Quantification of epicardial adipose tissue in coronary calcium score and CT coronary angiography image data sets: comparison of attenuation values, thickness and volumes
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to compare epicardial adipose tissue (EAT) characteristics assessed with coronary calcium score (CS) and CT coronary angiography (CTCA) image data sets. METHODS: In 76 patients (mean age 59 ± 13 years) who underwent CS and CTCA owing to suspected coronary artery disease (CAD), EAT was quantified in terms of density (Hounsfield units), thickness and volume. The EAT volume was extracted with a semi-automatic software. RESULTS: A moderate correlation was found between EAT density in CS and CTCA image data sets (-100 ± 19 HU vs -70 ± 24 HU; p < 0.05, r = 0.55). The distribution of EAT was not symmetrical with a maximal thickness at the right atrioventricular groove (14.2 ± 5.3 mm in CS, 15.7 ± 5 mm in CTCA; p > 0.05, r = 0.76). The EAT volume resulted as 122 ± 50 cm(3) in CS and 86 ± 40 cm(3) in CTCA (Δ = 30%, p < 0.05, r = 0.92). After adjustment for post-contrast EAT attenuation difference (Δ = 30 HU), the volume was 101 ± 47 cm(3) (Δ = 17%, p < 0.05, r = 0.92). Based on EAT volume median values, no differences were found between groups with smaller and larger volumes in terms of Agatston score and CAD severity. CONCLUSION: CS and CTCA image data sets may be equally employed for EAT assessment; however, an underestimation of volume is found with the latter acquisition even after post-contrast attenuation adjustment. ADVANCES IN KNOWLEDGE: EAT may be measured by processing either the CS or CTCA image data sets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".