A196 A COMPARISON OF LIVER FIBROSIS AND SIMPLE STEATOSIS ASSESSMENT USING GADOXETIC-ACID ENHANCED MRI WITH MR ELASTOGRAPHY AND MRI FAT FRACTION
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) has become a pandemic, affecting up to 25% of the global population, with high incidence and prevalence in North America.1 The spectrum of disease ranges from simple steatosis to steatohepatitis, with or without fibrosis (NASH). It is important to be able to diagnose patients with simple steatosis, but even more so to identify those with NASH given their increased risk of progression to cirrhosis. The gold standard for NASH diagnosis remains liver biopsy. However, given its invasive nature and recognized sampling error from heterogeneity of disease distribution, there has been an increase in the use of non-invasive techniques.2 MRI-fat fraction (MR-FF) is validated for the assessment of hepatic steatosis3 and MR elastography (MRE) for fibrosis.4 MRE requires special hardware and software not readily available. As such, a more readily available imaging modality known as gadoxetic-acid enhanced MRI (GE-MRI), has shown to potentially differentiate simple steatosis from NASH.5 To determine if GE-MRI can differentiate NAFLD from healthy controls compared to MR-FF, and/or simple steatosis from NASH compared to MRE. Healthy controls and NAFLD patients provided informed written consent to participate in this cross-sectional cohort study. The study was approved by the Research Ethics Board at Western University. The diagnosis of NAFLD was based on the AASLD definition.6 These NAFLD patients were divided into those with simple steatosis or NASH based on liver biopsy or transient elastography. All patients underwent MRI-FF, MRE, and GE-MRI. A total of 17 patients were studied. Five healthy control and 12 NAFLD patients, of whom 3 had biopsy proven NASH and 4 had fibrosis based on transient elastography, the remaining 5 had simple steatosis. GE-MRI was able to differentiate healthy patients from NAFLD patients [mean enhancement difference -35.07 ± 11.66 (p=0.0088)]. MR-FF had a statistically significant difference [0.2823 ± 0.03726 (p<0.0001)] and differentiated all patients with NAFLD. GE-MRI was not able to differentiate between hepatic steatosis and NASH, but there was a mean difference in enhancement of 18.67. MRE was able to differentiate between all patients with simple steatosis and NASH with statistical significance [mean difference -1.799 (95% CI = -3.584 to -0.01313, p=0.0482)]. This study shows the difference in enhancement on GE-MRI is much lower in patients with fatty liver and particularly those with liver fibrosis, potentially due to altered hepatocyte uptake of gadoxetic acid. Given the small patient size, specific cut-off values cannot reliably be established. The study also confirms that MR-FF is a reliable modality for the diagnosis of NAFLD, but not liver fibrosis, and that MRE is reliable for the diagnosis of NASH. None
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".