MRI vs. CT for the Detection of Liver Metastases in Patients With Pancreatic Carcinoma: A Comparative Diagnostic Test Accuracy Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: The detection of liver metastases is important for pancreatic cancer curative treatment eligibility. The data suggest that magnetic resonance imaging (MRI) is more sensitive than computed tomography (CT) for the diagnosis of pancreatic cancer liver metastases. However, MRI is not currently recommended in multiple published guidelines. PURPOSE: To perform a comparative diagnostic test accuracy systematic review and meta-analysis comparing CT and MRI for pancreatic cancer liver metastases detection. STUDY TYPE: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and multiple radiology society meeting archives were searched until November 2018. Comparative design studies reporting on liver CT and MRI accuracy for detection of pancreatic cancer liver metastases in the same cohort were included. FIELD STRENGTH: 1.5T or 3.0T. ASSESSMENT: Demographic, methodologic, and diagnostic test accuracy data were extracted. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. STATISTICAL TESTS: Accuracy metrics were obtained using bivariate random-effects meta-analysis. The impact of different covariates on accuracy estimates was assessed using a meta-regression model. Covariates included modality, study design, tumor characteristics, risk of bias, and imaging protocols. RESULTS: Fourteen studies including 987 patients with pancreatic cancer (205 with liver metastases) were included. Sensitivity for CT and MRI was 45% (confidence intervals [95% CI] 21-71%) and 83% (95% CI 74-88%), respectively. Specificity for CT and MRI was 94% (95% CI 84-98%) and 96% (95% CI 93-97%), respectively. The greater observed sensitivity of MRI was preserved in the meta-regression model (P = 0.01), while no difference in specificity was detected (P = 0.16). CT sensitivity was highest for triphasic and quadriphasic examinations compared to single phase or biphasic protocols (P = 0.03). Most studies were at high risk of bias. DATA CONCLUSION: MRI is more sensitive than CT for pancreatic cancer liver metastases detection, accounting for confounding variables. Consideration of this finding in clinical practice guidelines is recommended. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.
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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 | 0.033 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.049 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".