Positron Emission Tomography/Computed Tomographic Scans Compared to Computed Tomographic Scans for Detecting Colorectal Liver Metastases
Bibliographic record
Abstract
OBJECTIVE: To review diagnostic accuracy of positron emission tomography/computed tomography (PET/CT) for colorectal liver metastases. BACKGROUND: Colorectal liver metastases can be treated with surgical resection; however, recurrence is seen in 58% of patients. PET/CT may better detect extra-hepatic disease before surgery to more accurately identify eligible candidates for surgery and, through better selection, improve patient prognosis. METHODS: We conducted a comprehensive systematic review on adults with colorectal liver metastases who received PET/CT and CT scans to detect metastases. The gold standard to confirm the diagnosis was histology. Study selection, quality assessment, and data extraction were completed independently by 2 investigators. Pooling of results was not feasible because of heterogeneity. A qualitative summary of results is presented. RESULTS: From 1083 citations, we identified 6 studies (440 patients) for the review. For extra-hepatic lesions (3 studies; 178 patients), PET/CT was more sensitive than CT, but specificities were similar (PET/CT sensitivity [SN] = 75%-89% and specificity [SP] = 95%-96% vs. CT SN = 58%-64% and SP = 87%-97%). For hepatic lesions (5 studies; 316 patients), PET/CT had higher SN and SP than CT (PET/CT SN = 91%-100% and SP = 75%-100%; CT SN = 78%-94% and SP = 25%-98%). For local recurrence (3 studies; 206 patients), PET/CT also had better accuracy than CT with SN = 93% to 100% and SP = 97% to 98% versus SN = 0 %-100% and SP = 97%-98%. CONCLUSION: Based on this systematic review, we conclude that PET/CT has a higher accuracy for detection of extra-hepatic and hepatic colorectal metastatic disease than CT alone. However, the results are based on a small number of studies and should be interpreted cautiously.
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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 | 0.017 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".