Prospective Comparison of <scp>DWI</scp> ‐Derived Virtual <scp>MR</scp> Elastography and Conventional <scp>MR</scp> Elastography in Metabolic Dysfunction‐Associated Steatotic Liver Disease and Healthy Volunteers
Bibliographic record
Abstract
ABSTRACT Background Virtual MR elastography (VMRE) and MRE have been proposed for liver fibrosis staging in metabolic dysfunction‐associated steatotic liver disease (MASLD), but VMRE's diagnostic performance remains debated. Purpose To assess the inter‐visit and inter‐reader reproducibility of fat‐uncorrected and fat‐corrected diffusion‐weighted imaging (DWI)‐based VMRE and to compare their diagnostic performance with MRE for liver fibrosis staging in the MASLD population. Study Type Prospective. Population Fifty four participants were enrolled: 43 with biopsy‐proven MASLD (age: 57.0 ± 9.0 years; 26 males) and 11 healthy volunteers (age: 31.0 ± 15.0 years; 4 males). Field Strength/Sequence 3.0T, DWI ( b ‐values of 0, 200, and 1500 s/mm 2 ) for VMRE and phase‐contrast MRE at 60 Hz was performed. Assessment VMRE‐derived shifted apparent diffusion coefficients (sADC) reproducibility and diagnostic performance; MRE‐derived stiffness diagnostic performance. Statistical Tests Reproducibility was evaluated using intraclass correlation coefficients (ICC), within‐subject coefficient of variation (wCV), and bias and limits of agreement (LOA) in Bland–Altman analysis. Diagnostic performance was assessed with areas under the receiver operating characteristic curve (AUC) and compared with DeLong's test. p < 0.05 was considered statistically significant. Results For inter‐visit agreement, the ICC of fat‐uncorrected and fat‐corrected sADC were 0.88 and 0.83; wCV were 0.120 ± 0.30 and 0.141 ± 0.31; bias and 95% LOA were (−0.03 ± 0.18) × 10 −3 mm 2 /s and (−0.05 ± 0.33) × 10 −3 mm 2 /s, respectively. For inter‐reader agreement, the ICC of fat‐uncorrected and fat‐corrected VMRE were 0.99 and 0.99; wCV were 0.028 ± 0.011 and 0.039 ± 0.012, respectively; bias and 95% LOA were (−0.01 ± 0.03) × 10 −3 mm 2 /s and (−0.02 ± 0.05) × 10 −3 mm 2 /s, respectively. AUC of fat‐uncorrected, fat‐corrected sADC, and MRE‐derived stiffness for distinguishing fibrosis stages F0 versus ≥ F1 were 0.70 ± 0.17, 0.56 ± 0.18, and 0.87 ± 0.10; ≤ F1 versus ≥ F2 were 0.61 ± 0.16, 0.49 ± 0.17, and 0.86 ± 0.10; ≤ F2 versus ≥ F3 were 0.54 ± 0.16, 0.50 ± 0.16, and 0.89 ± 0.09; and ≤ F3 versus F4 were 0.58 ± 0.16, 0.55 ± 0.17, and 0.85 ± 0.11, respectively. MRE had significantly higher diagnostic performance than fat‐uncorrected and fat‐corrected VMRE for all fibrosis stages. Data Conclusion VMRE has good reproducibility, but has lower fibrosis staging accuracy than MRE. Evidence Level 1. Technical Efficacy Stage 2.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".