Imaging of Cardiac Fibrosis: An Update, From the <i>AJR</i> Special Series on Imaging of Fibrosis
Bibliographic record
Abstract
Myocardial fibrosis (MF) is defined as excessive production and deposition of extra-cellular matrix proteins that result in pathologic myocardial remodeling. Three types of MF have been identified: replacement fibrosis from tissue necrosis, reactive fibrosis from myocardial stress, and infiltrative interstitial fibrosis from progressive deposition of nondegradable material such as amyloid. Although echocardiography, nuclear medicine, and CT play important roles in the assessment of MF, MRI is pivotal in the evaluation of MF, with the late gadolinium enhancement (LGE) technique used as a primary end point. The LGE technique focuses on the pattern and distribution of gadolinium accumulation in the myocardium and assists in the diagnosis and establishment of the cause of both ischemic and nonischemic cardiomyopathy. LGE MRI also aids prognostication and risk stratification. In addition, LGE MRI is used to guide the management of patients considered for ablation for arrhythmias. Parametric mapping techniques, including T1 mapping and extracellular volume measurement, allow detection and quantification of diffuse fibrosis, which may not be detected by LGE MRI. These techniques also allow monitoring of disease progression and therapy response. This review provides an update on the imaging of MF, including prognostication and risk stratification tools, electrophysiologic considerations, and disease monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".