Diagnostic Accuracy of <scp>MRI</scp> for Differentiation of Benign and Malignant Pancreatic Cystic Lesions Compared to <scp>CT</scp> and Endoscopic Ultrasound: Systematic Review and <scp>Meta‐analysis</scp>
Bibliographic record
Abstract
BACKGROUND: Differentiation of benign and malignant pancreatic cystic lesions on MRI, computed tomography (CT), and endoscopic ultrasound (EUS) is critical for determining management. PURPOSE: To perform a systematic review evaluating the diagnostic accuracy of MRI for diagnosing malignant pancreatic cystic lesions, and to compare the accuracy of MRI to CT and EUS. STUDY TYPE: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus were searched until February 2020 for studies reporting MRI accuracy for assessing pancreatic cystic lesions. FIELD STRENGTH: 1.5T or 3.0T. ASSESSMENT: Methodologic and outcome data were extracted by two reviewers (AU and MA, 2 years of experience each). All studies of pancreatic cystic lesions on MRI were identified. Studies with incomplete MRI technique were excluded. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. STATISTICAL TESTS: Sensitivity/specificity was pooled using bivariate random-effects meta-analysis with 95% confidence intervals (95%CI). Pairwise-comparison of MRI to CT and EUS was performed. The impact of gadolinium-based contrast agents, mucinous lesions, and risk of bias were explored using meta-regression. RESULTS: MRI pooled sensitivity was 76% (95%CI 67% to 84%) and specificity was 80% (95%CI 74% to 85%) for distinguishing benign and malignant lesions. MRI and CT had similar sensitivity (P = 0.822) and specificity (P = 0.096), but MRI was more specific than EUS (80% vs. 75%, P < 0.05). Studies including only contrast-enhanced MRI were more sensitive than those including unenhanced exams (P < 0.05). MRI sensitivity and specificity did not differ for mucinous lesions (P = 0.537 and P = 0.384, respectively) or for studies at risk of bias (P = 0.789 and P = 0.791, respectively). DATA CONCLUSION: MRI and CT demonstrate comparable accuracy for diagnosing malignant pancreatic cystic lesions. EUS is less specific than MRI, which suggests that, in some cases, management should be guided by MRI findings rather than EUS. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".