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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 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".