Diagnostic accuracy of dynamic gadoxetic‐acid–enhanced MRI and PET/CT compared in patients with liver metastases from neuroendocrine neoplasms
Bibliographic record
Abstract
PURPOSE: To evaluate the diagnostic accuracy of dynamic-contrast-enhanced (DCE) MRI in comparison to both (18)F-FDG- and (68)Ga-DOTATATE-PET/CT in patients with liver metastases of neuroendocrine neoplasms (NEN). MATERIALS AND METHODS: Thirty-two patients with hepatic metastases from NEN were examined both in DCE-MRI and positron emission tomography/computed tomography (PET/CT), using either (18)F-fluorodeoxyglucose ((18)F-FDG) or (68)Ga-DOTATATE as tracer. DCE-MRI was performed at 3 Tesla with Gd-EOB-DTPA acquiring 48 slices every 2.2 s for 5 min. Three regions of interest (ROIs) representing liver background and liver metastases were defined in fat-saturated T1w three-dimensional GRE MRI sequences in the hepatobiliary phase. Corresponding ROIs were then defined in the DCE-MRI- and in the PET/CT-dataset. Area under the curve (AUC) was calculated for the differentiation between metastases and liver background for DCE-MRI and PET-CT parameters. RESULTS: AUC was very high for SUVmean (mean standardized uptake value) derived from (68)Ga-DOTATATE- (AUC = 0.966), and (18)F-FDG-PET/CT (AUC = 0.989). For DCE-MRI parameters, arterial flow fraction and intracellular uptake fraction showed the highest AUCs (AUC = 0.826, AUC = 0.819, respectively). The combination of those two had an AUC of 0.949. The combination of DCE-MRI and PET-CT parameters resulted in the highest AUC. CONCLUSION: Both PET/CT parameters and DCE-MRI perfusion parameters show a high diagnostic accuracy in the distinction between liver metastases and liver tissue. Our data suggest that both modalities provide complementary information.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".