Subendocardial quantification enhances coronary artery disease detection in 18F-flurpiridaz PET
Bibliographic record
Abstract
PURPOSE: F-flurpiridaz, is set to enter clinical use soon following its recent regulatory approval. We developed an approach for evaluating subendocardial analysis for stress total perfusion deficit (TPD) and ischemic TPD, assessed its performance for detection of coronary artery disease (CAD) and compared these measures to transmural analysis and expert physician assessments. METHODS: F-flurpiridaz phase III clinical trial (NCT01347710) were used. The subendocardial layer was automatically defined on the left ventricular contours and used for the derivation of polar maps. Areas under the receiver operating characteristic curve (AUC) for quantitative and visual measures were evaluated for detecting CAD, defined as ≥ 50% stenosis by invasive coronary angiography. RESULTS: In total, 753 cases were analyzed, with a median age of 63 (interquartile range 56,69) and 69% male. AUC for detecting ≥ 50% stenosis was higher for subendocardial than transmural analysis for stress (0.795 vs. 0.762, respectively; p = 0.013) and ischemic (0.795 vs. 0.767, respectively; p = 0.049) TPD. Subendocardial and transmural TPD achieved diagnostic performance greater than or comparable to that of the readers' assessments in the total population as well as across subgroups of interest. CONCLUSION: Subendocardial analysis of ischemic perfusion improves the detection of CAD compared to transmural quantitative analysis or expert visual reading. These measures can be derived automatically with minimal user interaction. Integrating TPD quantitative measures could standardize the diagnostic approach for this novel tracer.
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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 | 0.001 |
| 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".