Emerging cardiac CT biomarkers: a systematic review of diagnostic and prognostic utility in cardiovascular disease
Bibliographic record
Abstract
Background: Cardiac computed tomography (CT) has evolved from an anatomic test to a platform that quantifies functional, inflammatory, and tissue-characterization biomarkers. We synthesized evidence on the diagnostic and prognostic value of CT-based biomarkers. Methods: Systematic review of 29 human studies (2015–2025) appraising low-attenuation plaque (LAP), perivascular fat attenuation index (FAI/PCAT), total/non-calcified plaque burden, epicardial adipose tissue, CT-derived fractional flow reserve (FFR-CT), and CT myocardial perfusion. Study quality was assessed with risk of bias (RoB) 2.0, Newcastle-Ottawa Scale (NOS), and AMSTAR 2. Results: CT biomarkers extended risk assessment beyond stenosis severity. LAP burden > 4% predicted myocardial infarction (MI) [hazard ratio (HR) 4.65; 95% CI 2.06–10.5] and per-doubling LAP predicted MI (HR 1.60; 95% CI 1.10–2.34). Perivascular FAI/PCAT showed independent prognostic value: high FAI was associated with ~2-fold higher cardiac mortality (derivation HR 2.15, validation HR 2.06), and RCA PCAT ≥ −70.5 Hounsfield unit (HU) predicted MI (HR 2.45) with additive risk when combined with high-risk plaque (HRP) features (reported up to ~6-fold vs. reference). FFR-CT achieved up to 81% diagnostic accuracy (sensitivity ~86%, specificity ~79%) vs. invasive FFR, improving specificity over CTA alone. Emerging metrics (e.g., total plaque volume, CT perfusion) demonstrated incremental discrimination in selected cohorts, though standardization remains variable. Discussion: CT-based biomarkers provide measurable diagnostic and prognostic information on coronary anatomy, function, inflammation, and tissue health. Priorities include standardized acquisition/analysis, multicenter validation, and integration into decision pathways to optimize individualized risk stratification and therapy.
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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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".