Harnessing intrinsic cardiac motion <i>vs.</i> external mechanical vibrations: a comparison of MRI cine-tagging and MR elastography for liver fibrosis assessment
Bibliographic record
Abstract
OBJECTIVE: This study aims to assess and compare the diagnostic accuracy of MRI cine-tagging and magnetic resonance elastography (MRE) for staging histologically confirmed liver fibrosis in patients with chronic liver disease. METHODS: MRI cine-tagging evaluates liver strain as the deformation induced by intrinsic cardiac motion on the left liver lobe, whereas MRE captures liver stiffness in response to externally applied vibrations from a mechanical driver. A head-to-head comparison of MRI cine-tagging and MRE was performed in 76 participants with biopsy-proven chronic liver disease. Spearman's rank correlation coefficients and areas under the receiver operating characteristic curve (AUC) were assessed. AUCs were compared using the Delong method. RESULTS: MRE-derived shear modulus increased, while strain obtained from tagged cine MRI decreased with higher fibrosis stages (ρ = 0.73 and ρ = -0.67, respectively; P < .0001). Both shear modulus and strain values exhibited significant differences across fibrosis stages (P < .0001) and correlated with each other (ρ = -0.44, P < .0001). MRE provided higher AUCs than MRI cine-tagging only for distinguishing stages ≤F3 vs. F4 (0.91 vs. 0.87, P = .043). There were no significant differences in AUCs for differentiating other dichotomized fibrosis stages, including stages F0 vs. ≥F1 (0.87 vs. 0.81, P = .083), ≤F1 vs. ≥F2 (0.84 vs. 0.84, P = .889), and ≤F2 vs. ≥F3 (0.89 vs. 0.86, P = .116). CONCLUSION: MRI cine-tagging provided a similar diagnostic performance compared to MRE for staging liver fibrosis, except for the diagnosis of cirrhosis (F4). It is possible to assess liver strain as part of abdominal MRI screening, offering additional insight into the left lobe without the need for additional equipment. ADVANCES IN KNOWLEDGE: A head-to-head comparison of magnetic resonance elastography (MRE), the most accurate technique for the noninvasive staging of liver fibrosis, and MRI cine-tagging has not been performed yet. We found that MRI cine-tagging, having the advantage of not requiring any additional hardware, provides a similar diagnostic performance compared to MRE for staging liver fibrosis, except for the diagnosis of cirrhosis in patients with chronic liver disease.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".