Liver fibrosis: Review of current imaging and MRI quantification techniques
Bibliographic record
Abstract
Liver fibrosis is characterized by the accumulation of extracellular matrix proteins such as collagen in the liver interstitial space. All causes of chronic liver disease may lead to fibrosis and cirrhosis. The severity of liver fibrosis influences the decision to treat or the need to monitor hepatic or extrahepatic complications. The traditional reference standard for diagnosis of liver fibrosis is liver biopsy. However, this technique is invasive, associated with a risk of sampling error, and has low patient acceptance. Imaging techniques offer the potential for noninvasive diagnosis, staging, and monitoring of liver fibrosis. Recently, several of these have been implemented on ultrasound (US), computed tomography, or magnetic resonance imaging (MRI). Techniques that assess changes in liver morphology, texture, or perfusion that accompany liver fibrosis have been implemented on all three imaging modalities. Elastography, which measures changes in mechanical properties associated with liver fibrosis—such as strain, stiffness, or viscoelasticity—is available on US and MRI. Some techniques assessing liver shear stiffness have been adopted clinically, whereas others assessing strain or viscoelasticity remain investigational. Further, some techniques are only available on MRI—such as spin‐lattice relaxation time in the rotating frame ( T 1 ρ), diffusion of water molecules, and hepatocellular function based on the uptake of a liver‐specific contrast agent—remain investigational in the setting of liver fibrosis staging. In this review, we summarize the key concepts, advantages and limitations, and diagnostic performance of each technique. The use of multiparametric MRI techniques offers the potential for comprehensive assessment of chronic liver disease severity. Level of Evidence : 5 J. MAGN. RESON. IMAGING 2017;45:1276–1295
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".