FDG PET-CT Versus CT for Recurrence in Posttreatment Pancreatic Adenocarcinoma (PAC): Comparative Diagnostic Test Accuracy Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To perform a systematic review comparing diagnostic test accuracy of FDG PET-CT versus CT for assessing recurrence in post-treatment pancreatic adenocarcinoma (PAC). METHODS: Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews (Ovid), Cochrane Central Register of Controlled Trials (Ovid), and Web of Science searched until February 2024 for comparative diagnostic accuracy studies assessing PET-CT versus CT in post-treated subjects with PAC to evaluate diagnostic accuracy for recurrence. The reference standard was histopathology when available or clinical follow-up. Data extraction, risk of bias (ROB), and applicability assessment were performed by two authors. QUADAS-C was used for ROB assessment. Bivariate random-effects model meta-analysis, and meta-regression were performed for test comparison with 95% confidence intervals (95%CI). RESULTS: Of 5345 citations retrieved, nine articles met all inclusion criteria, with 400 PAC patients who had 320 recurrences included. Three studies were considered at low risk of bias, while the remaining six studies were at high risk for bias. The sensitivity/specificity (95%CI) and AUC of PET-CT was 89% (83-92%)/83% (73-90%) and 0.927 and for CT was 72% (64-79%)/76% (64-85%) and 0.803. A meta-regression model demonstrated a higher sensitivity for PET-CT than CT alone (P<0.001), with no significant difference in specificity (P=0.243). Risk of bias had no significant impact on CT or PET-CT diagnostic accuracy (P=0.072-0.775). CONCLUSIONS: PET-CT exhibited greater sensitivity compared to CT alone, with no significant variance in specificity between the two modalities, for recurrence evaluation in PAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.108 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".