Whole-Heart Histological and Electroanatomic Assessment of Postinfarction Cardiac Magnetic Resonance Imaging Scar and Conducting Channels
Bibliographic record
Abstract
BACKGROUND: Cardiac magnetic resonance imaging (CMR)–defined ventricular scar and anatomic conduction channels (CMR-CCs) offer promise in delineating ventricular tachycardia substrate. No studies have validated channels with coregistered histology, nor have they ascertained the histological characteristics of deceleration zones (DZs) within these channels. We aimed to validate CMR scar and CMR-CCs with whole-heart histology and electroanatomic mapping in a postinfarction model. METHODS: Five sheep underwent anteroseptal infarction. CMR (116±20 days post infarct) was postprocessed using ADAS-3D, varying pixel intensity thresholds (5545, 6040, 6535, and 7030). DZs were identified by electroanatomic mapping (129±12 days post infarct). Explanted hearts were sectioned and stained with Picrosirius red, and whole-heart histopathologic shells were generated. Scar topography as well as percentage fibrosis, adiposity, and remaining viable myocardium within 3 mm histological biopsies and within CMR-CCs were determined. RESULTS: Using the standard 6040 thresholding, CMR had 83.8% accuracy for identifying histological scar in the endocardium (κ, 0.666) and 61.4% in the epicardium (κ, 0.276). Thirty-seven CMR-CCs were identified by varying thresholding; 23 (62%) were unique. DZs colocalized to 19 of 23 (83%) CMR-CCs. Twenty (87%) CMR-CCs were histologically confirmed. Within-channel histological fibrosis did not differ by the presence of DZs ( P =0.242). Within-channel histological adiposity was significantly higher at sites with versus without DZs (24.1% versus 8.3%; P <0.001). CONCLUSIONS: Postprocessed CMR-derived scars and channels were validated by histology and electroanatomic mapping. Regions of CMR-CCs at sites of DZs had higher adiposity but similar fibrosis than regions without DZs, suggesting that lipomatous metaplasia may contribute to arrhythmogenicity of postinfarction scar.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".