MR elastography in nonalcoholic fatty liver disease: inter-center and inter-analysis-method measurement reproducibility and accuracy at 3T
Bibliographic record
Abstract
OBJECTIVES: To assess reproducibility and fibrosis classification accuracy of magnetic resonance elastography (MRE)-determined liver stiffness measured manually at two different centers, and by automated analysis software in adults with nonalcoholic fatty liver disease (NAFLD), using histopathology as a reference standard. METHODS: This retrospective, cross-sectional study included 91 adults with NAFLD who underwent liver MRE and biopsy. MRE-determined liver stiffness was measured independently for this analysis by an image analyst at each of two centers using standardized manual analysis methodology, and separately by an automated analysis. Reproducibility was assessed pairwise by intraclass correlation coefficient (ICC) and Bland-Altman analysis. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) analyses. RESULTS: ICC of liver stiffness measurements was 0.95 (95% CI: 0.93, 0.97) between center 1 and center 2 analysts, 0.96 (95% CI: 0.94, 0.97) between the center 1 analyst and automated analysis, and 0.94 (95% CI: 0.91, 0.96) between the center 2 analyst and automated analysis. Mean bias and 95% limits of agreement were 0.06 ± 0.38 kPa between center 1 and center 2 analysts, 0.05 ± 0.32 kPa between the center 1 analyst and automated analysis, and 0.11 ± 0.41 kPa between the center 2 analyst and automated analysis. The area under the ROC curves for the center 1 analyst, center 2 analyst, and automated analysis were 0.834, 0.833, and 0.847 for distinguishing fibrosis stage 0 vs. ≥ 1, and 0.939, 0.947, and 0.940 for distinguishing fibrosis stage ≤ 2 vs. ≥ 3. CONCLUSION: MRE-determined liver stiffness can be measured with high reproducibility and fibrosis classification accuracy at different centers and by an automated analysis. KEY POINTS: • Reproducibility of MRE liver stiffness measurements in adults with nonalcoholic fatty liver disease is high between two experienced centers and between manual and automated analysis methods. • Analysts at two centers had similar high diagnostic accuracy for distinguishing dichotomized fibrosis stages. • Automated analysis provides similar diagnostic accuracy as manual analysis for advanced fibrosis.
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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 | 0.001 | 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) | 0.000 | 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".