Longitudinal correlations between MRE, MRI-PDFF, and liver histology in patients with non-alcoholic steatohepatitis: Analysis of data from a phase II trial of selonsertib
Bibliographic record
Abstract
BACKGROUND & AIMS: Non-invasive tools for monitoring treatment response and disease progression in non-alcoholic steatohepatitis (NASH) are needed. Our objective was to evaluate the utility of magnetic resonance (MR)-based hepatic imaging measures for the assessment of liver histology in patients with NASH. METHODS: We analyzed data from patients with NASH and stage 2 or 3 fibrosis enrolled in a phase II study of selonsertib. Pre- and post-treatment assessments included centrally read MR elastography (MRE)-estimated liver stiffness, MR imaging-estimated proton density fat fraction (MRI-PDFF), and liver biopsies evaluated according to the NASH Clinical Research Network classification and the non-alcoholic fatty liver disease activity score (NAS). RESULTS: Among 54 patients with MRE and biopsies at baseline and week 24, 18 (33%) had fibrosis improvement (≥1-stage reduction) after undergoing 24 weeks of treatment with the study drug. The area under the receiver operating characteristic curve (AUROC) of MRE-stiffness to predict fibrosis improvement was 0.62 (95% CI 0.46-0.78) and the optimal threshold was a ≥0% relative reduction. At this threshold, MRE had 67% sensitivity, 64% specificity, 48% positive predictive value, 79% negative predictive value. Among 65 patients with MRI-PDFF and biopsies at baseline and week 24, a ≥1-grade reduction in steatosis was observed in 18 (28%). The AUROC of MRI-PDFF to predict steatosis response was 0.70 (95% CI 0.57-0.83) and the optimal threshold was a ≥0% relative reduction. At this threshold, MRI-PDFF had 89% sensitivity and 47% specificity, 39% positive predictive value, and 92% negative predictive value. CONCLUSIONS: These preliminary data support the further evaluation of MRE-stiffness and MRI-PDFF for the longitudinal assessment of histologic response in patients with NASH. LAY SUMMARY: Liver biopsy is a potentially painful and risky method to assess damage to the liver due to non-alcoholic steatohepatitis (NASH). We analyzed data from a clinical trial to determine if 2 methods of magnetic resonance imaging - 1 to measure liver fat and 1 to measure liver fibrosis (scarring) - could potentially replace liver biopsy in evaluating NASH-related liver injury. Both imaging methods were correlated with biopsy in showing the effects of NASH on the liver.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".