Myocardial Blood Flow Quantification Using Stress Cardiac Magnetic Resonance Improves Detection of Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) using stress cardiovascular magnetic resonance (CMR) have been shown to identify epicardial coronary artery disease. However, comparative analysis between quantitative perfusion and conventional qualitative assessment (QA) remains limited. OBJECTIVES: The aim of this multicenter study was to test the hypothesis that quantitative stress MBF (sMBF) and MPR analysis can identify obstructive coronary artery disease (obCAD) with comparable performance as QA of stress CMR performed by experienced physicians in interpretation. METHODS: The analysis included 127 individuals (mean age 62 ± 16 years, 84 men [67%]) who underwent stress CMR. obCAD was defined as the presence of stenosis ≥50% in the left main coronary artery or ≥70% in a major vessel. Each patient, coronary territory, and myocardial segment was categorized as having either obCAD or no obCAD (noCAD). Global, per coronary territory, and segmental MBF and MPR values were calculated. QA was performed by 4 CMR experts. RESULTS: At the patient level, global sMBF and MPR were significantly lower in subjects with obCAD than in those with noCAD, with median values of sMBF of 1.5 mL/g/min (Q1-Q3: 1.2-1.8 mL/g/min) vs 2.4 mL/g/min (Q1-Q3: 2.1-2.7 mL/g/min) (P < 0.001) and median values of MPR of 1.3 (Q1-Q3: 1.0-1.6) vs 2.1 (Q1-Q3: 1.6-2.7) (P < 0.001). At the coronary artery level, sMBF and MPR were also significantly lower in vessels with obCAD compared with those with noCAD. Global sMBF and MPR had areas under the curve (AUCs) of 0.90 (95% CI: 0.84-0.96) and 0.86 (95% CI: 0.80-0.93). The AUCs for QA by 4 physicians ranged between 0.69 and 0.88. The AUC for global sMBF and MPR was significantly better than the average AUC for QA. CONCLUSIONS: This study demonstrates that sMBF and MPR using dual-sequence stress CMR can identify obCAD more accurately than qualitative analysis by experienced CMR readers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".