Quantitative cardiac magnetic resonance standardized signal intensity comparison in dilated cardiomyopathy vs. cardiac sarcoidosis
Bibliographic record
Abstract
BACKGROUND: Dilated cardiomyopathy (DCM) and cardiac sarcoidosis (CS) manifest unique late gadolinium enhancement (LGE) patterns on cardiac magnetic resonance (CMR), indicative of different myocardial scar distributions. However, the overlap in these patterns due to their lack of specificity complicates differentiation. This study introduces a novel quantitative method employing z-score analysis of LGE-CMR intensity to objectively compare the spatial distribution of LGE intensity between DCM and CS. METHODS: This retrospective study included 22 NICM patients (13 DCM, 9 CS) who underwent CMR before electrophysiology study from November 2018 to May 2023. LGE images were delineated into sub-endocardial, mid-myocardial, and sub-epicardial layers across anterior, lateral, inferior, and septal walls using the AHA 17-segment model. CMR signal intensities were standardized to z-scores (z = (x - μ)/σ), with x as the signal intensity for a specific myocardial segment, and μ and σ as the mean and SD for all LV myocardial segments, to map regional intensity variations. RESULTS: Compared to DCM, CS patients exhibited significantly higher CMR signal intensity z-scores in the septum (β = 0.32, p = 0.009), particularly in the endocardial third of the right ventricular (RV) side (β = 0.56, p = 0.001). A z-score greater than 0.40 in this area was associated with a CS diagnosis, with an area under the ROC curve of 0.692 in fivefold cross-validation. CONCLUSION: Patients with CS exhibit higher affinity for contrast in the septum, particularly on the RV endocardium. Standardized analysis of CMR signal intensities provides a novel, quantitative method for distinguishing CS from DCM, with the former exhibiting higher CMR signal intensity z-scores in the septum.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".