Ultra-high resolution photon-counting detector coronary CT minimizes overestimation bias compared to invasive reference
Bibliographic record
Abstract
BACKGROUND: Photon-counting detector (PCD) coronary CT angiography (CCTA) at ultra-high-resolution (UHR) is a promising tool for the detailed evaluation of the coronary arteries. However, correlation with invasive quantitative coronary angiography (QCA) has not been thoroughly investigated. We here evaluated the efficacy of UHR-CCTA against invasive QCA in patients suspected of coronary artery disease (CAD). METHODS: Retrospectively, patients suggestive of CAD were included if they had undergone UHR-CCTA on a PCD-CT system showing coronary stenosis which clinically indicated subsequent invasive coronary angiography and no prior coronary interventions. CCTA datasets were reconstructed in 0.6 mm, 0.4 mm, and UHR 0.2 mm slice thicknesses. The extent of stenosis was compared between QCA and CCTA using univariate analysis of variance with post-hoc testing and Bland-Altman plots. Diagnostic performance was assessed based on the detection of relevant coronary stenosis (≥50 %) as confirmed by QCA. RESULTS: Forty-nine patients (71 ± 9 years; 37 % male) were included. Stenosis evaluation for 103 segments revealed decreasing mean stenosis diameter with improving spatial resolution (61.4 % for 0.6 mm, 55.3 % for 0.4 mm, 50.9 % for UHR 0.2 mm; p ≤ 0.001). Bias between CCTA and QCA decreased with increasing resolution (13.2 %, limits of agreement [LoA] 30 vs. 9.4 %, 28.1 vs. 5.2 %, 23). UHR-CCTA reconstructions showed superior diagnostic accuracy and positive predictive value (PPV) for detecting relevant CAD compared to lower resolutions (61.2 vs. 61.2 % vs. 71.4 and 53.7 % vs. 53.9 vs. 61.8 %, respectively). CONCLUSIONS: UHR-CCTA with photon-counting detector CT demonstrated a decrease in overestimation bias and an increase in PPV.
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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".