Multifrequency MR elastography for grading inflammation in metabolic dysfunction-associated steatotic liver disease: a pilot study
Bibliographic record
Abstract
Abstract Background Noninvasive grading of liver inflammation in metabolic dysfunction-associated steatotic liver disease (MASLD) remains an unmet clinical need. Purpose To evaluate the diagnostic performance of multifrequency MR elastography (MMRE) for grading liver inflammation and diagnosing metabolic dysfunction-associated steatohepatitis (MASH). Materials and methods In this prospective, single-site study, participants underwent MMRE at 30, 40, and 60 Hz on a 3T system. Multifrequency dispersion coefficient (α), storage modulus (G'), loss modulus (G”), and magnitude of the shear modulus (|G*|) were computed. The reference standard was histopathological analysis of needle biopsy specimens. The MASLD activity score was computed to assign MASH status. Univariate and multivariable correlation and areas under the receiver operating characteristic curve (AUCs) were assessed. Results Of the 72 participants enrolled, 60 (12 healthy, 48 MASLD) were analyzable. Correlations of α, G', G”, and |G*| with lobular inflammation grades were ρ = −0.62, P < .001; ρ = 0.46, P < .001; ρ = 0.39, P < .01; and ρ = 0.44, P < .001; and the corresponding correlations with ballooning ρ = −0.42, P < .001; ρ = 0.42, P < .001; ρ = 0.40, P < .001; and ρ = 0.41, P < .001. The correlation between α and lobular inflammation remained after adjusting for steatosis, ballooning, and fibrosis (β = −0.06, P < .001); and with ballooning after adjusting for steatosis, lobular inflammation, and fibrosis (β = −0.02, P < .01). AUCs of α were 0.85, 0.96, and 0.94, respectively, for distinguishing lobular inflammation grades 0 vs ≥1, ≤1 vs ≥2, and ≤2 vs 3; 0.84 and 0.78 for distinguishing ballooning grades 0 vs ≥1 and ≤1 vs 2; and 0.81 to 0.85 for diagnosing MASH. Conclusion The multifrequency dispersion coefficient α was associated with histologic inflammation and should be further evaluated with external validation as a possible clinical marker in MASLD.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".