled sensitivity of 0.89 (95% CI 0.80-0.94) and a pooled specificity of 0.91 (95% CI 0.79-0.97) for CIM (p = 0.90). For regional metastasis, there was a pooled sensitivity of 0.90 (95% CI 0.73-0.97) and a pooled specificity of 0.96 (95% CI 0.92-0.98) for PET/CT, and a pooled sensitivity of 0.80 (95% CI 0.62-0.91) and a pooled specificity of 0.95 (0.90-0.98) for CIM (p = 0.26). For distant metastasis, there was a pooled sensitivity of 0.96 (95% CI 0.90-0.99) and a pooled specificity of 0.95 (95% CI 0.85-0.98) for PET/CT, and a pooled sensitivity of 0.80 (95% CI 0.71-0.86) and a pooled specificity of 0.97 (95% CI 0.87-0.99) for CIM (p = 0.018). CONCLUSIONS: 18F-FDG-PET/CT imaging is accurate for the detection of SGC recurrence, particularly for the detection of regional and distant metastases. LEVEL OF EVIDENCE: NA Laryngoscope, 135:1884-1898, 2025.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".