Liver Fibrosis Quantification by Magnetic Resonance Imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Liver fibrosis is a hallmark of chronic liver disease characterized by the excessive accumulation of extracellular matrix proteins. Although liver biopsy is the reference standard for diagnosis and staging of liver fibrosis, it has some limitations, including potential pain, sampling variability, and low patient acceptance. Hence, there has been an effort to develop noninvasive imaging techniques for diagnosis, staging, and monitoring of liver fibrosis. Many quantitative techniques have been implemented on magnetic resonance imaging (MRI) for this indication. The most widely validated technique is magnetic resonance elastography, which aims to measure viscoelastic properties of the liver and relate them to fibrosis stage. Several additional MRI methods have been developed or adapted to liver fibrosis quantification. Diffusion-weighted imaging measures the Brownian motion of water molecules which is restricted by collagen fibers. Texture analysis assesses the changes in the texture of liver parenchyma associated with fibrosis. Perfusion imaging relies on signal intensity and pharmacokinetic models to extract quantitative perfusion parameters. Hepatocellular function, which decreases with increasing fibrosis stage, can be estimated by the uptake of hepatobiliary contrast agents. Strain imaging measures liver deformation in response to physiological motion such as cardiac contraction. T1ρ quantification is an investigational technique, which measures the spin-lattice relaxation time in the rotating frame. This article will review the MRI techniques used in liver fibrosis staging, their advantages and limitations, and diagnostic performance. We will briefly discuss future directions, such as longitudinal monitoring of disease, prediction of portal hypertension, and risk stratification of hepatocellular carcinoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it