Can preoperative liver MRI with gadoxetic acid help reduce open-close laparotomies for curative intent pancreatic cancer surgery?
Bibliographic record
Abstract
OBJECTIVES: To evaluate gadoxetic acid-enhanced liver MRI (EOB-MRI) versus contrast-enhanced computed tomography (CECT) for preoperative detection of liver metastasis (LM) and reduction of open-close laparotomies for pancreatic ductal adenocarcinoma (PDAC). METHODS: Sixty-six patients with PDAC had undergone preoperative EOB-MRI and CECT. LM detection by EOB-MRI and CECT and their impact on surgical planning, open-close laparotomies were compared by clinical and radiology reports and retrospective analysis of imaging by two blinded independent readers. Histopathology or imaging follow-up was the reference standard. Statistical analysis was performed at patient and lesion levels with two-sided McNemar tests. RESULTS: EOB-MRI showed higher sensitivity versus CECT (71.7% [62.1-80.0] vs. 34% [25.0-43.8]; p = 0.009), comparable specificity (98.6%, [96.9-99.5] vs. 100%, [99.1-100], and higher AUROC (85.1%, [80.4-89.9] vs. 66.9%, [60.9-73.1]) for LM detection. An incremental 7.6% of patients were excluded from surgery with a potential reduction of up to 13.6% in futile open-close laparotomies due to LM detected on EOB-MRI only. CONCLUSIONS: Preoperative EOB-MRI has superior diagnostic performance in detecting LM from PDAC. This better informs surgical eligibility with potential reduction of futile open-close laparotomies from attempted curative intent pancreatic cancer surgery.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".