Nonalcoholic Fatty Liver Disease: MR Imaging of Liver Proton Density Fat Fraction to Assess Hepatic Steatosis
Bibliographic record
Abstract
PURPOSE: To evaluate the diagnostic performance of magnetic resonance (MR) imaging-estimated proton density fat fraction (PDFF) for assessing hepatic steatosis in nonalcoholic fatty liver disease (NAFLD) by using centrally scored histopathologic validation as the reference standard. MATERIALS AND METHODS: This prospectively designed, cross-sectional, internal review board-approved, HIPAA-compliant study was conducted in 77 patients who had NAFLD and liver biopsy. MR imaging-PDFF was estimated from magnitude-based low flip angle multiecho gradient-recalled echo images after T2* correction and multifrequency fat modeling. Histopathologic scoring was obtained by consensus of the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network Pathology Committee. Spearman correlation, additivity and variance stabilization for regression for exploring the effect of a number of potential confounders, and receiver operating characteristic analyses were performed. RESULTS: Liver MR imaging-PDFF was systematically higher, with higher histologic steatosis grade (P < .001), and was significantly correlated with histologic steatosis grade (ρ = 0.69, P < .001). The correlation was not confounded by age, sex, lobular inflammation, hepatocellular ballooning, NASH diagnosis, fibrosis, or magnetic field strength (P = .65). Area under the receiver operating characteristic curves was 0.989 (95% confidence interval: 0.968, 1.000) for distinguishing patients with steatosis grade 0 (n = 5) from those with grade 1 or higher (n = 72), 0.825 (95% confidence interval: 0.734, 0.915) to distinguish those with grade 1 or lower (n = 31) from those with grade 2 or higher (n = 46), and 0.893 (95% confidence interval: 0.809, 0.977) to distinguish those with grade 2 or lower (n = 58) from those with grade 3 (n = 19). CONCLUSION: MR imaging-PDFF showed promise for assessment of hepatic steatosis grade in patients with NAFLD. For validation, further studies with larger sample sizes are needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".