A Novel 3-Dimensional technique in measuring pericoronary epicardial adipose tissue radiodensity
Bibliographic record
Abstract
ABSTRACT Objectives This study aimed to investigate a novel semi-automated three-dimensional (3D) quantification of the pericoronary epicardial adipose tissue radiodensity (PCATrd). Methods Twenty-four subjects who previously underwent contrast-enhanced cardiac CT scans were retrospectively identified. The PCATrd was measured in ITK-SNAP imaging software using a Hounsfield unit threshold (−190,-3) to define epicardial adipose tissue (EAT). A spherical 3D brush tool was used on multiplanar reformatted images to segment the PCAT. We defined the PCATrd as EAT within the orthogonal distance from the coronary artery (CA) outer wall equal to the diameter of the corresponding CA segment. The segmentation followed the path of major CAs. Additionally, the PCAT of twenty-five calcified segments were segmented. Reliability of this novel segmentation protocol was assessed using Dice Similarity Coefficients (DSCs) and intraclass coefficient (ICC). Results The segmentation reproducibility for the PCAT was high, with intraobserver DSC 0.86±0.04 for the full length of major CAs and 0.85±0.07 for the calcified segments, and interobserver DSC 0.84±0.04 for the full length of major CAs and 0.83±0.05 for the calcified segments. The reproducibility of the PCATrd value assessed by ICC was also excellent, with intraobserver ICC 0.99 for the full length of major CAs and 0.99 for the calcified segments, and interobserver ICC 0.99 for the full length of major CAs and 0.99 for the calcified segments. Conclusions Our novel 3D PCATrd quantification technique is reliable and reproducible. The availability of the open source software and detailed image analysis pipeline will enable reliable replications and broad uptake of our technique. Key points We have produced a novel, semiautomated technique to comprehensively quantify pericoronary epicardial adipose tissue radiodensity (PCATrd) which is a novel imaging biomarker of coronary inflammation. Our method of PCAT segmentation has excellent reproducibility. We use open source software and provide detailed image analysis pipeline of quantifying PCATrd, which will allow easy replication and broad uptake of our technique.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".