Sex-specific prognostic value of automated epicardial adipose tissue quantification on serial lung cancer screening chest computed tomography
Bibliographic record
Abstract
AIMS: Epicardial adipose tissue (EAT) is a metabolically active fat depot associated with coronary atherosclerosis and cardiovascular (CV) risk. While EAT is a known prognostic marker in lung cancer screening, its sex-specific prognostic value remains unclear. This study investigated sex differences in the prognostic utility of serial EAT measurements on low-dose chest computed tomography (CT). METHODS AND RESULTS: We analysed baseline and 2-year changes in EAT volume and density using a validated automated deep-learning algorithm in 24 008 heavy-smoking participants from the National Lung Screening Trial (NLST). Sex-stratified multivariable Cox models, adjusted for CV risk factors, body mass index (BMI), and coronary artery calcium (CAC), assessed associations between EAT and all-cause and CV mortality [median follow-up 12.3 years (IQR: 11.9-12.8), 4668 (19.4%) all-cause deaths, 1083 (4.5%) CV deaths]. Women (n = 9841; 41%) were younger, with fewer CV risk factors, lower BMI, fewer pack-years, and lower CAC than men (all P < 0.001). Baseline EAT was associated with similar all-cause and CV mortality risk in both sexes [max. aHR women: 1.70; 95% confidence interval (CI): 1.13-2.55; men: 1.83; 95% CI: 1.40-2.40, P-interaction = 0.986]. However, 2-year EAT changes predicted CV death only in women (aHR: 1.82; 95% CI: 1.37-2.49; P < 0.001), and showed a stronger association with all-cause mortality in women (aHR: 1.52; 95% CI: 1.31-1.77) than in men (aHR: 1.26; 95% CI: 1.13-1.40; P-interaction = 0.041). CONCLUSION: In this large lung cancer screening cohort, serial EAT changes independently predicted CV mortality in women and were more strongly associated with all-cause mortality in women than in men. These findings support routine EAT quantification on chest CT for improved sex-specific cardiovascular risk stratification.
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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 | 0.005 |
| 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 | 0.001 | 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".