Clinical evaluation of three‐dimensional late enhancement MRI
Bibliographic record
Abstract
PURPOSE: To assess the diagnostic value of three-dimensional late enhancement (3D-LGE) for the detection of myocardial necrosis in a routine clinical setting. 3D-LGE has been proposed as a novel magnetic resonance (MR) technique for the accurate detection of myocardial scar in both the ventricles and atria. Its performance in clinical practice has been poorly examined. MATERIALS AND METHODS: Fifty-seven patients referred for cardiac MR examination including scar imaging were prospectively enrolled. Gadolinium enhanced single breathhold 3D T1-weighted gradient-echo inversion recovery sequence and a conventional 2D-LGE sequence were performed using a 1.5 Tesla clinical MR imaging system. The presence, pattern and transmurality of LGE, diagnostic accuracy and level of diagnostic confidence as well as image quality (median quality, mean LGE signal intensity, sharpness, virtual scan time) were graded on a 4-point scale. RESULTS: Interpretable images were obtained in 52/57 2D-LGE and in 47/57 3D high-resolution exams. LGE was detected in 10 patients with ischemic pattern, 9 with nonischemic pattern, while it was absent in 28, resulting in a total of 47 complete datasets. The detection of global and segmental LGE as well as its transmural extent were similar for both techniques (P = 0.65, P = 0.305, and P = 0.15, respectively). Image quality (median quality, LGE/ myocardial and LGE/ blood pool sharpness) was similar for both techniques (P = 0.740, P = 0.34, and P = 1.00, respectively), but LGE signal intensity was higher with 2D (P = 0.020). CONCLUSION: 3D-LGE diagnostic and quality scores were comparable to 2D-LGE in a routine clinical setting. Further technical refinement is required for 3D LGE to offer a reliable alternative for high quality scar imaging. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;45:1675-1683.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".