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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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; a candidate call from one teacher head, 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".