Prospective Evaluation of Virtual <scp>MR</scp> Elastography With Diffusion‐Weighted Imaging in Subjects With <scp>Nonalcoholic</scp> Fatty Liver Disease
Bibliographic record
Abstract
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is increasingly common worldwide and can lead to the development of cirrhosis, liver failure and cancer. Virtual magnetic resonance elastography (VMRE), which is based on a shifted apparent diffusion coefficient (sADC), is a potential noninvasive method to assess liver fibrosis without the specialized hardware and expertise required to implement traditional MR elastography (MRE). Although hepatic steatosis is known to confound ADC measurements, previous studies using VMRE have not corrected for hepatic fat fraction. PURPOSE: To compare VMRE, corrected for the confounding effects of unsuppressed fat signal, to MRE and biopsy in subjects with suspected NAFLD. STUDY TYPE: Prospective, cross-sectional. POPULATION: A total of 49 adult subjects with suspected NAFLD (18 male; median age 55 years, range 33-74 years) who underwent liver biopsy. FIELD STRENGTH/SEQUENCE: 3T, diffusion-weighted spin echo planar, chemical-shift encoded (IDEAL IQ) and MRE sequences. ASSESSMENT: Two observers drew regions of interest on sADC, proton density fat fraction and MRE-derived stiffness maps. Fat-corrected sADC values were used to calculate the diffusion-based shear modulus according to the VMRE method. Predicted fibrosis stage for MRE and VMRE was determined using previously published cut-off values. STATISTICAL TESTS: ). Agreement between MRE and VMRE-predicted fibrosis stage was evaluated with a kappa coefficient and accuracy compared using McNemar's test. A one-way ANOVA determined if the fat-corrected sADC (VMRE) and MRE differed by fibrosis stage. A P value < 0.05 was considered statistically significant. RESULTS: = 0.046 and a slope that was not significantly different from zero (P = 0.14). There was no agreement between MRE and VMRE-predicted fibrosis stage (kappa = -0.01). The proportion of correctly predicted fibrosis stage was significantly higher for MRE compared to VMRE. MRE was significantly associated with fibrosis stage, but fat-corrected sADC was not (P = 0.24). DATA CONCLUSION: Fat-corrected VMRE was not associated with fibrosis stage in NAFLD. Further investigation is required if VMRE is to be considered in subjects with NAFLD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".