The association between myocardial texture characteristics on cardiac magnetic resonance and the development of major adverse cardiovascular events in patients with acute myocardial injury
Bibliographic record
Abstract
Introduction . Cardiac magnetic resonance (CMR) is the gold standard for assessing myocardial remodeling after myocardial infarction. Particular attention is paid to myocardial tissue characteristics assessed using late gadolinium enhancement (LGE). Textural heterogeneity parameters of LGE are a novel quantitative metric that reflects the structural heterogeneity of left ventricular (LV) myocardial tissue changes. Aim: To investigate the association between textural parameters, assessed by quantitative analysis of signal intensity heterogeneity on late gadolinium enhancement CMR, and the development of major adverse cardiovascular events (MACE) in patients with acute myocardial injury. Material and methods . This retrospective study included 108 patients admitted to the emergency cardiology department with a diagnosis of primary ST-elevation or non-ST-elevation myocardial infarction (STEMI or NSTEMI). A composite primary endpoint was established, which included the following clinical outcomes: cardiovascular death, all-cause death, non-fatal myocardial infarction, and non-fatal acute stroke. Inclusion criteria were: 1) performance of contrast-enhanced CMR within 4–7 days of hospitalization; 2) CMR findings consistent with acute ischemic injury of the LV; and 3) satisfactory image quality. CMR criteria for acute ischemic injury included: a high-intensity signal on T2-weighted images (T2WI) with co-localized LGE in a segment(s) demonstrating an ischemic pattern of contrast distribution. Quantitative CMR analysis was performed using the dedicated post-processing software CVI42 (Circle Cardiovascular Imaging, Canada). Myocardial texture analysis was conducted using the 3D Slicer application, version 5.2.2 (The Slicer Community, USA). For the analysis, LGE images were used. From each slice, textural features of signal intensity (SI) heterogeneity were extracted separately for the following regions of interest (ROIs): the LV myocardial injury zone, intact myocardium, and the entire LV (comprising both injured and intact myocardium). Results . The mean age of the patients was 59.56 ± 10.7 years, with 75% (n = 81) being male. STEMI was present in 89.3% of the entire cohort. The follow-up period was 1095 ± 23 days. Follow-up data were obtained for all 108 patients (100% of the sample). Based on the occurrence of the primary endpoint, two groups were formed: the group without cardiovascular events (“–MACE”) and the group that reached the endpoint (“+MACE”). Analysis of LV myocardial tissue characteristics assessed in the LGE phase revealed no significant differences between the study groups for almost all parameters, with the exception of the global LV SI elevation on T2-WI, which was significantly lower in the “+MACE” group. Quantitative analysis of SI heterogeneity across the entire LV using textural features revealed differences in first-order statistics, with higher values of these indices in the “+MACE” group. Patients who experienced a MACE during the follow-up period were characterized by a more asymmetric and complex signal texture, featuring abrupt variations in gray-level intensity, higher gray-level irregularity, shorter lengths of homogeneous areas and run lengths, and a predominance of small heterogeneous areas. Analysis of the intact myocardium in the LV also demonstrated higher heterogeneity and gray-level irregularity, with a high number of small heterogeneous regions. Conclusion . Heterogeneity parameters assessed by CMR reflect the changes occurring in the LV myocardium after MI, are associated with cardiac functional indices, and may be considered prognostic factors for an adverse clinical course. Given the limitations of this study, further research is needed to investigate the relationship between LV tissue characteristics on CMR, entropy, and adverse outcomes after acute myocardial injury.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".